fix precommit

.buildkite/nightly-benchmarks/nightly-annotation.md

@@ -16,7 +16,7 @@ Please download the visualization scripts in the post
   - Download `nightly-benchmarks.zip`.
   - In the same folder, run the following code:
 
-  ```bash
+  ```console
   export HF_TOKEN=<your HF token>
   apt update
   apt install -y git

.buildkite/release-pipeline.yaml

@@ -102,7 +102,6 @@ steps:
     commands:
       - "aws ecr-public get-login-password --region us-east-1 | docker login --username AWS --password-stdin public.ecr.aws/q9t5s3a7"
       - "DOCKER_BUILDKIT=1 docker build --build-arg max_jobs=16 --build-arg GIT_REPO_CHECK=1 --tag public.ecr.aws/q9t5s3a7/vllm-cpu-release-repo:$(buildkite-agent meta-data get release-version) --tag public.ecr.aws/q9t5s3a7/vllm-cpu-release-repo:latest --progress plain --target vllm-openai -f docker/Dockerfile.cpu ."
-      - "docker push public.ecr.aws/q9t5s3a7/vllm-cpu-release-repo:latest"
       - "docker push public.ecr.aws/q9t5s3a7/vllm-cpu-release-repo:$(buildkite-agent meta-data get release-version)"
     env:
       DOCKER_BUILDKIT: "1"
@@ -118,7 +117,6 @@ steps:
     commands:
       - "aws ecr-public get-login-password --region us-east-1 | docker login --username AWS --password-stdin public.ecr.aws/q9t5s3a7"
       - "DOCKER_BUILDKIT=1 docker build --build-arg max_jobs=16 --build-arg GIT_REPO_CHECK=1 --tag public.ecr.aws/q9t5s3a7/vllm-neuron-release-repo:$(buildkite-agent meta-data get release-version) --tag public.ecr.aws/q9t5s3a7/vllm-neuron-release-repo:latest --progress plain -f docker/Dockerfile.neuron ."
-      - "docker push public.ecr.aws/q9t5s3a7/vllm-neuron-release-repo:latest"
       - "docker push public.ecr.aws/q9t5s3a7/vllm-neuron-release-repo:$(buildkite-agent meta-data get release-version)"
     env:
       DOCKER_BUILDKIT: "1"

.buildkite/scripts/hardware_ci/run-neuron-test.sh

@@ -54,11 +54,10 @@ docker run --rm -it --device=/dev/neuron0 --network bridge \
        --name "${container_name}" \
        ${image_name} \
        /bin/bash -c "
-            set -e; # Exit on first error
             python3 /workspace/vllm/examples/offline_inference/neuron.py;
             python3 -m pytest /workspace/vllm/tests/neuron/1_core/ -v --capture=tee-sys;
             for f in /workspace/vllm/tests/neuron/2_core/*.py; do
-                echo \"Running test file: \$f\";
+                echo 'Running test file: '$f;
                 python3 -m pytest \$f -v --capture=tee-sys;
             done
        "

.buildkite/scripts/tpu/config_v6e_1.env

@@ -4,8 +4,8 @@ CONTAINER_NAME=vllm-tpu
 
 # vllm config
 MODEL=meta-llama/Llama-3.1-8B-Instruct
-MAX_NUM_SEQS=256
-MAX_NUM_BATCHED_TOKENS=1024
+MAX_NUM_SEQS=512
+MAX_NUM_BATCHED_TOKENS=512
 TENSOR_PARALLEL_SIZE=1
 MAX_MODEL_LEN=2048
 DOWNLOAD_DIR=/mnt/disks/persist

.buildkite/test-pipeline.yaml

@@ -89,7 +89,7 @@ steps:
   - VLLM_TEST_ENABLE_ARTIFICIAL_PREEMPT=1 pytest -v -s basic_correctness/test_preemption.py
 
 - label: Chunked Prefill Test
-  mirror_hardwares: [amdexperimental, amdproduction]
+  mirror_hardwares: [amdexperimental]
   source_file_dependencies:
   - vllm/
   - tests/basic_correctness/test_chunked_prefill
@@ -271,15 +271,6 @@ steps:
   commands:
     - pytest -v -s prefix_caching
 
-
-- label: Platform Tests (CUDA)
-  mirror_hardwares: [amdexperimental]
-  source_file_dependencies:
-  - vllm/
-  - tests/cuda
-  commands:
-    - pytest -v -s cuda/test_cuda_context.py
-
 - label: Samplers Test # 36min
   mirror_hardwares: [amdexperimental]
   source_file_dependencies:

.github/mergify.yml

@@ -45,7 +45,6 @@ pull_request_rules:
       - files~=^vllm/entrypoints/openai/tool_parsers/llama.*\.py
       - files~=^vllm/model_executor/models/.*llama.*\.py
       - files~=^vllm/transformers_utils/configs/.*llama.*\.py
-      - title~=(?i)llama
   actions:
     label:
       add:
@@ -66,19 +65,6 @@ pull_request_rules:
       add:
         - multi-modality
 
-- name: label-performance
-  description: Automatically apply performance label
-  conditions:
-    - or:
-      - files~=^benchmarks/
-      - files~=^vllm/benchmarks/
-      - files~=^tests/benchmarks/
-      - files~=^\.buildkite/nightly-benchmarks/
-  actions:
-    label:
-      add:
-        - performance
-
 - name: label-qwen
   description: Automatically apply qwen label
   conditions:
@@ -88,6 +74,7 @@ pull_request_rules:
       - files~=^vllm/model_executor/models/.*qwen.*\.py
       - files~=^vllm/reasoning/.*qwen.*\.py
       - title~=(?i)Qwen
+      - body~=(?i)Qwen
   actions:
     label:
       add:

.pre-commit-config.yaml

@@ -115,11 +115,6 @@ repos:
     entry: python tools/check_spdx_header.py
     language: python
     types: [python]
-  - id: check-root-lazy-imports
-    name: Check root lazy imports
-    entry: python tools/check_init_lazy_imports.py
-    language: python
-    types: [python]
   - id: check-filenames
     name: Check for spaces in all filenames
     entry: bash

README.md

@@ -154,13 +154,11 @@ If you use vLLM for your research, please cite our [paper](https://arxiv.org/abs
 
 ## Contact Us
 
-<!-- --8<-- [start:contact-us] -->
 - For technical questions and feature requests, please use GitHub [Issues](https://github.com/vllm-project/vllm/issues) or [Discussions](https://github.com/vllm-project/vllm/discussions)
 - For discussing with fellow users, please use the [vLLM Forum](https://discuss.vllm.ai)
 - For coordinating contributions and development, please use [Slack](https://slack.vllm.ai)
 - For security disclosures, please use GitHub's [Security Advisories](https://github.com/vllm-project/vllm/security/advisories) feature
 - For collaborations and partnerships, please contact us at [vllm-questions@lists.berkeley.edu](mailto:vllm-questions@lists.berkeley.edu)
-<!-- --8<-- [end:contact-us] -->
 
 ## Media Kit
 

benchmarks/README.md

@@ -387,178 +387,3 @@ python3 vllm/benchmarks/benchmark_throughput.py \
   --enable-lora \
   --lora-path yard1/llama-2-7b-sql-lora-test
   ```
-
----
-## Example - Structured Output Benchmark
-
-Benchmark the performance of structured output generation (JSON, grammar, regex).
-
-### Server Setup
-
-```bash
-vllm serve NousResearch/Hermes-3-Llama-3.1-8B --disable-log-requests
-```
-
-### JSON Schema Benchmark
-
-```bash
-python3 benchmarks/benchmark_serving_structured_output.py \
-  --backend vllm \
-  --model NousResearch/Hermes-3-Llama-3.1-8B \
-  --dataset json \
-  --structured-output-ratio 1.0 \
-  --request-rate 10 \
-  --num-prompts 1000
-```
-
-### Grammar-based Generation Benchmark
-
-```bash
-python3 benchmarks/benchmark_serving_structured_output.py \
-  --backend vllm \
-  --model NousResearch/Hermes-3-Llama-3.1-8B \
-  --dataset grammar \
-  --structure-type grammar \
-  --request-rate 10 \
-  --num-prompts 1000
-```
-
-### Regex-based Generation Benchmark
-
-```bash
-python3 benchmarks/benchmark_serving_structured_output.py \
-  --backend vllm \
-  --model NousResearch/Hermes-3-Llama-3.1-8B \
-  --dataset regex \
-  --request-rate 10 \
-  --num-prompts 1000
-```
-
-### Choice-based Generation Benchmark
-
-```bash
-python3 benchmarks/benchmark_serving_structured_output.py \
-  --backend vllm \
-  --model NousResearch/Hermes-3-Llama-3.1-8B \
-  --dataset choice \
-  --request-rate 10 \
-  --num-prompts 1000
-```
-
-### XGrammar Benchmark Dataset
-
-```bash
-python3 benchmarks/benchmark_serving_structured_output.py \
-  --backend vllm \
-  --model NousResearch/Hermes-3-Llama-3.1-8B \
-  --dataset xgrammar_bench \
-  --request-rate 10 \
-  --num-prompts 1000
-```
-
----
-## Example - Long Document QA Throughput Benchmark
-
-Benchmark the performance of long document question-answering with prefix caching.
-
-### Basic Long Document QA Test
-
-```bash
-python3 benchmarks/benchmark_long_document_qa_throughput.py \
-  --model meta-llama/Llama-2-7b-chat-hf \
-  --enable-prefix-caching \
-  --num-documents 16 \
-  --document-length 2000 \
-  --output-len 50 \
-  --repeat-count 5
-```
-
-### Different Repeat Modes
-
-```bash
-# Random mode (default) - shuffle prompts randomly
-python3 benchmarks/benchmark_long_document_qa_throughput.py \
-  --model meta-llama/Llama-2-7b-chat-hf \
-  --enable-prefix-caching \
-  --num-documents 8 \
-  --document-length 3000 \
-  --repeat-count 3 \
-  --repeat-mode random
-
-# Tile mode - repeat entire prompt list in sequence
-python3 benchmarks/benchmark_long_document_qa_throughput.py \
-  --model meta-llama/Llama-2-7b-chat-hf \
-  --enable-prefix-caching \
-  --num-documents 8 \
-  --document-length 3000 \
-  --repeat-count 3 \
-  --repeat-mode tile
-
-# Interleave mode - repeat each prompt consecutively
-python3 benchmarks/benchmark_long_document_qa_throughput.py \
-  --model meta-llama/Llama-2-7b-chat-hf \
-  --enable-prefix-caching \
-  --num-documents 8 \
-  --document-length 3000 \
-  --repeat-count 3 \
-  --repeat-mode interleave
-```
-
----
-## Example - Prefix Caching Benchmark
-
-Benchmark the efficiency of automatic prefix caching.
-
-### Fixed Prompt with Prefix Caching
-
-```bash
-python3 benchmarks/benchmark_prefix_caching.py \
-  --model meta-llama/Llama-2-7b-chat-hf \
-  --enable-prefix-caching \
-  --num-prompts 1 \
-  --repeat-count 100 \
-  --input-length-range 128:256
-```
-
-### ShareGPT Dataset with Prefix Caching
-
-```bash
-# download dataset
-# wget https://huggingface.co/datasets/anon8231489123/ShareGPT_Vicuna_unfiltered/resolve/main/ShareGPT_V3_unfiltered_cleaned_split.json
-
-python3 benchmarks/benchmark_prefix_caching.py \
-  --model meta-llama/Llama-2-7b-chat-hf \
-  --dataset-path /path/ShareGPT_V3_unfiltered_cleaned_split.json \
-  --enable-prefix-caching \
-  --num-prompts 20 \
-  --repeat-count 5 \
-  --input-length-range 128:256
-```
-
----
-## Example - Request Prioritization Benchmark
-
-Benchmark the performance of request prioritization in vLLM.
-
-### Basic Prioritization Test
-
-```bash
-python3 benchmarks/benchmark_prioritization.py \
-  --model meta-llama/Llama-2-7b-chat-hf \
-  --input-len 128 \
-  --output-len 64 \
-  --num-prompts 100 \
-  --scheduling-policy priority
-```
-
-### Multiple Sequences per Prompt
-
-```bash
-python3 benchmarks/benchmark_prioritization.py \
-  --model meta-llama/Llama-2-7b-chat-hf \
-  --input-len 128 \
-  --output-len 64 \
-  --num-prompts 100 \
-  --scheduling-policy priority \
-  --n 2
-```

benchmarks/auto_tune.sh

@@ -10,7 +10,6 @@
 # 3. Set variables (ALL REQUIRED)
 #   BASE: your directory for vllm repo
 #   MODEL: the model served by vllm
-#   SYSTEM: the hardware, choice TPU or GPU, for other systems, "get best profile" might not support.
 #   TP: ways of tensor parallelism
 #   DOWNLOAD_DIR: directory to download and load model weights.
 #   INPUT_LEN: request input len
@@ -35,7 +34,6 @@
 TAG=$(date +"%Y_%m_%d_%H_%M")
 BASE=""
 MODEL="meta-llama/Llama-3.1-8B-Instruct"
-SYSTEM="TPU"
 TP=1
 DOWNLOAD_DIR=""
 INPUT_LEN=4000
@@ -47,15 +45,12 @@ NUM_BATCHED_TOKENS_LIST="512 1024 2048 4096"
 
 LOG_FOLDER="$BASE/auto-benchmark/$TAG"
 RESULT="$LOG_FOLDER/result.txt"
-PROFILE_PATH="$LOG_FOLDER/profile"
 
 echo "result file: $RESULT"
 echo "model: $MODEL"
 
 rm -rf $LOG_FOLDER
-rm -rf $PROFILE_PATH
 mkdir -p $LOG_FOLDER
-mkdir -p $PROFILE_PATH
 
 cd "$BASE/vllm"
 
@@ -75,11 +70,10 @@ start_server() {
     local max_num_seqs=$2
     local max_num_batched_tokens=$3
     local vllm_log=$4
-    local profile_dir=$5
     
     pkill -f vllm
 
-    VLLM_USE_V1=1 VLLM_SERVER_DEV_MODE=1 VLLM_TORCH_PROFILER_DIR=$profile_dir vllm serve $MODEL \
+    VLLM_USE_V1=1 VLLM_SERVER_DEV_MODE=1 vllm serve $MODEL \
         --disable-log-requests \
         --port 8004 \
         --gpu-memory-utilization $gpu_memory_utilization \
@@ -111,37 +105,19 @@ start_server() {
     fi
 }
 
-update_best_profile() {
-    local profile_dir=$1
-    local profile_index=$2
-    sorted_paths=($(find "$profile_dir" -maxdepth 1 -not -path "$profile_dir" | sort))
-    selected_profile_file=
-    if [[ "$SYSTEM" == "TPU" ]]; then
-        selected_profile_file="${sorted_paths[$profile_index]}/*.xplane.pb"
-    fi 
-    if [[ "$SYSTEM" == "GPU" ]]; then
-        selected_profile_file="${sorted_paths[$profile_index]}"
-    fi 
-    rm -f $PROFILE_PATH/*
-    cp $selected_profile_file $PROFILE_PATH
-}
-
 run_benchmark() {
     local max_num_seqs=$1
     local max_num_batched_tokens=$2
     local gpu_memory_utilization=$3
     echo "max_num_seq: $max_num_seqs, max_num_batched_tokens: $max_num_batched_tokens"
     local vllm_log="$LOG_FOLDER/vllm_log_${max_num_seqs}_${max_num_batched_tokens}.txt"
-    local profile_dir="$LOG_FOLDER/profile_${max_num_seqs}_${max_num_batched_tokens}"
     echo "vllm_log: $vllm_log"
     echo
     rm -f $vllm_log
-    mkdir -p $profile_dir
     pkill -f vllm
-    local profile_index=0
 
     echo "starting server..."
-    start_server $gpu_memory_utilization $max_num_seqs $max_num_batched_tokens $vllm_log $profile_dir
+    start_server $gpu_memory_utilization $max_num_seqs $max_num_batched_tokens $vllm_log
     result=$?
     if [[ "$result" -eq 1 ]]; then
         echo "server failed to start. gpu_memory_utilization:$gpu_memory_utilization, max_num_seqs:$max_num_seqs, max_num_batched_tokens: $max_num_batched_tokens"
@@ -168,8 +144,7 @@ run_benchmark() {
         --goodput e2el:$MAX_LATENCY_ALLOWED_MS \
         --num-prompts 1000 \
         --random-prefix-len $prefix_len \
-        --port 8004 \
-        --profile &> "$bm_log"
+        --port 8004 &> "$bm_log"
     throughput=$(grep "Request throughput (req/s):" "$bm_log" | sed 's/[^0-9.]//g')
     e2el=$(grep "P99 E2EL (ms):" "$bm_log" | awk '{print $NF}')
     goodput=$(grep "Request goodput (req/s):" "$bm_log" | sed 's/[^0-9.]//g')
@@ -183,7 +158,6 @@ run_benchmark() {
     # start from request-rate as int(throughput) + 1
         request_rate=$((${throughput%.*} + 1))
         while ((request_rate > 0)); do
-            profile_index=$((profile_index+1))
             # clear prefix cache
             curl -X POST http://0.0.0.0:8004/reset_prefix_cache
             sleep 5
@@ -221,12 +195,6 @@ run_benchmark() {
             best_max_num_seqs=$max_num_seqs
             best_num_batched_tokens=$max_num_batched_tokens
             best_goodput=$goodput
-            if [[ "$SYSTEM" == "TPU" ]]; then
-                update_best_profile "$profile_dir/plugins/profile" $profile_index
-            fi
-            if [[ "$SYSTEM" == "GPU" ]]; then
-                update_best_profile "$profile_dir" $profile_index
-            fi
         fi
     else
         echo "max_num_seqs: $max_num_seqs, max_num_batched_tokens: $max_num_batched_tokens does not meet latency requirement ${MAX_LATENCY_ALLOWED_MS}"
@@ -271,6 +239,6 @@ for num_seqs in "${num_seqs_list[@]}"; do
     done
 done
 echo "finish permutations"
-echo "best_max_num_seqs: $best_max_num_seqs, best_num_batched_tokens: $best_num_batched_tokens, best_throughput: $best_throughput, profile saved in: $PROFILE_PATH"
-echo "best_max_num_seqs: $best_max_num_seqs, best_num_batched_tokens: $best_num_batched_tokens, best_throughput: $best_throughput, profile saved in: $PROFILE_PATH" >> "$RESULT"
+echo "best_max_num_seqs: $best_max_num_seqs, best_num_batched_tokens: $best_num_batched_tokens, best_throughput: $best_throughput"
+echo "best_max_num_seqs: $best_max_num_seqs, best_num_batched_tokens: $best_num_batched_tokens, best_throughput: $best_throughput" >> "$RESULT"
 

benchmarks/backend_request_func.py

@@ -404,14 +404,8 @@ async def async_request_openai_chat_completions(
                         chunk_bytes = chunk_bytes.strip()
                         if not chunk_bytes:
                             continue
-                        chunk_bytes = chunk_bytes.decode("utf-8")
-                        # NOTE: SSE comments (often used as pings) start with a colon.
-                        # These are not JSON data payload and should be skipped.
-                        if chunk_bytes.startswith(":"):
-                            continue
-
-                        chunk = chunk_bytes.removeprefix("data: ")
 
+                        chunk = chunk_bytes.decode("utf-8").removeprefix("data: ")
                         if chunk != "[DONE]":
                             timestamp = time.perf_counter()
                             data = json.loads(chunk)

benchmarks/benchmark_dataset.py

@@ -353,7 +353,7 @@ class RandomDataset(BenchmarkDataset):
                 : input_lens[i]
             ]
             prompt = tokenizer.decode(re_encoded_sequence)
-            total_input_len = len(re_encoded_sequence)
+            total_input_len = prefix_len + int(input_lens[i])
             requests.append(
                 SampleRequest(
                     prompt=prompt,

benchmarks/benchmark_throughput.py

@@ -97,7 +97,7 @@ def run_vllm(
         assert lora_requests is None, "BeamSearch API does not support LoRA"
         prompts = [request.prompt for request in requests]
         # output_len should be the same for all requests.
-        output_len = requests[0].expected_output_len
+        output_len = requests[0][2]
         for request in requests:
             assert request.expected_output_len == output_len
         start = time.perf_counter()

benchmarks/kernels/benchmark_marlin.py

@@ -22,16 +22,8 @@ from vllm.model_executor.layers.quantization.utils.marlin_utils import (
     MARLIN_SUPPORTED_GROUP_SIZES,
     query_marlin_supported_quant_types,
 )
-from vllm.model_executor.layers.quantization.utils.marlin_utils_fp4 import (
-    FP4_MARLIN_SUPPORTED_GROUP_SIZES,
-    rand_marlin_weight_fp4_like,
-)
-from vllm.model_executor.layers.quantization.utils.marlin_utils_fp8 import (
-    marlin_quant_fp8_torch,
-)
 from vllm.model_executor.layers.quantization.utils.marlin_utils_test import (
     MarlinWorkspace,
-    awq_marlin_quantize,
     marlin_quantize,
 )
 from vllm.model_executor.layers.quantization.utils.marlin_utils_test_24 import (
@@ -43,7 +35,7 @@ from vllm.model_executor.layers.quantization.utils.quant_utils import (
     quantize_weights,
     sort_weights,
 )
-from vllm.scalar_type import ScalarType, scalar_types
+from vllm.scalar_type import ScalarType
 from vllm.utils import FlexibleArgumentParser
 
 DEFAULT_MODELS = ["meta-llama/Llama-2-7b-hf/TP1"]
@@ -65,79 +57,36 @@ def bench_run(
     size_n: int,
 ):
     label = "Quant Matmul"
+
     sub_label = "{}, act={} k_full={}, q={}, g={}, MKN=({}x{}x{})".format(
         model, act_order, is_k_full, str(quant_type), group_size, size_m, size_k, size_n
     )
+
     print(f"Testing: {sub_label}")
 
     a = torch.randn(size_m, size_k).to(torch.half).cuda()
     b = torch.rand(size_k, size_n).to(torch.half).cuda()
-    has_zp = quant_type in [scalar_types.uint4, scalar_types.uint8]
-    if act_order and (group_size == -1 or group_size == size_k or has_zp):
-        return
-    if size_k % group_size != 0:
-        return
 
-    marlin_24_supported = (
-        quant_type in GPTQ_MARLIN_24_SUPPORTED_QUANT_TYPES
-        and group_size in GPTQ_MARLIN_24_SUPPORTED_GROUP_SIZES
-    )
-    repack_supported = (
-        quant_type in GPTQ_MARLIN_24_SUPPORTED_QUANT_TYPES
-        and group_size in MARLIN_SUPPORTED_GROUP_SIZES
-    )
-    allspark_supported = (
-        quant_type in ALLSPARK_SUPPORTED_QUANT_TYPES
-        and group_size == -1
-        and not act_order
-        and is_k_full
-    )
+    a_tmp = torch.zeros(size_m, size_k).to(torch.half).cuda()
 
-    def gen_marlin_params():
     # Marlin quant
-        marlin_g_idx = marlin_sort_indices = marlin_zp = marlin_s2 = None
-        if quant_type == scalar_types.float4_e2m1f:
-            if group_size != 16 or act_order:
-                return
-            marlin_w_ref, marlin_q_w, marlin_s, marlin_s2 = rand_marlin_weight_fp4_like(
-                b.T, group_size
-            )
-        elif quant_type == scalar_types.float8_e4m3fn:
-            if group_size not in [-1, 128] or act_order:
-                return
-            marlin_w_ref, marlin_q_w, marlin_s = marlin_quant_fp8_torch(b.T, group_size)
-        elif group_size == 16:
-            return
-        elif has_zp:
-            marlin_w_ref, marlin_q_w, marlin_s, marlin_zp = awq_marlin_quantize(
-                b, quant_type, group_size
-            )
-        else:
-            marlin_w_ref, marlin_q_w, marlin_s, marlin_g_idx, marlin_sort_indices, _ = (
-                marlin_quantize(b, quant_type, group_size, act_order)
-            )
-        return (
+    (
         marlin_w_ref,
         marlin_q_w,
         marlin_s,
-            marlin_s2,
-            marlin_zp,
         marlin_g_idx,
         marlin_sort_indices,
-        )
+        marlin_rand_perm,
+    ) = marlin_quantize(b, quant_type, group_size, act_order)
 
-    def gen_marlin_24_params():
-        marlin_24_w_ref = marlin_24_q_w_comp = marlin_24_meta = marlin_24_s = None
-        if marlin_24_supported:
+    # Marlin_24 quant
     (marlin_24_w_ref, marlin_24_q_w_comp, marlin_24_meta, marlin_24_s) = (
         marlin_24_quantize(b, quant_type, group_size)
     )
-        return (marlin_24_w_ref, marlin_24_q_w_comp, marlin_24_meta, marlin_24_s)
 
-    def gen_repack_params():
-        q_w_gptq = None
-        repack_sort_indices = None
-        if repack_supported:
+    marlin_zp = torch.empty(0, dtype=torch.int, device=b.device)
+
+    # GPTQ quant
     (w_ref, q_w, s, g_idx, rand_perm) = gptq_quantize_weights(
         b, quant_type, group_size, act_order
     )
@@ -148,21 +97,33 @@ def bench_run(
     repack_sort_indices = torch.empty(0, dtype=torch.int, device=b.device)
     if act_order:
         (q_w, g_idx, repack_sort_indices) = sort_weights(q_w, g_idx)
-        return q_w_gptq, repack_sort_indices
 
-    def gen_allspark_params():
-        qw_reorder = s_reorder = zp_reorder = sm_count = sm_version = (
-            CUBLAS_M_THRESHOLD
-        ) = None
-        nonlocal allspark_supported
-        if allspark_supported:
+    # Prepare
+    marlin_workspace = MarlinWorkspace(
+        size_n, GPTQ_MARLIN_MIN_THREAD_N, GPTQ_MARLIN_MAX_PARALLEL
+    )
+
+    marlin_24_workspace = MarlinWorkspace(
+        size_n, GPTQ_MARLIN_24_MIN_THREAD_N, GPTQ_MARLIN_24_MAX_PARALLEL
+    )
+    marlin_zp = torch.zeros_like(marlin_s, dtype=torch.int)
+
+    # AllSpark W8A16 quant
+    as_supported_case = (
+        quant_type in ALLSPARK_SUPPORTED_QUANT_TYPES
+        and group_size == -1
+        and not act_order
+        and is_k_full
+    )
+    if as_supported_case:
         properties = torch.cuda.get_device_properties(b.device.index)
         sm_count = properties.multi_processor_count
         sm_version = properties.major * 10 + properties.minor
 
         supported_arch = sm_version >= 80 and sm_version < 90
-            allspark_supported = allspark_supported and supported_arch
+        as_supported_case = as_supported_case and supported_arch
         if supported_arch:
+            has_zp = False
             w_ref, qw, s, zp = quantize_weights(b, quant_type, group_size, has_zp)
             qw = qw.to(torch.uint8)
 
@@ -170,39 +131,6 @@ def bench_run(
                 qw, s, zp, has_zp
             )
             CUBLAS_M_THRESHOLD = ALLSPARK_AMPERE_M_CUBLAS_THRESHOLD
-        return (
-            qw_reorder,
-            s_reorder,
-            zp_reorder,
-            sm_count,
-            sm_version,
-            CUBLAS_M_THRESHOLD,
-        )
-
-    (
-        marlin_w_ref,
-        marlin_q_w,
-        marlin_s,
-        marlin_s2,
-        marlin_zp,
-        marlin_g_idx,
-        marlin_sort_indices,
-    ) = gen_marlin_params()
-    marlin_24_w_ref, marlin_24_q_w_comp, marlin_24_meta, marlin_24_s = (
-        gen_marlin_24_params()
-    )
-    q_w_gptq, repack_sort_indices = gen_repack_params()
-    qw_reorder, s_reorder, zp_reorder, sm_count, sm_version, CUBLAS_M_THRESHOLD = (
-        gen_allspark_params()
-    )
-
-    # Prepare
-    marlin_workspace = MarlinWorkspace(
-        size_n, GPTQ_MARLIN_MIN_THREAD_N, GPTQ_MARLIN_MAX_PARALLEL
-    )
-    marlin_24_workspace = MarlinWorkspace(
-        size_n, GPTQ_MARLIN_24_MIN_THREAD_N, GPTQ_MARLIN_24_MAX_PARALLEL
-    )
 
     globals = {
         # Gen params
@@ -212,14 +140,15 @@ def bench_run(
         "size_n": size_n,
         "size_k": size_k,
         "a": a,
+        "a_tmp": a_tmp,
         # Marlin params
         "marlin_w_ref": marlin_w_ref,
         "marlin_q_w": marlin_q_w,
         "marlin_s": marlin_s,
-        "marlin_s2": marlin_s2,
         "marlin_zp": marlin_zp,
         "marlin_g_idx": marlin_g_idx,
         "marlin_sort_indices": marlin_sort_indices,
+        "marlin_rand_perm": marlin_rand_perm,
         "marlin_workspace": marlin_workspace,
         "is_k_full": is_k_full,
         # Marlin_24 params
@@ -232,12 +161,12 @@ def bench_run(
         "q_w_gptq": q_w_gptq,
         "repack_sort_indices": repack_sort_indices,
         # AllSpark W8A16 params
-        "qw_reorder": qw_reorder,
-        "s_reorder": s_reorder,
-        "zp_reorder": zp_reorder,
-        "sm_count": sm_count,
-        "sm_version": sm_version,
-        "CUBLAS_M_THRESHOLD": CUBLAS_M_THRESHOLD,
+        "qw_reorder": qw_reorder if as_supported_case else None,
+        "s_reorder": s_reorder if as_supported_case else None,
+        "zp_reorder": zp_reorder if as_supported_case else None,
+        "sm_count": sm_count if as_supported_case else None,
+        "sm_version": sm_version if as_supported_case else None,
+        "CUBLAS_M_THRESHOLD": CUBLAS_M_THRESHOLD if as_supported_case else None,
         # Kernels
         "gptq_marlin_gemm": ops.gptq_marlin_gemm,
         "gptq_marlin_24_gemm": ops.gptq_marlin_24_gemm,
@@ -248,7 +177,7 @@ def bench_run(
     min_run_time = 1
 
     # Warmup pytorch
-    for _ in range(5):
+    for i in range(5):
         torch.matmul(a, marlin_w_ref)
 
     results.append(
@@ -263,17 +192,17 @@ def bench_run(
 
     results.append(
         benchmark.Timer(
-            stmt="output = gptq_marlin_gemm(a, None, marlin_q_w, marlin_s, marlin_s2, marlin_zp, marlin_g_idx, marlin_sort_indices, marlin_workspace.scratch, quant_type, size_m, size_n, size_k, is_k_full, False, False, False)",  # noqa: E501
+            stmt="output = gptq_marlin_gemm(a, marlin_q_w, marlin_s, marlin_zp, marlin_g_idx, marlin_sort_indices, marlin_workspace.scratch, quant_type, size_m, size_n, size_k, is_k_full, False, False, False)",  # noqa: E501
             globals=globals,
             label=label,
             sub_label=sub_label,
-            description="gptq_marlin_gemm",
+            description="gptq_marlin_gemm_fp16",
         ).blocked_autorange(min_run_time=min_run_time)
     )
 
     results.append(
         benchmark.Timer(
-            stmt="output = gptq_marlin_gemm(a, None, marlin_q_w, marlin_s, marlin_s2, marlin_zp, marlin_g_idx, marlin_sort_indices, marlin_workspace.scratch, quant_type, size_m, size_n, size_k, is_k_full, False, True, False)",  # noqa: E501
+            stmt="output = gptq_marlin_gemm(a, marlin_q_w, marlin_s, marlin_zp, marlin_g_idx, marlin_sort_indices, marlin_workspace.scratch, quant_type, size_m, size_n, size_k, is_k_full, False, True, False)",  # noqa: E501
             globals=globals,
             label=label,
             sub_label=sub_label,
@@ -281,7 +210,10 @@ def bench_run(
         ).blocked_autorange(min_run_time=min_run_time)
     )
 
-    if marlin_24_supported:
+    if (
+        quant_type in GPTQ_MARLIN_24_SUPPORTED_QUANT_TYPES
+        and group_size in GPTQ_MARLIN_24_SUPPORTED_GROUP_SIZES
+    ):
         results.append(
             benchmark.Timer(
                 stmt="output = gptq_marlin_24_gemm(a, marlin_24_q_w_comp, marlin_24_meta, marlin_24_s, marlin_24_workspace.scratch, quant_type, size_m, size_n, size_k)",  # noqa: E501
@@ -292,7 +224,6 @@ def bench_run(
             ).blocked_autorange(min_run_time=min_run_time)
         )
 
-    if repack_supported:
     results.append(
         benchmark.Timer(
             stmt="q_res = gptq_marlin_repack(q_w_gptq, repack_sort_indices, size_k, size_n, quant_type.size_bits)",  # noqa: E501
@@ -303,7 +234,7 @@ def bench_run(
         ).blocked_autorange(min_run_time=min_run_time)
     )
 
-    if allspark_supported:
+    if as_supported_case:
         results.append(
             benchmark.Timer(
                 stmt="output = allspark_w8a16_gemm(a, qw_reorder, s_reorder, zp_reorder, size_n, group_size, sm_count, sm_version, CUBLAS_M_THRESHOLD, False, True)",  # noqa: E501
@@ -319,6 +250,7 @@ def main(args):
     print("Benchmarking models:")
     for i, model in enumerate(args.models):
         print(f"[{i}]  {model}")
+
     results: list[benchmark.Measurement] = []
 
     for model in args.models:
@@ -346,17 +278,14 @@ def main(args):
                     ):
                         continue
 
-                    for quant_type in query_marlin_supported_quant_types():
+                    for quant_type in query_marlin_supported_quant_types(False):
                         if (
                             len(args.limit_num_bits) > 0
                             and quant_type.size_bits not in args.limit_num_bits
                         ):
                             continue
 
-                        for group_size in (
-                            MARLIN_SUPPORTED_GROUP_SIZES
-                            + FP4_MARLIN_SUPPORTED_GROUP_SIZES
-                        ):
+                        for group_size in MARLIN_SUPPORTED_GROUP_SIZES:
                             if (
                                 len(args.limit_group_size) > 0
                                 and group_size not in args.limit_group_size

docker/Dockerfile.rocm_base

@@ -12,7 +12,7 @@ ARG PYTORCH_REPO="https://github.com/pytorch/pytorch.git"
 ARG PYTORCH_VISION_REPO="https://github.com/pytorch/vision.git"
 ARG FA_BRANCH="1a7f4dfa"
 ARG FA_REPO="https://github.com/Dao-AILab/flash-attention.git"
-ARG AITER_BRANCH="6487649"
+ARG AITER_BRANCH="c1debd8"
 ARG AITER_REPO="https://github.com/ROCm/aiter.git"
 
 FROM ${BASE_IMAGE} AS base

docs/ci/update_pytorch_version.md

@@ -91,7 +91,7 @@ source to unblock the update process.
 ### FlashInfer
 Here is how to build and install it from source with torch2.7.0+cu128 in vLLM [Dockerfile](https://github.com/vllm-project/vllm/blob/27bebcd89792d5c4b08af7a65095759526f2f9e1/docker/Dockerfile#L259-L271):
 
-```bash
+```
 export TORCH_CUDA_ARCH_LIST='7.5 8.0 8.9 9.0 10.0+PTX'
 export FLASHINFER_ENABLE_SM90=1
 uv pip install --system --no-build-isolation "git+https://github.com/flashinfer-ai/flashinfer@v0.2.6.post1"
@@ -105,14 +105,14 @@ team if you want to get the package published there.
 ### xFormers
 Similar to FlashInfer, here is how to build and install xFormers from source:
 
-```bash
+```
 export TORCH_CUDA_ARCH_LIST='7.0 7.5 8.0 8.9 9.0 10.0+PTX'
 MAX_JOBS=16 uv pip install --system --no-build-isolation "git+https://github.com/facebookresearch/xformers@v0.0.30"
 ```
 
 ### Mamba
 
-```bash
+```
 uv pip install --system --no-build-isolation "git+https://github.com/state-spaces/mamba@v2.2.4"
 ```
 

docs/cli/README.md

@@ -16,7 +16,7 @@ vllm {chat,complete,serve,bench,collect-env,run-batch}
 
 Start the vLLM OpenAI Compatible API server.
 
-??? Examples
+Examples:
 
 ```bash
 # Start with a model
@@ -43,6 +43,8 @@ Start the vLLM OpenAI Compatible API server.
 
 Generate chat completions via the running API server.
 
+Examples:
+
 ```bash
 # Directly connect to localhost API without arguments
 vllm chat
@@ -58,6 +60,8 @@ vllm chat --quick "hi"
 
 Generate text completions based on the given prompt via the running API server.
 
+Examples:
+
 ```bash
 # Directly connect to localhost API without arguments
 vllm complete
@@ -69,8 +73,6 @@ vllm complete --url http://{vllm-serve-host}:{vllm-serve-port}/v1
 vllm complete --quick "The future of AI is"
 ```
 
-</details>
-
 ## bench
 
 Run benchmark tests for latency online serving throughput and offline inference throughput.
@@ -87,6 +89,8 @@ vllm bench {latency, serve, throughput}
 
 Benchmark the latency of a single batch of requests.
 
+Example:
+
 ```bash
 vllm bench latency \
     --model meta-llama/Llama-3.2-1B-Instruct \
@@ -100,6 +104,8 @@ vllm bench latency \
 
 Benchmark the online serving throughput.
 
+Example:
+
 ```bash
 vllm bench serve \
     --model meta-llama/Llama-3.2-1B-Instruct \
@@ -114,6 +120,8 @@ vllm bench serve \
 
 Benchmark offline inference throughput.
 
+Example:
+
 ```bash
 vllm bench throughput \
     --model meta-llama/Llama-3.2-1B-Instruct \
@@ -135,8 +143,7 @@ vllm collect-env
 
 Run batch prompts and write results to file.
 
-<details>
-<summary>Examples</summary>
+Examples:
 
 ```bash
 # Running with a local file
@@ -152,8 +159,6 @@ vllm run-batch \
     --model meta-llama/Meta-Llama-3-8B-Instruct
 ```
 
-</details>
-
 ## More Help
 
 For detailed options of any subcommand, use:

docs/community/contact_us.md

@@ -1,6 +0,0 @@
----
-title: Contact Us
----
-[](){ #contactus }
-
---8<-- "README.md:contact-us"

docs/configuration/conserving_memory.md

@@ -57,8 +57,6 @@ By default, we optimize model inference using CUDA graphs which take up extra me
 
 You can adjust `compilation_config` to achieve a better balance between inference speed and memory usage:
 
-??? Code
-
 ```python
 from vllm import LLM
 from vllm.config import CompilationConfig, CompilationLevel
@@ -129,8 +127,6 @@ reduce the size of the processed multi-modal inputs, which in turn saves memory.
 
 Here are some examples:
 
-??? Code
-
 ```python
 from vllm import LLM
 

docs/configuration/env_vars.md

@@ -7,8 +7,6 @@ vLLM uses the following environment variables to configure the system:
 
     All environment variables used by vLLM are prefixed with `VLLM_`. **Special care should be taken for Kubernetes users**: please do not name the service as `vllm`, otherwise environment variables set by Kubernetes might conflict with vLLM's environment variables, because [Kubernetes sets environment variables for each service with the capitalized service name as the prefix](https://kubernetes.io/docs/concepts/services-networking/service/#environment-variables).
 
-??? Code
-
 ```python
 --8<-- "vllm/envs.py:env-vars-definition"
 ```

docs/contributing/README.md

@@ -93,8 +93,6 @@ For additional features and advanced configurations, refer to the official [MkDo
 
 ## Testing
 
-??? note "Commands"
-
 ```bash
 pip install -r requirements/dev.txt
 

docs/contributing/model/basic.md

@@ -27,8 +27,6 @@ All vLLM modules within the model must include a `prefix` argument in their cons
 
 The initialization code should look like this:
 
-??? Code
-
 ```python
 from torch import nn
 from vllm.config import VllmConfig

docs/contributing/model/multimodal.md

@@ -25,8 +25,6 @@ Further update the model as follows:
 
 - Implement [get_multimodal_embeddings][vllm.model_executor.models.interfaces.SupportsMultiModal.get_multimodal_embeddings] that returns the embeddings from running the multimodal inputs through the multimodal tokenizer of the model. Below we provide a boilerplate of a typical implementation pattern, but feel free to adjust it to your own needs.
 
-    ??? Code
-
     ```python
     class YourModelForImage2Seq(nn.Module):
         ...
@@ -55,8 +53,6 @@ Further update the model as follows:
 
 - Implement [get_input_embeddings][vllm.model_executor.models.interfaces.SupportsMultiModal.get_input_embeddings] to merge `multimodal_embeddings` with text embeddings from the `input_ids`. If input processing for the model is implemented correctly (see sections below), then you can leverage the utility function we provide to easily merge the embeddings.
 
-    ??? Code
-
     ```python
     from .utils import merge_multimodal_embeddings
 
@@ -139,8 +135,6 @@ Assuming that the memory usage increases with the number of tokens, the dummy in
 
     Looking at the code of HF's `LlavaForConditionalGeneration`:
 
-    ??? Code
-
     ```python
     # https://github.com/huggingface/transformers/blob/v4.47.1/src/transformers/models/llava/modeling_llava.py#L530-L544
     n_image_tokens = (input_ids == self.config.image_token_index).sum().item()
@@ -163,8 +157,6 @@ Assuming that the memory usage increases with the number of tokens, the dummy in
     The number of placeholder feature tokens per image is `image_features.shape[1]`.
     `image_features` is calculated inside the `get_image_features` method:
 
-    ??? Code
-
     ```python
     # https://github.com/huggingface/transformers/blob/v4.47.1/src/transformers/models/llava/modeling_llava.py#L290-L300
     image_outputs = self.vision_tower(pixel_values, output_hidden_states=True)
@@ -201,8 +193,6 @@ Assuming that the memory usage increases with the number of tokens, the dummy in
 
     To find the sequence length, we turn to the code of `CLIPVisionEmbeddings`:
 
-    ??? Code
-
     ```python
     # https://github.com/huggingface/transformers/blob/v4.47.1/src/transformers/models/clip/modeling_clip.py#L247-L257
     target_dtype = self.patch_embedding.weight.dtype
@@ -228,8 +218,6 @@ Assuming that the memory usage increases with the number of tokens, the dummy in
 
     Overall, the number of placeholder feature tokens for an image can be calculated as:
 
-    ??? Code
-
     ```python
     def get_num_image_tokens(
         self,
@@ -253,8 +241,6 @@ Assuming that the memory usage increases with the number of tokens, the dummy in
     Notice that the number of image tokens doesn't depend on the image width and height.
     We can simply use a dummy `image_size` to calculate the multimodal profiling data:
 
-    ??? Code
-
     ```python
     # NOTE: In actuality, this is usually implemented as part of the
     # model's subclass of `BaseProcessingInfo`, but we show it as is
@@ -298,8 +284,6 @@ Assuming that the memory usage increases with the number of tokens, the dummy in
 
     Looking at the code of HF's `FuyuForCausalLM`:
 
-    ??? Code
-
     ```python
     # https://github.com/huggingface/transformers/blob/v4.48.3/src/transformers/models/fuyu/modeling_fuyu.py#L311-L322
     if image_patches is not None and past_key_values is None:
@@ -328,8 +312,6 @@ Assuming that the memory usage increases with the number of tokens, the dummy in
     In `FuyuImageProcessor.preprocess`, the images are resized and padded to the target `FuyuImageProcessor.size`,
     returning the dimensions after resizing (but before padding) as metadata.
 
-    ??? Code
-
     ```python
     # https://github.com/huggingface/transformers/blob/v4.48.3/src/transformers/models/fuyu/processing_fuyu.py#L541-L544
     image_encoding = self.image_processor.preprocess(images, **output_kwargs["images_kwargs"])
@@ -366,8 +348,6 @@ Assuming that the memory usage increases with the number of tokens, the dummy in
 
     In `FuyuImageProcessor.preprocess_with_tokenizer_info`, the images are split into patches based on this metadata:
 
-    ??? Code
-
     ```python
     # https://github.com/huggingface/transformers/blob/v4.48.3/src/transformers/models/fuyu/processing_fuyu.py#L417-L425
     model_image_input = self.image_processor.preprocess_with_tokenizer_info(
@@ -404,8 +384,6 @@ Assuming that the memory usage increases with the number of tokens, the dummy in
 
     The number of patches is in turn defined by `FuyuImageProcessor.get_num_patches`:
 
-    ??? Code
-
     ```python
     # https://github.com/huggingface/transformers/blob/v4.48.3/src/transformers/models/fuyu/image_processing_fuyu.py#L552-L562
     patch_size = patch_size if patch_size is not None else self.patch_size
@@ -441,8 +419,6 @@ Assuming that the memory usage increases with the number of tokens, the dummy in
 
     For the multimodal image profiling data, the logic is very similar to LLaVA:
 
-    ??? Code
-
     ```python
     def get_dummy_mm_data(
         self,
@@ -479,7 +455,6 @@ return a schema of the tensors outputted by the HF processor that are related to
     The output of `CLIPImageProcessor` is a simple tensor with shape
     `(num_images, num_channels, image_height, image_width)`:
 
-
     ```python
     # https://github.com/huggingface/transformers/blob/v4.47.1/src/transformers/models/clip/image_processing_clip.py#L339-L345
     images = [
@@ -530,8 +505,6 @@ return a schema of the tensors outputted by the HF processor that are related to
     In order to support the use of [MultiModalFieldConfig.batched][] like in LLaVA,
     we remove the extra batch dimension by overriding [BaseMultiModalProcessor._call_hf_processor][]:
 
-    ??? Code
-
     ```python
     def _call_hf_processor(
         self,
@@ -600,8 +573,6 @@ Each [PromptUpdate][vllm.multimodal.processing.PromptUpdate] instance specifies
     It simply repeats each input `image_token` a number of times equal to the number of placeholder feature tokens (`num_image_tokens`).
     Based on this, we override [_get_prompt_updates][vllm.multimodal.processing.BaseMultiModalProcessor._get_prompt_updates] as follows:
 
-    ??? Code
-
     ```python
     def _get_prompt_updates(
         self,
@@ -645,8 +616,6 @@ Each [PromptUpdate][vllm.multimodal.processing.PromptUpdate] instance specifies
 
     We define a helper function to return `ncols` and `nrows` directly:
 
-    ??? Code
-
     ```python
     def get_image_feature_grid_size(
         self,
@@ -675,8 +644,6 @@ Each [PromptUpdate][vllm.multimodal.processing.PromptUpdate] instance specifies
 
     Based on this, we can initially define our replacement tokens as:
 
-    ??? Code
-
     ```python
     def get_replacement(item_idx: int):
         images = mm_items.get_items("image", ImageProcessorItems)
@@ -695,8 +662,6 @@ Each [PromptUpdate][vllm.multimodal.processing.PromptUpdate] instance specifies
     However, this is not entirely correct. After `FuyuImageProcessor.preprocess_with_tokenizer_info` is called,
     a BOS token (`<s>`) is also added to the promopt:
 
-    ??? Code
-
     ```python
     # https://github.com/huggingface/transformers/blob/v4.48.3/src/transformers/models/fuyu/processing_fuyu.py#L417-L435
     model_image_input = self.image_processor.preprocess_with_tokenizer_info(
@@ -722,8 +687,6 @@ Each [PromptUpdate][vllm.multimodal.processing.PromptUpdate] instance specifies
     To assign the vision embeddings to only the image tokens, instead of a string
     you can return an instance of [PromptUpdateDetails][vllm.multimodal.processing.PromptUpdateDetails]:
 
-    ??? Code
-
     ```python
     hf_config = self.info.get_hf_config()
     bos_token_id = hf_config.bos_token_id  # `<s>`
@@ -749,8 +712,6 @@ Each [PromptUpdate][vllm.multimodal.processing.PromptUpdate] instance specifies
     Finally, noticing that the HF processor removes the `|ENDOFTEXT|` token from the tokenized prompt,
     we can search for it to conduct the replacement at the start of the string:
 
-    ??? Code
-
     ```python
     def _get_prompt_updates(
         self,

docs/contributing/profiling.md

@@ -30,21 +30,13 @@ Refer to <gh-file:examples/offline_inference/simple_profiling.py> for an example
 #### OpenAI Server
 
 ```bash
-VLLM_TORCH_PROFILER_DIR=./vllm_profile \
-    python -m vllm.entrypoints.openai.api_server \
-    --model meta-llama/Meta-Llama-3-70B
+VLLM_TORCH_PROFILER_DIR=./vllm_profile python -m vllm.entrypoints.openai.api_server --model meta-llama/Meta-Llama-3-70B
 ```
 
 benchmark_serving.py:
 
 ```bash
-python benchmarks/benchmark_serving.py \
-    --backend vllm \
-    --model meta-llama/Meta-Llama-3-70B \
-    --dataset-name sharegpt \
-    --dataset-path sharegpt.json \
-    --profile \
-    --num-prompts 2
+python benchmarks/benchmark_serving.py --backend vllm --model meta-llama/Meta-Llama-3-70B --dataset-name sharegpt --dataset-path sharegpt.json --profile --num-prompts 2
 ```
 
 ## Profile with NVIDIA Nsight Systems
@@ -72,16 +64,7 @@ For basic usage, you can just append `nsys profile -o report.nsys-rep --trace-fo
 The following is an example using the `benchmarks/benchmark_latency.py` script:
 
 ```bash
-nsys profile -o report.nsys-rep \
-    --trace-fork-before-exec=true \
-    --cuda-graph-trace=node \
-    python benchmarks/benchmark_latency.py \
-    --model meta-llama/Llama-3.1-8B-Instruct \
-    --num-iters-warmup 5 \
-    --num-iters 1 \
-    --batch-size 16 \
-    --input-len 512 \
-    --output-len 8
+nsys profile -o report.nsys-rep --trace-fork-before-exec=true --cuda-graph-trace=node python benchmarks/benchmark_latency.py --model meta-llama/Llama-3.1-8B-Instruct --num-iters-warmup 5 --num-iters 1 --batch-size 16 --input-len 512 --output-len 8
 ```
 
 #### OpenAI Server
@@ -90,21 +73,10 @@ To profile the server, you will want to prepend your `vllm serve` command with `
 
 ```bash
 # server
-nsys profile -o report.nsys-rep \
-    --trace-fork-before-exec=true \
-    --cuda-graph-trace=node \
-    --delay 30 \
-    --duration 60 \
-    vllm serve meta-llama/Llama-3.1-8B-Instruct
+nsys profile -o report.nsys-rep --trace-fork-before-exec=true --cuda-graph-trace=node --delay 30 --duration 60 vllm serve meta-llama/Llama-3.1-8B-Instruct
 
 # client
-python benchmarks/benchmark_serving.py \
-    --backend vllm \
-    --model meta-llama/Llama-3.1-8B-Instruct \
-    --num-prompts 1 \
-    --dataset-name random \
-    --random-input 1024 \
-    --random-output 512
+python benchmarks/benchmark_serving.py --backend vllm --model meta-llama/Llama-3.1-8B-Instruct --num-prompts 1 --dataset-name random --random-input 1024 --random-output 512
 ```
 
 In practice, you should set the `--duration` argument to a large value. Whenever you want the server to stop profiling, run:
@@ -125,7 +97,7 @@ to manually kill the profiler and generate your `nsys-rep` report.
 
 You can view these profiles either as summaries in the CLI, using `nsys stats [profile-file]`, or in the GUI by installing Nsight [locally following the directions here](https://developer.nvidia.com/nsight-systems/get-started).
 
-??? CLI example
+CLI example:
 
 ```bash
 nsys stats report1.nsys-rep

docs/deployment/docker.md

@@ -10,7 +10,7 @@ title: Using Docker
 vLLM offers an official Docker image for deployment.
 The image can be used to run OpenAI compatible server and is available on Docker Hub as [vllm/vllm-openai](https://hub.docker.com/r/vllm/vllm-openai/tags).
 
-```bash
+```console
 docker run --runtime nvidia --gpus all \
     -v ~/.cache/huggingface:/root/.cache/huggingface \
     --env "HUGGING_FACE_HUB_TOKEN=<secret>" \
@@ -22,7 +22,7 @@ docker run --runtime nvidia --gpus all \
 
 This image can also be used with other container engines such as [Podman](https://podman.io/).
 
-```bash
+```console
 podman run --gpus all \
   -v ~/.cache/huggingface:/root/.cache/huggingface \
   --env "HUGGING_FACE_HUB_TOKEN=$HF_TOKEN" \
@@ -71,7 +71,7 @@ You can add any other [engine-args][engine-args] you need after the image tag (`
 
 You can build and run vLLM from source via the provided <gh-file:docker/Dockerfile>. To build vLLM:
 
-```bash
+```console
 # optionally specifies: --build-arg max_jobs=8 --build-arg nvcc_threads=2
 DOCKER_BUILDKIT=1 docker build . \
     --target vllm-openai \
@@ -97,9 +97,7 @@ of PyTorch Nightly and should be considered **experimental**. Using the flag `--
     flags to speed up build process. However, ensure your `max_jobs` is substantially larger than `nvcc_threads` to get the most benefits.
     Keep an eye on memory usage with parallel jobs as it can be substantial (see example below).
 
-??? Command
-
-    ```bash
+```console
 # Example of building on Nvidia GH200 server. (Memory usage: ~15GB, Build time: ~1475s / ~25 min, Image size: 6.93GB)
 python3 use_existing_torch.py
 DOCKER_BUILDKIT=1 docker build . \
@@ -118,7 +116,7 @@ of PyTorch Nightly and should be considered **experimental**. Using the flag `--
 
     Run the following command on your host machine to register QEMU user static handlers:
 
-    ```bash
+    ```console
     docker run --rm --privileged multiarch/qemu-user-static --reset -p yes
     ```
 
@@ -128,7 +126,7 @@ of PyTorch Nightly and should be considered **experimental**. Using the flag `--
 
 To run vLLM with the custom-built Docker image:
 
-```bash
+```console
 docker run --runtime nvidia --gpus all \
     -v ~/.cache/huggingface:/root/.cache/huggingface \
     -p 8000:8000 \

docs/deployment/frameworks/anything-llm.md

@@ -15,7 +15,7 @@ It allows you to deploy a large language model (LLM) server with vLLM as the bac
 
 - Start the vLLM server with the supported chat completion model, e.g.
 
-```bash
+```console
 vllm serve Qwen/Qwen1.5-32B-Chat-AWQ --max-model-len 4096
 ```
 

docs/deployment/frameworks/autogen.md

@@ -11,7 +11,7 @@ title: AutoGen
 
 - Setup [AutoGen](https://microsoft.github.io/autogen/0.2/docs/installation/) environment
 
-```bash
+```console
 pip install vllm
 
 # Install AgentChat and OpenAI client from Extensions
@@ -23,15 +23,13 @@ pip install -U "autogen-agentchat" "autogen-ext[openai]"
 
 - Start the vLLM server with the supported chat completion model, e.g.
 
-```bash
+```console
 python -m vllm.entrypoints.openai.api_server \
     --model mistralai/Mistral-7B-Instruct-v0.2
 ```
 
 - Call it with AutoGen:
 
-??? Code
-
 ```python
 import asyncio
 from autogen_core.models import UserMessage

docs/deployment/frameworks/cerebrium.md

@@ -11,14 +11,14 @@ vLLM can be run on a cloud based GPU machine with [Cerebrium](https://www.cerebr
 
 To install the Cerebrium client, run:
 
-```bash
+```console
 pip install cerebrium
 cerebrium login
 ```
 
 Next, create your Cerebrium project, run:
 
-```bash
+```console
 cerebrium init vllm-project
 ```
 
@@ -34,8 +34,6 @@ vllm = "latest"
 
 Next, let us add our code to handle inference for the LLM of your choice (`mistralai/Mistral-7B-Instruct-v0.1` for this example), add the following code to your `main.py`:
 
-??? Code
-
 ```python
 from vllm import LLM, SamplingParams
 
@@ -58,14 +56,12 @@ Next, let us add our code to handle inference for the LLM of your choice (`mistr
 
 Then, run the following code to deploy it to the cloud:
 
-```bash
+```console
 cerebrium deploy
 ```
 
 If successful, you should be returned a CURL command that you can call inference against. Just remember to end the url with the function name you are calling (in our case`/run`)
 
-??? Command
-
 ```python
 curl -X POST https://api.cortex.cerebrium.ai/v4/p-xxxxxx/vllm/run \
  -H 'Content-Type: application/json' \
@@ -82,8 +78,6 @@ If successful, you should be returned a CURL command that you can call inference
 
 You should get a response like:
 
-??? Response
-
 ```python
 {
     "run_id": "52911756-3066-9ae8-bcc9-d9129d1bd262",

docs/deployment/frameworks/chatbox.md

@@ -15,7 +15,7 @@ It allows you to deploy a large language model (LLM) server with vLLM as the bac
 
 - Start the vLLM server with the supported chat completion model, e.g.
 
-```bash
+```console
 vllm serve qwen/Qwen1.5-0.5B-Chat
 ```
 

docs/deployment/frameworks/dify.md

@@ -18,13 +18,13 @@ This guide walks you through deploying Dify using a vLLM backend.
 
 - Start the vLLM server with the supported chat completion model, e.g.
 
-```bash
+```console
 vllm serve Qwen/Qwen1.5-7B-Chat
 ```
 
 - Start the Dify server with docker compose ([details](https://github.com/langgenius/dify?tab=readme-ov-file#quick-start)):
 
-```bash
+```console
 git clone https://github.com/langgenius/dify.git
 cd dify
 cd docker

docs/deployment/frameworks/dstack.md

@@ -11,14 +11,14 @@ vLLM can be run on a cloud based GPU machine with [dstack](https://dstack.ai/),
 
 To install dstack client, run:
 
-```bash
+```console
 pip install "dstack[all]
 dstack server
 ```
 
 Next, to configure your dstack project, run:
 
-```bash
+```console
 mkdir -p vllm-dstack
 cd vllm-dstack
 dstack init
@@ -26,8 +26,6 @@ dstack init
 
 Next, to provision a VM instance with LLM of your choice (`NousResearch/Llama-2-7b-chat-hf` for this example), create the following `serve.dstack.yml` file for the dstack `Service`:
 
-??? Config
-
 ```yaml
 type: service
 
@@ -48,8 +46,6 @@ Next, to provision a VM instance with LLM of your choice (`NousResearch/Llama-2-
 
 Then, run the following CLI for provisioning:
 
-??? Command
-
 ```console
 $ dstack run . -f serve.dstack.yml
 
@@ -79,8 +75,6 @@ Then, run the following CLI for provisioning:
 
 After the provisioning, you can interact with the model by using the OpenAI SDK:
 
-??? Code
-
 ```python
 from openai import OpenAI
 

docs/deployment/frameworks/haystack.md

@@ -13,7 +13,7 @@ It allows you to deploy a large language model (LLM) server with vLLM as the bac
 
 - Setup vLLM and Haystack environment
 
-```bash
+```console
 pip install vllm haystack-ai
 ```
 
@@ -21,14 +21,12 @@ pip install vllm haystack-ai
 
 - Start the vLLM server with the supported chat completion model, e.g.
 
-```bash
+```console
 vllm serve mistralai/Mistral-7B-Instruct-v0.1
 ```
 
 - Use the `OpenAIGenerator` and `OpenAIChatGenerator` components in Haystack to query the vLLM server.
 
-??? Code
-
 ```python
 from haystack.components.generators.chat import OpenAIChatGenerator
 from haystack.dataclasses import ChatMessage
@@ -51,6 +49,8 @@ vllm serve mistralai/Mistral-7B-Instruct-v0.1
 print("-"*30)
 ```
 
+Output e.g.:
+
 ```console
 ------------------------------
 {'replies': [ChatMessage(_role=<ChatRole.ASSISTANT: 'assistant'>, _content=[TextContent(text=' Of course! Where in Italy would you like to go and what type of trip are you looking to plan?')], _name=None, _meta={'model': 'mistralai/Mistral-7B-Instruct-v0.1', 'index': 0, 'finish_reason': 'stop', 'usage': {'completion_tokens': 23, 'prompt_tokens': 21, 'total_tokens': 44, 'completion_tokens_details': None, 'prompt_tokens_details': None}})]}

docs/deployment/frameworks/helm.md

@@ -22,7 +22,7 @@ Before you begin, ensure that you have the following:
 
 To install the chart with the release name `test-vllm`:
 
-```bash
+```console
 helm upgrade --install --create-namespace --namespace=ns-vllm test-vllm . -f values.yaml --set secrets.s3endpoint=$ACCESS_POINT --set secrets.s3bucketname=$BUCKET --set secrets.s3accesskeyid=$ACCESS_KEY --set secrets.s3accesskey=$SECRET_KEY
 ```
 
@@ -30,7 +30,7 @@ helm upgrade --install --create-namespace --namespace=ns-vllm test-vllm . -f val
 
 To uninstall the `test-vllm` deployment:
 
-```bash
+```console
 helm uninstall test-vllm --namespace=ns-vllm
 ```
 

docs/deployment/frameworks/litellm.md

@@ -18,7 +18,7 @@ And LiteLLM supports all models on VLLM.
 
 - Setup vLLM and litellm environment
 
-```bash
+```console
 pip install vllm litellm
 ```
 
@@ -28,14 +28,12 @@ pip install vllm litellm
 
 - Start the vLLM server with the supported chat completion model, e.g.
 
-```bash
+```console
 vllm serve qwen/Qwen1.5-0.5B-Chat
 ```
 
 - Call it with litellm:
 
-??? Code
-
 ```python
 import litellm 
 
@@ -56,7 +54,7 @@ vllm serve qwen/Qwen1.5-0.5B-Chat
 
 - Start the vLLM server with the supported embedding model, e.g.
 
-```bash
+```console
 vllm serve BAAI/bge-base-en-v1.5
 ```
 

docs/deployment/frameworks/lws.md

@@ -17,8 +17,6 @@ vLLM can be deployed with [LWS](https://github.com/kubernetes-sigs/lws) on Kuber
 
 Deploy the following yaml file `lws.yaml`
 
-??? Yaml
-
 ```yaml
 apiVersion: leaderworkerset.x-k8s.io/v1
 kind: LeaderWorkerSet
@@ -177,8 +175,6 @@ curl http://localhost:8080/v1/completions \
 
 The output should be similar to the following
 
-??? Output
-
 ```text
 {
   "id": "cmpl-1bb34faba88b43f9862cfbfb2200949d",

docs/deployment/frameworks/open-webui.md

@@ -7,13 +7,13 @@ title: Open WebUI
 
 2. Start the vLLM server with the supported chat completion model, e.g.
 
-```bash
+```console
 vllm serve qwen/Qwen1.5-0.5B-Chat
 ```
 
 1. Start the [Open WebUI](https://github.com/open-webui/open-webui) docker container (replace the vllm serve host and vllm serve port):
 
-```bash
+```console
 docker run -d -p 3000:8080 \
 --name open-webui \
 -v open-webui:/app/backend/data \

docs/deployment/frameworks/retrieval_augmented_generation.md

@@ -15,7 +15,7 @@ Here are the integrations:
 
 - Setup vLLM and langchain environment
 
-```bash
+```console
 pip install -U vllm \
             langchain_milvus langchain_openai \
             langchain_community beautifulsoup4 \
@@ -26,14 +26,14 @@ pip install -U vllm \
 
 - Start the vLLM server with the supported embedding model, e.g.
 
-```bash
+```console
 # Start embedding service (port 8000)
 vllm serve ssmits/Qwen2-7B-Instruct-embed-base
 ```
 
 - Start the vLLM server with the supported chat completion model, e.g.
 
-```bash
+```console
 # Start chat service (port 8001)
 vllm serve qwen/Qwen1.5-0.5B-Chat --port 8001
 ```
@@ -52,7 +52,7 @@ python retrieval_augmented_generation_with_langchain.py
 
 - Setup vLLM and llamaindex environment
 
-```bash
+```console
 pip install vllm \
             llama-index llama-index-readers-web \
             llama-index-llms-openai-like    \
@@ -64,14 +64,14 @@ pip install vllm \
 
 - Start the vLLM server with the supported embedding model, e.g.
 
-```bash
+```console
 # Start embedding service (port 8000)
 vllm serve ssmits/Qwen2-7B-Instruct-embed-base
 ```
 
 - Start the vLLM server with the supported chat completion model, e.g.
 
-```bash
+```console
 # Start chat service (port 8001)
 vllm serve qwen/Qwen1.5-0.5B-Chat --port 8001
 ```

docs/deployment/frameworks/skypilot.md

@@ -15,7 +15,7 @@ vLLM can be **run and scaled to multiple service replicas on clouds and Kubernet
 - Check that you have installed SkyPilot ([docs](https://skypilot.readthedocs.io/en/latest/getting-started/installation.html)).
 - Check that `sky check` shows clouds or Kubernetes are enabled.
 
-```bash
+```console
 pip install skypilot-nightly
 sky check
 ```
@@ -24,8 +24,6 @@ sky check
 
 See the vLLM SkyPilot YAML for serving, [serving.yaml](https://github.com/skypilot-org/skypilot/blob/master/llm/vllm/serve.yaml).
 
-??? Yaml
-
 ```yaml
 resources:
   accelerators: {L4, A10g, A10, L40, A40, A100, A100-80GB} # We can use cheaper accelerators for 8B model.
@@ -71,7 +69,7 @@ See the vLLM SkyPilot YAML for serving, [serving.yaml](https://github.com/skypil
 
 Start the serving the Llama-3 8B model on any of the candidate GPUs listed (L4, A10g, ...):
 
-```bash
+```console
 HF_TOKEN="your-huggingface-token" sky launch serving.yaml --env HF_TOKEN
 ```
 
@@ -83,7 +81,7 @@ Check the output of the command. There will be a shareable gradio link (like the
 
 **Optional**: Serve the 70B model instead of the default 8B and use more GPU:
 
-```bash
+```console
 HF_TOKEN="your-huggingface-token" \
   sky launch serving.yaml \
   --gpus A100:8 \
@@ -95,8 +93,6 @@ HF_TOKEN="your-huggingface-token" \
 
 SkyPilot can scale up the service to multiple service replicas with built-in autoscaling, load-balancing and fault-tolerance. You can do it by adding a services section to the YAML file.
 
-??? Yaml
-
 ```yaml
 service:
   replicas: 2
@@ -111,7 +107,8 @@ SkyPilot can scale up the service to multiple service replicas with built-in aut
   max_completion_tokens: 1
 ```
 
-??? Yaml
+<details>
+<summary>Click to see the full recipe YAML</summary>
 
 ```yaml
 service:
@@ -157,9 +154,11 @@ SkyPilot can scale up the service to multiple service replicas with built-in aut
     2>&1 | tee api_server.log
 ```
 
+</details>
+
 Start the serving the Llama-3 8B model on multiple replicas:
 
-```bash
+```console
 HF_TOKEN="your-huggingface-token" \
   sky serve up -n vllm serving.yaml \
   --env HF_TOKEN
@@ -167,11 +166,12 @@ HF_TOKEN="your-huggingface-token" \
 
 Wait until the service is ready:
 
-```bash
+```console
 watch -n10 sky serve status vllm
 ```
 
-Example outputs:
+<details>
+<summary>Example outputs:</summary>
 
 ```console
 Services
@@ -184,11 +184,11 @@ vllm          1   1        xx.yy.zz.121  18 mins ago  1x GCP([Spot]{'L4': 1})  R
 vllm          2   1        xx.yy.zz.245  18 mins ago  1x GCP([Spot]{'L4': 1})  READY   us-east4
 ```
 
+</details>
+
 After the service is READY, you can find a single endpoint for the service and access the service with the endpoint:
 
-??? Commands
-
-    ```bash
+```console
 ENDPOINT=$(sky serve status --endpoint 8081 vllm)
 curl -L http://$ENDPOINT/v1/chat/completions \
   -H "Content-Type: application/json" \
@@ -220,7 +220,8 @@ service:
 
 This will scale the service up to when the QPS exceeds 2 for each replica.
 
-??? Yaml
+<details>
+<summary>Click to see the full recipe YAML</summary>
 
 ```yaml
 service:
@@ -269,15 +270,17 @@ This will scale the service up to when the QPS exceeds 2 for each replica.
     2>&1 | tee api_server.log
 ```
 
+</details>
+
 To update the service with the new config:
 
-```bash
+```console
 HF_TOKEN="your-huggingface-token" sky serve update vllm serving.yaml --env HF_TOKEN
 ```
 
 To stop the service:
 
-```bash
+```console
 sky serve down vllm
 ```
 
@@ -285,7 +288,8 @@ sky serve down vllm
 
 It is also possible to access the Llama-3 service with a separate GUI frontend, so the user requests send to the GUI will be load-balanced across replicas.
 
-??? Yaml
+<details>
+<summary>Click to see the full GUI YAML</summary>
 
 ```yaml
 envs:
@@ -315,9 +319,11 @@ It is also possible to access the Llama-3 service with a separate GUI frontend,
     --stop-token-ids 128009,128001 | tee ~/gradio.log
 ```
 
+</details>
+
 1. Start the chat web UI:
 
-    ```bash
+    ```console
     sky launch \
       -c gui ./gui.yaml \
       --env ENDPOINT=$(sky serve status --endpoint vllm)

docs/deployment/frameworks/streamlit.md

@@ -15,13 +15,13 @@ It can be quickly integrated with vLLM as a backend API server, enabling powerfu
 
 - Start the vLLM server with the supported chat completion model, e.g.
 
-```bash
+```console
 vllm serve qwen/Qwen1.5-0.5B-Chat
 ```
 
 - Install streamlit and openai:
 
-```bash
+```console
 pip install streamlit openai
 ```
 
@@ -29,7 +29,7 @@ pip install streamlit openai
 
 - Start the streamlit web UI and start to chat:
 
-```bash
+```console
 streamlit run streamlit_openai_chatbot_webserver.py
 
 # or specify the VLLM_API_BASE or VLLM_API_KEY

docs/deployment/integrations/llamastack.md

@@ -7,7 +7,7 @@ vLLM is also available via [Llama Stack](https://github.com/meta-llama/llama-sta
 
 To install Llama Stack, run
 
-```bash
+```console
 pip install llama-stack -q
 ```
 

docs/deployment/integrations/production-stack.md

@@ -60,7 +60,7 @@ And then you can send out a query to the OpenAI-compatible API to check the avai
 curl -o- http://localhost:30080/models
 ```
 
-??? Output
+Expected output:
 
 ```json
 {
@@ -89,7 +89,7 @@ curl -X POST http://localhost:30080/completions \
   }'
 ```
 
-??? Output
+Expected output:
 
 ```json
 {
@@ -121,8 +121,6 @@ sudo helm uninstall vllm
 
 The core vLLM production stack configuration is managed with YAML. Here is the example configuration used in the installation above:
 
-??? Yaml
-
 ```yaml
 servingEngineSpec:
   runtimeClassName: ""

docs/deployment/k8s.md

@@ -29,8 +29,6 @@ Alternatively, you can deploy vLLM to Kubernetes using any of the following:
 
 First, create a Kubernetes PVC and Secret for downloading and storing Hugging Face model:
 
-??? Config
-
 ```bash
 cat <<EOF |kubectl apply -f -
 apiVersion: v1
@@ -57,8 +55,6 @@ First, create a Kubernetes PVC and Secret for downloading and storing Hugging Fa
 
 Next, start the vLLM server as a Kubernetes Deployment and Service:
 
-??? Config
-
 ```bash
 cat <<EOF |kubectl apply -f -
 apiVersion: apps/v1
@@ -115,7 +111,7 @@ Next, start the vLLM server as a Kubernetes Deployment and Service:
 
 We can verify that the vLLM server has started successfully via the logs (this might take a couple of minutes to download the model):
 
-```bash
+```console
 kubectl logs -l app.kubernetes.io/name=vllm
 ...
 INFO:     Started server process [1]
@@ -132,9 +128,6 @@ INFO:     Uvicorn running on http://0.0.0.0:8000 (Press CTRL+C to quit)
 
       PVC is used to store the model cache and it is optional, you can use hostPath or other storage options
 
-      <details>
-      <summary>Yaml</summary>
-
       ```yaml
       apiVersion: v1
       kind: PersistentVolumeClaim
@@ -151,8 +144,6 @@ INFO:     Uvicorn running on http://0.0.0.0:8000 (Press CTRL+C to quit)
         volumeMode: Filesystem
       ```
 
-      </details>
-
       Secret is optional and only required for accessing gated models, you can skip this step if you are not using gated models
 
       ```yaml
@@ -172,9 +163,6 @@ INFO:     Uvicorn running on http://0.0.0.0:8000 (Press CTRL+C to quit)
 
       NVIDIA GPU:
 
-      <details>
-      <summary>Yaml</summary>
-
       ```yaml
       apiVersion: apps/v1
       kind: Deployment
@@ -245,15 +233,10 @@ INFO:     Uvicorn running on http://0.0.0.0:8000 (Press CTRL+C to quit)
                 periodSeconds: 5
       ```
 
-      </details>
-
       AMD GPU:
 
       You can refer to the `deployment.yaml` below if using AMD ROCm GPU like MI300X.
 
-      <details>
-      <summary>Yaml</summary>
-
       ```yaml
       apiVersion: apps/v1
       kind: Deployment
@@ -322,17 +305,12 @@ INFO:     Uvicorn running on http://0.0.0.0:8000 (Press CTRL+C to quit)
                 mountPath: /dev/shm
       ```
 
-      </details>
-
       You can get the full example with steps and sample yaml files from <https://github.com/ROCm/k8s-device-plugin/tree/master/example/vllm-serve>.
 
 2. Create a Kubernetes Service for vLLM
 
       Next, create a Kubernetes Service file to expose the `mistral-7b` deployment:
 
-      <details>
-      <summary>Yaml</summary>
-
       ```yaml
       apiVersion: v1
       kind: Service
@@ -352,20 +330,18 @@ INFO:     Uvicorn running on http://0.0.0.0:8000 (Press CTRL+C to quit)
         type: ClusterIP
       ```
 
-      </details>
-
 3. Deploy and Test
 
       Apply the deployment and service configurations using `kubectl apply -f <filename>`:
 
-      ```bash
+      ```console
       kubectl apply -f deployment.yaml
       kubectl apply -f service.yaml
       ```
 
       To test the deployment, run the following `curl` command:
 
-      ```bash
+      ```console
       curl http://mistral-7b.default.svc.cluster.local/v1/completions \
         -H "Content-Type: application/json" \
         -d '{

docs/deployment/nginx.md

@@ -11,13 +11,13 @@ This document shows how to launch multiple vLLM serving containers and use Nginx
 
 This guide assumes that you have just cloned the vLLM project and you're currently in the vllm root directory.
 
-```bash
+```console
 export vllm_root=`pwd`
 ```
 
 Create a file named `Dockerfile.nginx`:
 
-```dockerfile
+```console
 FROM nginx:latest
 RUN rm /etc/nginx/conf.d/default.conf
 EXPOSE 80
@@ -26,7 +26,7 @@ CMD ["nginx", "-g", "daemon off;"]
 
 Build the container:
 
-```bash
+```console
 docker build . -f Dockerfile.nginx --tag nginx-lb
 ```
 
@@ -36,8 +36,6 @@ docker build . -f Dockerfile.nginx --tag nginx-lb
 
 Create a file named `nginx_conf/nginx.conf`. Note that you can add as many servers as you'd like. In the below example we'll start with two. To add more, add another `server vllmN:8000 max_fails=3 fail_timeout=10000s;` entry to `upstream backend`.
 
-??? Config
-
 ```console
 upstream backend {
     least_conn;
@@ -60,14 +58,14 @@ Create a file named `nginx_conf/nginx.conf`. Note that you can add as many serve
 
 ## Build vLLM Container
 
-```bash
+```console
 cd $vllm_root
 docker build -f docker/Dockerfile . --tag vllm
 ```
 
 If you are behind proxy, you can pass the proxy settings to the docker build command as shown below:
 
-```bash
+```console
 cd $vllm_root
 docker build \
     -f docker/Dockerfile . \
@@ -80,7 +78,7 @@ docker build \
 
 ## Create Docker Network
 
-```bash
+```console
 docker network create vllm_nginx
 ```
 
@@ -95,8 +93,6 @@ Notes:
 - The below example assumes GPU backend used. If you are using CPU backend, remove `--gpus device=ID`, add `VLLM_CPU_KVCACHE_SPACE` and `VLLM_CPU_OMP_THREADS_BIND` environment variables to the docker run command.
 - Adjust the model name that you want to use in your vLLM servers if you don't want to use `Llama-2-7b-chat-hf`.
 
-??? Commands
-
 ```console
 mkdir -p ~/.cache/huggingface/hub/
 hf_cache_dir=~/.cache/huggingface/
@@ -129,7 +125,7 @@ Notes:
 
 ## Launch Nginx
 
-```bash
+```console
 docker run \
     -itd \
     -p 8000:80 \
@@ -142,7 +138,7 @@ docker run \
 
 ## Verify That vLLM Servers Are Ready
 
-```bash
+```console
 docker logs vllm0 | grep Uvicorn
 docker logs vllm1 | grep Uvicorn
 ```

docs/design/arch_overview.md

@@ -22,8 +22,6 @@ server.
 
 Here is a sample of `LLM` class usage:
 
-??? Code
-
 ```python
 from vllm import LLM, SamplingParams
 
@@ -180,8 +178,6 @@ vision-language model.
 
     To avoid accidentally passing incorrect arguments, the constructor is now keyword-only. This ensures that the constructor will raise an error if old configurations are passed. vLLM developers have already made this change for all models within vLLM. For out-of-tree registered models, developers need to update their models, for example by adding shim code to adapt the old constructor signature to the new one:
 
-    ??? Code
-
     ```python
     class MyOldModel(nn.Module):
         def __init__(

docs/design/kernel/paged_attention.md

@@ -448,8 +448,6 @@ elements of the entire head for all context tokens. However, overall,
 all results for output have been calculated but are just stored in
 different thread register memory.
 
-??? Code
-
 ```cpp
 float* out_smem = reinterpret_cast<float*>(shared_mem);
 for (int i = NUM_WARPS; i > 1; i /= 2) {

docs/design/plugin_system.md

@@ -13,8 +13,6 @@ Plugins are user-registered code that vLLM executes. Given vLLM's architecture (
 
 vLLM's plugin system uses the standard Python `entry_points` mechanism. This mechanism allows developers to register functions in their Python packages for use by other packages. An example of a plugin:
 
-??? Code
-
 ```python
 # inside `setup.py` file
 from setuptools import setup

docs/design/v1/p2p_nccl_connector.md

@@ -61,8 +61,6 @@ To address the above issues, I have designed and developed a local Tensor memory
 
 # Install vLLM
 
-??? Commands
-
 ```shell
 # Enter the home directory or your working directory.
 cd /home
@@ -106,8 +104,6 @@ python3 disagg_prefill_proxy_xpyd.py &
 
 ### Prefill1 (e.g. 10.0.1.2 or 10.0.1.1)
 
-??? Command
-
 ```shell
 VLLM_USE_V1=1 CUDA_VISIBLE_DEVICES=0 vllm serve {your model directory} \
     --host 0.0.0.0 \
@@ -128,8 +124,6 @@ python3 disagg_prefill_proxy_xpyd.py &
 
 ### Decode1 (e.g. 10.0.1.3 or 10.0.1.1)
 
-??? Command
-
 ```shell
 VLLM_USE_V1=1 CUDA_VISIBLE_DEVICES=1 vllm serve {your model directory} \
     --host 0.0.0.0 \
@@ -150,8 +144,6 @@ python3 disagg_prefill_proxy_xpyd.py &
 
 ### Decode2 (e.g. 10.0.1.4 or 10.0.1.1)
 
-??? Command
-
 ```shell
 VLLM_USE_V1=1 CUDA_VISIBLE_DEVICES=2 vllm serve {your model directory} \
     --host 0.0.0.0 \
@@ -172,8 +164,6 @@ python3 disagg_prefill_proxy_xpyd.py &
 
 ### Decode3 (e.g. 10.0.1.5 or 10.0.1.1)
 
-??? Command
-
 ```shell
 VLLM_USE_V1=1 CUDA_VISIBLE_DEVICES=3 vllm serve {your model directory} \
     --host 0.0.0.0 \
@@ -203,8 +193,6 @@ python3 disagg_prefill_proxy_xpyd.py &
 
 ### Prefill1 (e.g. 10.0.1.2 or 10.0.1.1)
 
-??? Command
-
 ```shell
 VLLM_USE_V1=1 CUDA_VISIBLE_DEVICES=0 vllm serve {your model directory} \
     --host 0.0.0.0 \
@@ -225,8 +213,6 @@ python3 disagg_prefill_proxy_xpyd.py &
 
 ### Prefill2 (e.g. 10.0.1.3 or 10.0.1.1)
 
-??? Command
-
 ```shell
 VLLM_USE_V1=1 CUDA_VISIBLE_DEVICES=1 vllm serve {your model directory} \
     --host 0.0.0.0 \
@@ -247,8 +233,6 @@ python3 disagg_prefill_proxy_xpyd.py &
 
 ### Prefill3 (e.g. 10.0.1.4 or 10.0.1.1)
 
-??? Command
-
 ```shell
 VLLM_USE_V1=1 CUDA_VISIBLE_DEVICES=2 vllm serve {your model directory} \
     --host 0.0.0.0 \
@@ -269,8 +253,6 @@ python3 disagg_prefill_proxy_xpyd.py &
 
 ### Decode1 (e.g. 10.0.1.5 or 10.0.1.1)
 
-??? Command
-
 ```shell
 VLLM_USE_V1=1 CUDA_VISIBLE_DEVICES=3 vllm serve {your model directory} \
     --host 0.0.0.0 \
@@ -304,8 +286,6 @@ curl -X POST -s http://10.0.1.1:10001/v1/completions \
 
 # Benchmark
 
-??? Command
-
 ```shell
 python3 benchmark_serving.py \
     --backend vllm \

docs/design/v1/torch_compile.md

@@ -28,9 +28,7 @@ A unique aspect of vLLM's `torch.compile` integration, is that we guarantee all
 
 In the very verbose logs, we can see:
 
-??? Logs
-
-      ```text
+```
 DEBUG 03-07 03:06:52 [decorators.py:203] Start compiling function <code object forward at 0x7f08acf40c90, file "xxx/vllm/model_executor/models/llama.py", line 339>
 
 DEBUG 03-07 03:06:54 [backends.py:370] Traced files (to be considered for compilation cache):
@@ -101,17 +99,14 @@ This time, Inductor compilation is completely bypassed, and we will load from di
 
 The above example just uses Inductor to compile for a general shape (i.e. symbolic shape). We can also use Inductor to compile for some of the specific shapes, for example:
 
-```bash
-vllm serve meta-llama/Llama-3.2-1B \
-  --compilation_config '{"compile_sizes": [1, 2, 4, 8]}'
+```
+vllm serve meta-llama/Llama-3.2-1B --compilation_config '{"compile_sizes": [1, 2, 4, 8]}'
 ```
 
 Then it will also compile a specific kernel just for batch size `1, 2, 4, 8`. At this time, all of the shapes in the computation graph are static and known, and we will turn on auto-tuning to tune for max performance. This can be slow when you run it for the first time, but the next time you run it, we can directly bypass the tuning and run the tuned kernel.
 
 When all the shapes are known, `torch.compile` can compare different configs, and often find some better configs to run the kernel. For example, we can see the following log:
 
-??? Logs
-
 ```
 AUTOTUNE mm(8x2048, 2048x3072)
   triton_mm_4 0.0130 ms 100.0% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=128, BLOCK_M=16, BLOCK_N=32, B_PROLOGUE_CAST_TYPE=None, EVEN_K=True, GROUP_M=8, num_stages=5, num_warps=2
@@ -141,9 +136,8 @@ The cudagraphs are captured and managed by the compiler backend, and replayed wh
 
 By default, vLLM will try to determine a set of sizes to capture cudagraph. You can also override it using the config `cudagraph_capture_sizes`:
 
-```bash
-vllm serve meta-llama/Llama-3.2-1B \
-  --compilation-config '{"cudagraph_capture_sizes": [1, 2, 4, 8]}'
+```
+vllm serve meta-llama/Llama-3.2-1B --compilation-config '{"cudagraph_capture_sizes": [1, 2, 4, 8]}'
 ```
 
 Then it will only capture cudagraph for the specified sizes. It can be useful to have fine-grained control over the cudagraph capture.

docs/features/lora.md

@@ -29,8 +29,6 @@ We can now submit the prompts and call `llm.generate` with the `lora_request` pa
 of `LoRARequest` is a human identifiable name, the second parameter is a globally unique ID for the adapter and
 the third parameter is the path to the LoRA adapter.
 
-??? Code
-
 ```python
 sampling_params = SamplingParams(
     temperature=0,
@@ -70,8 +68,6 @@ The server entrypoint accepts all other LoRA configuration parameters (`max_lora
 etc.), which will apply to all forthcoming requests. Upon querying the `/models` endpoint, we should see our LoRA along
 with its base model (if `jq` is not installed, you can follow [this guide](https://jqlang.org/download/) to install it.):
 
-??? Command
-
 ```bash
 curl localhost:8000/v1/models | jq .
 {
@@ -172,7 +168,7 @@ Alternatively, follow these example steps to implement your own plugin:
 
 1. Implement the LoRAResolver interface.
 
-    ??? Example of a simple S3 LoRAResolver implementation
+    Example of a simple S3 LoRAResolver implementation:
 
     ```python
     import os
@@ -238,8 +234,6 @@ The new format of `--lora-modules` is mainly to support the display of parent mo
 - The `parent` field of LoRA model `sql-lora` now links to its base model `meta-llama/Llama-2-7b-hf`. This correctly reflects the hierarchical relationship between the base model and the LoRA adapter.
 - The `root` field points to the artifact location of the lora adapter.
 
-??? Command output
-
 ```bash
 $ curl http://localhost:8000/v1/models
 

docs/features/multimodal_inputs.md

@@ -20,8 +20,6 @@ To input multi-modal data, follow this schema in [vllm.inputs.PromptType][]:
 
 You can pass a single image to the `'image'` field of the multi-modal dictionary, as shown in the following examples:
 
-??? Code
-
 ```python
 from vllm import LLM
 
@@ -68,8 +66,6 @@ Full example: <gh-file:examples/offline_inference/vision_language.py>
 
 To substitute multiple images inside the same text prompt, you can pass in a list of images instead:
 
-??? Code
-
 ```python
 from vllm import LLM
 
@@ -103,8 +99,6 @@ Full example: <gh-file:examples/offline_inference/vision_language_multi_image.py
 
 Multi-image input can be extended to perform video captioning. We show this with [Qwen2-VL](https://huggingface.co/Qwen/Qwen2-VL-2B-Instruct) as it supports videos:
 
-??? Code
-
 ```python
 from vllm import LLM
 
@@ -150,8 +144,6 @@ Full example: <gh-file:examples/offline_inference/audio_language.py>
 To input pre-computed embeddings belonging to a data type (i.e. image, video, or audio) directly to the language model,
 pass a tensor of shape `(num_items, feature_size, hidden_size of LM)` to the corresponding field of the multi-modal dictionary.
 
-??? Code
-
 ```python
 from vllm import LLM
 
@@ -177,8 +169,6 @@ pass a tensor of shape `(num_items, feature_size, hidden_size of LM)` to the cor
 
 For Qwen2-VL and MiniCPM-V, we accept additional parameters alongside the embeddings:
 
-??? Code
-
 ```python
 # Construct the prompt based on your model
 prompt = ...
@@ -245,8 +235,6 @@ vllm serve microsoft/Phi-3.5-vision-instruct --task generate \
 
 Then, you can use the OpenAI client as follows:
 
-??? Code
-
 ```python
 from openai import OpenAI
 
@@ -307,7 +295,7 @@ Full example: <gh-file:examples/online_serving/openai_chat_completion_client_for
     By default, the timeout for fetching images through HTTP URL is `5` seconds.
     You can override this by setting the environment variable:
 
-    ```bash
+    ```console
     export VLLM_IMAGE_FETCH_TIMEOUT=<timeout>
     ```
 
@@ -323,8 +311,6 @@ vllm serve llava-hf/llava-onevision-qwen2-0.5b-ov-hf --task generate --max-model
 
 Then, you can use the OpenAI client as follows:
 
-??? Code
-
 ```python
 from openai import OpenAI
 
@@ -370,7 +356,7 @@ Full example: <gh-file:examples/online_serving/openai_chat_completion_client_for
     By default, the timeout for fetching videos through HTTP URL is `30` seconds.
     You can override this by setting the environment variable:
 
-    ```bash
+    ```console
     export VLLM_VIDEO_FETCH_TIMEOUT=<timeout>
     ```
 
@@ -387,8 +373,6 @@ vllm serve fixie-ai/ultravox-v0_5-llama-3_2-1b
 
 Then, you can use the OpenAI client as follows:
 
-??? Code
-
 ```python
 import base64
 import requests
@@ -443,8 +427,6 @@ Then, you can use the OpenAI client as follows:
 
 Alternatively, you can pass `audio_url`, which is the audio counterpart of `image_url` for image input:
 
-??? Code
-
 ```python
 chat_completion_from_url = client.chat.completions.create(
     messages=[{
@@ -476,7 +458,7 @@ Full example: <gh-file:examples/online_serving/openai_chat_completion_client_for
     By default, the timeout for fetching audios through HTTP URL is `10` seconds.
     You can override this by setting the environment variable:
 
-    ```bash
+    ```console
     export VLLM_AUDIO_FETCH_TIMEOUT=<timeout>
     ```
 
@@ -488,8 +470,6 @@ pass a tensor of shape to the corresponding field of the multi-modal dictionary.
 For image embeddings, you can pass the base64-encoded tensor to the `image_embeds` field.
 The following example demonstrates how to pass image embeddings to the OpenAI server:
 
-??? Code
-
 ```python
 image_embedding = torch.load(...)
 grid_thw = torch.load(...) # Required by Qwen/Qwen2-VL-2B-Instruct

docs/features/quantization/auto_awq.md

@@ -9,14 +9,12 @@ The main benefits are lower latency and memory usage.
 
 You can quantize your own models by installing AutoAWQ or picking one of the [6500+ models on Huggingface](https://huggingface.co/models?search=awq).
 
-```bash
+```console
 pip install autoawq
 ```
 
 After installing AutoAWQ, you are ready to quantize a model. Please refer to the [AutoAWQ documentation](https://casper-hansen.github.io/AutoAWQ/examples/#basic-quantization) for further details. Here is an example of how to quantize `mistralai/Mistral-7B-Instruct-v0.2`:
 
-??? Code
-
 ```python
 from awq import AutoAWQForCausalLM
 from transformers import AutoTokenizer
@@ -43,7 +41,7 @@ After installing AutoAWQ, you are ready to quantize a model. Please refer to the
 
 To run an AWQ model with vLLM, you can use [TheBloke/Llama-2-7b-Chat-AWQ](https://huggingface.co/TheBloke/Llama-2-7b-Chat-AWQ) with the following command:
 
-```bash
+```console
 python examples/offline_inference/llm_engine_example.py \
     --model TheBloke/Llama-2-7b-Chat-AWQ \
     --quantization awq
@@ -51,8 +49,6 @@ python examples/offline_inference/llm_engine_example.py \
 
 AWQ models are also supported directly through the LLM entrypoint:
 
-??? Code
-
 ```python
 from vllm import LLM, SamplingParams
 

docs/features/quantization/bitblas.md

@@ -12,7 +12,7 @@ vLLM now supports [BitBLAS](https://github.com/microsoft/BitBLAS) for more effic
 
 Below are the steps to utilize BitBLAS with vLLM.
 
-```bash
+```console
 pip install bitblas>=0.1.0
 ```
 
@@ -43,8 +43,6 @@ llm = LLM(
 
 ## Read gptq format checkpoint
 
-??? Code
-
 ```python
 from vllm import LLM
 import torch

docs/features/quantization/bnb.md

@@ -9,7 +9,7 @@ Compared to other quantization methods, BitsAndBytes eliminates the need for cal
 
 Below are the steps to utilize BitsAndBytes with vLLM.
 
-```bash
+```console
 pip install bitsandbytes>=0.45.3
 ```
 
@@ -54,6 +54,6 @@ llm = LLM(
 
 Append the following to your model arguments for 4bit inflight quantization:
 
-```bash
+```console
 --quantization bitsandbytes
 ```

docs/features/quantization/fp8.md

@@ -23,7 +23,7 @@ The FP8 types typically supported in hardware have two distinct representations,
 
 To produce performant FP8 quantized models with vLLM, you'll need to install the [llm-compressor](https://github.com/vllm-project/llm-compressor/) library:
 
-```bash
+```console
 pip install llmcompressor
 ```
 
@@ -58,8 +58,6 @@ For FP8 quantization, we can recover accuracy with simple RTN quantization. We r
 
 Since simple RTN does not require data for weight quantization and the activations are quantized dynamically, we do not need any calibration data for this quantization flow.
 
-??? Code
-
 ```python
 from llmcompressor.transformers import oneshot
 from llmcompressor.modifiers.quantization import QuantizationModifier
@@ -81,7 +79,7 @@ Since simple RTN does not require data for weight quantization and the activatio
 
 Install `vllm` and `lm-evaluation-harness` for evaluation:
 
-```bash
+```console
 pip install vllm lm-eval==0.4.4
 ```
 
@@ -99,9 +97,9 @@ Evaluate accuracy with `lm_eval` (for example on 250 samples of `gsm8k`):
 !!! note
     Quantized models can be sensitive to the presence of the `bos` token. `lm_eval` does not add a `bos` token by default, so make sure to include the `add_bos_token=True` argument when running your evaluations.
 
-```bash
-MODEL=$PWD/Meta-Llama-3-8B-Instruct-FP8-Dynamic
-lm_eval \
+```console
+$ MODEL=$PWD/Meta-Llama-3-8B-Instruct-FP8-Dynamic
+$ lm_eval \
   --model vllm \
   --model_args pretrained=$MODEL,add_bos_token=True \
   --tasks gsm8k  --num_fewshot 5 --batch_size auto --limit 250

docs/features/quantization/gguf.md

@@ -11,7 +11,7 @@ title: GGUF
 
 To run a GGUF model with vLLM, you can download and use the local GGUF model from [TheBloke/TinyLlama-1.1B-Chat-v1.0-GGUF](https://huggingface.co/TheBloke/TinyLlama-1.1B-Chat-v1.0-GGUF) with the following command:
 
-```bash
+```console
 wget https://huggingface.co/TheBloke/TinyLlama-1.1B-Chat-v1.0-GGUF/resolve/main/tinyllama-1.1b-chat-v1.0.Q4_K_M.gguf
 # We recommend using the tokenizer from base model to avoid long-time and buggy tokenizer conversion.
 vllm serve ./tinyllama-1.1b-chat-v1.0.Q4_K_M.gguf \
@@ -20,7 +20,7 @@ vllm serve ./tinyllama-1.1b-chat-v1.0.Q4_K_M.gguf \
 
 You can also add `--tensor-parallel-size 2` to enable tensor parallelism inference with 2 GPUs:
 
-```bash
+```console
 # We recommend using the tokenizer from base model to avoid long-time and buggy tokenizer conversion.
 vllm serve ./tinyllama-1.1b-chat-v1.0.Q4_K_M.gguf \
    --tokenizer TinyLlama/TinyLlama-1.1B-Chat-v1.0 \
@@ -32,7 +32,7 @@ vllm serve ./tinyllama-1.1b-chat-v1.0.Q4_K_M.gguf \
 
 GGUF assumes that huggingface can convert the metadata to a config file. In case huggingface doesn't support your model you can manually create a config and pass it as hf-config-path
 
-```bash
+```console
 # If you model is not supported by huggingface you can manually provide a huggingface compatible config path
 vllm serve ./tinyllama-1.1b-chat-v1.0.Q4_K_M.gguf \
    --tokenizer TinyLlama/TinyLlama-1.1B-Chat-v1.0 \
@@ -41,8 +41,6 @@ vllm serve ./tinyllama-1.1b-chat-v1.0.Q4_K_M.gguf \
 
 You can also use the GGUF model directly through the LLM entrypoint:
 
-??? Code
-
 ```python
 from vllm import LLM, SamplingParams
 

docs/features/quantization/gptqmodel.md

@@ -21,7 +21,7 @@ for more details on this and other advanced features.
 
 You can quantize your own models by installing [GPTQModel](https://github.com/ModelCloud/GPTQModel) or picking one of the [5000+ models on Huggingface](https://huggingface.co/models?search=gptq).
 
-```bash
+```console
 pip install -U gptqmodel --no-build-isolation -v
 ```
 
@@ -31,8 +31,6 @@ After installing GPTQModel, you are ready to quantize a model. Please refer to t
 
 Here is an example of how to quantize `meta-llama/Llama-3.2-1B-Instruct`:
 
-??? Code
-
 ```python
 from datasets import load_dataset
 from gptqmodel import GPTQModel, QuantizeConfig
@@ -60,7 +58,7 @@ Here is an example of how to quantize `meta-llama/Llama-3.2-1B-Instruct`:
 
 To run an GPTQModel quantized model with vLLM, you can use [DeepSeek-R1-Distill-Qwen-7B-gptqmodel-4bit-vortex-v2](https://huggingface.co/ModelCloud/DeepSeek-R1-Distill-Qwen-7B-gptqmodel-4bit-vortex-v2) with the following command:
 
-```bash
+```console
 python examples/offline_inference/llm_engine_example.py \
     --model ModelCloud/DeepSeek-R1-Distill-Qwen-7B-gptqmodel-4bit-vortex-v2
 ```
@@ -69,8 +67,6 @@ python examples/offline_inference/llm_engine_example.py \
 
 GPTQModel quantized models are also supported directly through the LLM entrypoint:
 
-??? Code
-
 ```python
 from vllm import LLM, SamplingParams
 

docs/features/quantization/int4.md

@@ -14,13 +14,13 @@ Please visit the HF collection of [quantized INT4 checkpoints of popular LLMs re
 
 To use INT4 quantization with vLLM, you'll need to install the [llm-compressor](https://github.com/vllm-project/llm-compressor/) library:
 
-```bash
+```console
 pip install llmcompressor
 ```
 
 Additionally, install `vllm` and `lm-evaluation-harness` for evaluation:
 
-```bash
+```console
 pip install vllm lm-eval==0.4.4
 ```
 
@@ -53,8 +53,6 @@ When quantizing weights to INT4, you need sample data to estimate the weight upd
 It's best to use calibration data that closely matches your deployment data.
 For a general-purpose instruction-tuned model, you can use a dataset like `ultrachat`:
 
-??? Code
-
 ```python
 from datasets import load_dataset
 
@@ -78,8 +76,6 @@ For a general-purpose instruction-tuned model, you can use a dataset like `ultra
 
 Now, apply the quantization algorithms:
 
-??? Code
-
 ```python
 from llmcompressor.transformers import oneshot
 from llmcompressor.modifiers.quantization import GPTQModifier
@@ -116,8 +112,8 @@ model = LLM("./Meta-Llama-3-8B-Instruct-W4A16-G128")
 
 To evaluate accuracy, you can use `lm_eval`:
 
-```bash
-lm_eval --model vllm \
+```console
+$ lm_eval --model vllm \
   --model_args pretrained="./Meta-Llama-3-8B-Instruct-W4A16-G128",add_bos_token=true \
   --tasks gsm8k \
   --num_fewshot 5 \
@@ -141,8 +137,6 @@ lm_eval --model vllm \
 
 The following is an example of an expanded quantization recipe you can tune to your own use case:
 
-??? Code
-
 ```python
 from compressed_tensors.quantization import (
     QuantizationArgs,

docs/features/quantization/int8.md

@@ -15,13 +15,13 @@ Please visit the HF collection of [quantized INT8 checkpoints of popular LLMs re
 
 To use INT8 quantization with vLLM, you'll need to install the [llm-compressor](https://github.com/vllm-project/llm-compressor/) library:
 
-```bash
+```console
 pip install llmcompressor
 ```
 
 Additionally, install `vllm` and `lm-evaluation-harness` for evaluation:
 
-```bash
+```console
 pip install vllm lm-eval==0.4.4
 ```
 
@@ -54,8 +54,6 @@ When quantizing activations to INT8, you need sample data to estimate the activa
 It's best to use calibration data that closely matches your deployment data.
 For a general-purpose instruction-tuned model, you can use a dataset like `ultrachat`:
 
-??? Code
-
 ```python
 from datasets import load_dataset
 
@@ -75,14 +73,10 @@ For a general-purpose instruction-tuned model, you can use a dataset like `ultra
 ds = ds.map(tokenize, remove_columns=ds.column_names)
 ```
 
-</details>
-
 ### 3. Applying Quantization
 
 Now, apply the quantization algorithms:
 
-??? Code
-
 ```python
 from llmcompressor.transformers import oneshot
 from llmcompressor.modifiers.quantization import GPTQModifier
@@ -122,8 +116,8 @@ model = LLM("./Meta-Llama-3-8B-Instruct-W8A8-Dynamic-Per-Token")
 
 To evaluate accuracy, you can use `lm_eval`:
 
-```bash
-lm_eval --model vllm \
+```console
+$ lm_eval --model vllm \
   --model_args pretrained="./Meta-Llama-3-8B-Instruct-W8A8-Dynamic-Per-Token",add_bos_token=true \
   --tasks gsm8k \
   --num_fewshot 5 \

docs/features/quantization/modelopt.md

@@ -4,7 +4,7 @@ The [NVIDIA TensorRT Model Optimizer](https://github.com/NVIDIA/TensorRT-Model-O
 
 We recommend installing the library with:
 
-```bash
+```console
 pip install nvidia-modelopt
 ```
 
@@ -14,8 +14,6 @@ You can quantize HuggingFace models using the example scripts provided in the Te
 
 Below is an example showing how to quantize a model using modelopt's PTQ API:
 
-??? Code
-
 ```python
 import modelopt.torch.quantization as mtq
 from transformers import AutoModelForCausalLM
@@ -50,8 +48,6 @@ with torch.inference_mode():
 
 The quantized checkpoint can then be deployed with vLLM. As an example, the following code shows how to deploy `nvidia/Llama-3.1-8B-Instruct-FP8`, which is the FP8 quantized checkpoint derived from `meta-llama/Llama-3.1-8B-Instruct`, using vLLM:
 
-??? Code
-
 ```python
 from vllm import LLM, SamplingParams
 

docs/features/quantization/quantized_kvcache.md

@@ -35,8 +35,6 @@ Studies have shown that FP8 E4M3 quantization typically only minimally degrades
 
 Here is an example of how to enable FP8 quantization:
 
-??? Code
-
 ```python
 # To calculate kv cache scales on the fly enable the calculate_kv_scales
 # parameter
@@ -65,7 +63,7 @@ For optimal model quality when using FP8 KV Cache, we recommend using calibrated
 
 First, install the required dependencies:
 
-```bash
+```console
 pip install llmcompressor
 ```
 
@@ -73,8 +71,6 @@ pip install llmcompressor
 
 Here's a complete example using `meta-llama/Llama-3.1-8B-Instruct` (most models can use this same pattern):
 
-??? Code
-
 ```python
 from datasets import load_dataset
 from transformers import AutoModelForCausalLM, AutoTokenizer

docs/features/quantization/quark.md

@@ -13,7 +13,7 @@ AWQ, GPTQ, Rotation and SmoothQuant.
 
 Before quantizing models, you need to install Quark. The latest release of Quark can be installed with pip:
 
-```bash
+```console
 pip install amd-quark
 ```
 
@@ -22,7 +22,7 @@ for more installation details.
 
 Additionally, install `vllm` and `lm-evaluation-harness` for evaluation:
 
-```bash
+```console
 pip install vllm lm-eval==0.4.4
 ```
 
@@ -42,8 +42,6 @@ The Quark quantization process can be listed for 5 steps as below:
 Quark uses [Transformers](https://huggingface.co/docs/transformers/en/index)
 to fetch model and tokenizer.
 
-??? Code
-
 ```python
 from transformers import AutoTokenizer, AutoModelForCausalLM
 
@@ -65,8 +63,6 @@ Quark uses the [PyTorch Dataloader](https://pytorch.org/tutorials/beginner/basic
 to load calibration data. For more details about how to use calibration datasets efficiently, please refer
 to [Adding Calibration Datasets](https://quark.docs.amd.com/latest/pytorch/calibration_datasets.html).
 
-??? Code
-
 ```python
 from datasets import load_dataset
 from torch.utils.data import DataLoader
@@ -98,8 +94,6 @@ kv-cache and the quantization algorithm is AutoSmoothQuant.
     AutoSmoothQuant config file for Llama is
     `examples/torch/language_modeling/llm_ptq/models/llama/autosmoothquant_config.json`.
 
-??? Code
-
 ```python
 from quark.torch.quantization import (Config, QuantizationConfig,
                                      FP8E4M3PerTensorSpec,
@@ -145,8 +139,6 @@ HuggingFace `safetensors`, you can refer to
 [HuggingFace format exporting](https://quark.docs.amd.com/latest/pytorch/export/quark_export_hf.html)
 for more exporting format details.
 
-??? Code
-
 ```python
 import torch
 from quark.torch import ModelQuantizer, ModelExporter
@@ -176,8 +168,6 @@ for more exporting format details.
 
 Now, you can load and run the Quark quantized model directly through the LLM entrypoint:
 
-??? Code
-
 ```python
 from vllm import LLM, SamplingParams
 
@@ -209,8 +199,8 @@ Now, you can load and run the Quark quantized model directly through the LLM ent
 
 Or, you can use `lm_eval` to evaluate accuracy:
 
-```bash
-lm_eval --model vllm \
+```console
+$ lm_eval --model vllm \
   --model_args pretrained=Llama-2-70b-chat-hf-w-fp8-a-fp8-kvcache-fp8-pertensor-autosmoothquant,kv_cache_dtype='fp8',quantization='quark' \
   --tasks gsm8k
 ```
@@ -222,7 +212,7 @@ to quantize large language models more conveniently. It supports quantizing mode
 of different quantization schemes and optimization algorithms. It can export the quantized model
 and run evaluation tasks on the fly. With the script, the example above can be:
 
-```bash
+```console
 python3 quantize_quark.py --model_dir meta-llama/Llama-2-70b-chat-hf \
                           --output_dir /path/to/output \
                           --quant_scheme w_fp8_a_fp8 \

docs/features/quantization/torchao.md

@@ -4,7 +4,7 @@ TorchAO is an architecture optimization library for PyTorch, it provides high pe
 
 We recommend installing the latest torchao nightly with
 
-```bash
+```console
 # Install the latest TorchAO nightly build
 # Choose the CUDA version that matches your system (cu126, cu128, etc.)
 pip install \
@@ -15,8 +15,6 @@ pip install \
 ## Quantizing HuggingFace Models
 You can quantize your own huggingface model with torchao, e.g. [transformers](https://huggingface.co/docs/transformers/main/en/quantization/torchao) and [diffusers](https://huggingface.co/docs/diffusers/en/quantization/torchao), and save the checkpoint to huggingface hub like [this](https://huggingface.co/jerryzh168/llama3-8b-int8wo) with the following example code:
 
-??? Code
-
 ```Python
 import torch
 from transformers import TorchAoConfig, AutoModelForCausalLM, AutoTokenizer

docs/features/reasoning_outputs.md

@@ -33,8 +33,6 @@ vllm serve deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B \
 
 Next, make a request to the model that should return the reasoning content in the response.
 
-??? Code
-
 ```python
 from openai import OpenAI
 
@@ -70,8 +68,6 @@ The `reasoning_content` field contains the reasoning steps that led to the final
 
 Streaming chat completions are also supported for reasoning models. The `reasoning_content` field is available in the `delta` field in [chat completion response chunks](https://platform.openai.com/docs/api-reference/chat/streaming).
 
-??? Json
-
 ```json
 {
     "id": "chatcmpl-123",
@@ -95,8 +91,6 @@ Streaming chat completions are also supported for reasoning models. The `reasoni
 
 OpenAI Python client library does not officially support `reasoning_content` attribute for streaming output. But the client supports extra attributes in the response. You can use `hasattr` to check if the `reasoning_content` attribute is present in the response. For example:
 
-??? Code
-
 ```python
 from openai import OpenAI
 
@@ -152,8 +146,6 @@ Remember to check whether the `reasoning_content` exists in the response before
 
 The reasoning content is also available when both tool calling and the reasoning parser are enabled. Additionally, tool calling only parses functions from the `content` field, not from the `reasoning_content`.
 
-??? Code
-
 ```python
 from openai import OpenAI
 
@@ -200,8 +192,6 @@ For more examples, please refer to <gh-file:examples/online_serving/openai_chat_
 
 You can add a new `ReasoningParser` similar to <gh-file:vllm/reasoning/deepseek_r1_reasoning_parser.py>.
 
-??? Code
-
 ```python
 # import the required packages
 
@@ -258,8 +248,6 @@ You can add a new `ReasoningParser` similar to <gh-file:vllm/reasoning/deepseek_
 
 Additionally, to enable structured output, you'll need to create a new `Reasoner` similar to the one in <gh-file:vllm/reasoning/deepseek_r1_reasoning_parser.py>.
 
-??? Code
-
 ```python
 @dataclass
 class DeepSeekReasoner(Reasoner):

docs/features/spec_decode.md

@@ -18,8 +18,6 @@ Speculative decoding is a technique which improves inter-token latency in memory
 
 The following code configures vLLM in an offline mode to use speculative decoding with a draft model, speculating 5 tokens at a time.
 
-??? Code
-
 ```python
 from vllm import LLM, SamplingParams
 
@@ -62,8 +60,6 @@ python -m vllm.entrypoints.openai.api_server \
 
 Then use a client:
 
-??? Code
-
 ```python
 from openai import OpenAI
 
@@ -103,8 +99,6 @@ Then use a client:
 The following code configures vLLM to use speculative decoding where proposals are generated by
 matching n-grams in the prompt. For more information read [this thread.](https://x.com/joao_gante/status/1747322413006643259)
 
-??? Code
-
 ```python
 from vllm import LLM, SamplingParams
 
@@ -137,8 +131,6 @@ draft models that conditioning draft predictions on both context vectors and sam
 For more information see [this blog](https://pytorch.org/blog/hitchhikers-guide-speculative-decoding/) or
 [this technical report](https://arxiv.org/abs/2404.19124).
 
-??? Code
-
 ```python
 from vllm import LLM, SamplingParams
 
@@ -185,8 +177,6 @@ A variety of speculative models of this type are available on HF hub:
 The following code configures vLLM to use speculative decoding where proposals are generated by
 an [EAGLE (Extrapolation Algorithm for Greater Language-model Efficiency)](https://arxiv.org/pdf/2401.15077) based draft model. A more detailed example for offline mode, including how to extract request level acceptance rate, can be found [here](gh-file:examples/offline_inference/eagle.py).
 
-??? Code
-
 ```python
 from vllm import LLM, SamplingParams
 

docs/features/structured_outputs.md

@@ -33,8 +33,6 @@ text.
 
 Now let´s see an example for each of the cases, starting with the `guided_choice`, as it´s the easiest one:
 
-??? Code
-
 ```python
 from openai import OpenAI
 client = OpenAI(
@@ -55,8 +53,6 @@ Now let´s see an example for each of the cases, starting with the `guided_choic
 
 The next example shows how to use the `guided_regex`. The idea is to generate an email address, given a simple regex template:
 
-??? Code
-
 ```python
 completion = client.chat.completions.create(
     model=model,
@@ -79,8 +75,6 @@ For this we can use the `guided_json` parameter in two different ways:
 
 The next example shows how to use the `guided_json` parameter with a Pydantic model:
 
-??? Code
-
 ```python
 from pydantic import BaseModel
 from enum import Enum
@@ -127,8 +121,6 @@ difficult to use, but it´s really powerful. It allows us to define complete
 languages like SQL queries. It works by using a context free EBNF grammar.
 As an example, we can use to define a specific format of simplified SQL queries:
 
-??? Code
-
 ```python
 simplified_sql_grammar = """
     root ::= select_statement
@@ -169,8 +161,6 @@ vllm serve deepseek-ai/DeepSeek-R1-Distill-Qwen-7B --reasoning-parser deepseek_r
 
 Note that you can use reasoning with any provided structured outputs feature. The following uses one with JSON schema:
 
-??? Code
-
 ```python
 from pydantic import BaseModel
 
@@ -212,8 +202,6 @@ For the following examples, vLLM was setup using `vllm serve meta-llama/Llama-3.
 
 Here is a simple example demonstrating how to get structured output using Pydantic models:
 
-??? Code
-
 ```python
 from pydantic import BaseModel
 from openai import OpenAI
@@ -240,6 +228,8 @@ Here is a simple example demonstrating how to get structured output using Pydant
 print("Age:", message.parsed.age)
 ```
 
+Output:
+
 ```console
 ParsedChatCompletionMessage[Testing](content='{"name": "Cameron", "age": 28}', refusal=None, role='assistant', audio=None, function_call=None, tool_calls=[], parsed=Testing(name='Cameron', age=28))
 Name: Cameron
@@ -248,8 +238,6 @@ Age: 28
 
 Here is a more complex example using nested Pydantic models to handle a step-by-step math solution:
 
-??? Code
-
 ```python
 from typing import List
 from pydantic import BaseModel
@@ -308,8 +296,6 @@ These parameters can be used in the same way as the parameters from the Online
 Serving examples above. One example for the usage of the `choice` parameter is
 shown below:
 
-??? Code
-
 ```python
 from vllm import LLM, SamplingParams
 from vllm.sampling_params import GuidedDecodingParams

docs/features/tool_calling.md

@@ -15,8 +15,6 @@ vllm serve meta-llama/Llama-3.1-8B-Instruct \
 
 Next, make a request to the model that should result in it using the available tools:
 
-??? Code
-
 ```python
 from openai import OpenAI
 import json
@@ -228,25 +226,6 @@ AI21's Jamba-1.5 models are supported.
 
 Flags: `--tool-call-parser jamba`
 
-### xLAM Models (`xlam`)
-
-The xLAM tool parser is designed to support models that generate tool calls in various JSON formats. It detects function calls in several different output styles:
-
-1. Direct JSON arrays: Output strings that are JSON arrays starting with `[` and ending with `]`
-2. Thinking tags: Using `<think>...</think>` tags containing JSON arrays
-3. Code blocks: JSON in code blocks (```json ...```)
-4. Tool calls tags: Using `[TOOL_CALLS]` or `<tool_call>...</tool_call>` tags
-
-Parallel function calls are supported, and the parser can effectively separate text content from tool calls.
-
-Supported models:
-* Salesforce Llama-xLAM models: `Salesforce/Llama-xLAM-2-8B-fc-r`, `Salesforce/Llama-xLAM-2-70B-fc-r`
-* Qwen-xLAM models: `Salesforce/xLAM-1B-fc-r`, `Salesforce/xLAM-3B-fc-r`, `Salesforce/Qwen-xLAM-32B-fc-r`
-
-Flags:
-* For Llama-based xLAM models: `--tool-call-parser xlam --chat-template examples/tool_chat_template_xlam_llama.jinja`
-* For Qwen-based xLAM models: `--tool-call-parser xlam --chat-template examples/tool_chat_template_xlam_qwen.jinja`
-
 ### Qwen Models
 
 For Qwen2.5, the chat template in tokenizer_config.json has already included support for the Hermes-style tool use. Therefore, you can use the `hermes` parser to enable tool calls for Qwen models. For more detailed information, please refer to the official [Qwen documentation](https://qwen.readthedocs.io/en/latest/framework/function_call.html#vllm)
@@ -303,8 +282,6 @@ A tool parser plugin is a Python file containing one or more ToolParser implemen
 
 Here is a summary of a plugin file:
 
-??? Code
-
 ```python
 
 # import the required packages
@@ -351,7 +328,7 @@ Here is a summary of a plugin file:
 
 Then you can use this plugin in the command line like this.
 
-```bash
+```console
     --enable-auto-tool-choice \
     --tool-parser-plugin <absolute path of the plugin file>
     --tool-call-parser example \

docs/getting_started/installation/aws_neuron.md

@@ -26,7 +26,7 @@ The easiest way to launch a Trainium or Inferentia instance with pre-installed N
 - After launching the instance, follow the instructions in [Connect to your instance](https://docs.aws.amazon.com/AWSEC2/latest/UserGuide/AccessingInstancesLinux.html) to connect to the instance
 - Once inside your instance, activate the pre-installed virtual environment for inference by running
 
-```bash
+```console
 source /opt/aws_neuronx_venv_pytorch_2_6_nxd_inference/bin/activate
 ```
 
@@ -47,7 +47,7 @@ Currently, there are no pre-built Neuron wheels.
 
 To build and install vLLM from source, run:
 
-```bash
+```console
 git clone https://github.com/vllm-project/vllm.git
 cd vllm
 pip install -U -r requirements/neuron.txt
@@ -66,7 +66,7 @@ Refer to [vLLM User Guide for NxD Inference](https://awsdocs-neuron.readthedocs-
 
 To install the AWS Neuron fork, run the following:
 
-```bash
+```console
 git clone -b neuron-2.23-vllm-v0.7.2 https://github.com/aws-neuron/upstreaming-to-vllm.git
 cd upstreaming-to-vllm
 pip install -r requirements/neuron.txt
@@ -100,7 +100,7 @@ to perform most of the heavy lifting which includes PyTorch model initialization
 To configure NxD Inference features through the vLLM entrypoint, use the `override_neuron_config` setting. Provide the configs you want to override
 as a dictionary (or JSON object when starting vLLM from the CLI). For example, to disable auto bucketing, include
 
-```python
+```console
 override_neuron_config={
     "enable_bucketing":False,
 }
@@ -108,7 +108,7 @@ override_neuron_config={
 
 or when launching vLLM from the CLI, pass
 
-```bash
+```console
 --override-neuron-config "{\"enable_bucketing\":false}"
 ```
 

docs/getting_started/installation/cpu.md

@@ -76,15 +76,11 @@ Currently, there are no pre-built CPU wheels.
 
 ### Build image from source
 
-??? Commands
-
-    ```bash
-    docker build -f docker/Dockerfile.cpu \
-            --tag vllm-cpu-env \
-            --target vllm-openai .
+```console
+$ docker build -f docker/Dockerfile.cpu --tag vllm-cpu-env --target vllm-openai .
 
 # Launching OpenAI server 
-    docker run --rm \
+$ docker run --rm \
              --privileged=true \
              --shm-size=4g \
              -p 8000:8000 \
@@ -123,7 +119,7 @@ vLLM CPU backend supports the following vLLM features:
 
 - We highly recommend to use TCMalloc for high performance memory allocation and better cache locality. For example, on Ubuntu 22.4, you can run:
 
-```bash
+```console
 sudo apt-get install libtcmalloc-minimal4 # install TCMalloc library
 find / -name *libtcmalloc* # find the dynamic link library path
 export LD_PRELOAD=/usr/lib/x86_64-linux-gnu/libtcmalloc_minimal.so.4:$LD_PRELOAD # prepend the library to LD_PRELOAD
@@ -132,7 +128,7 @@ python examples/offline_inference/basic/basic.py # run vLLM
 
 - When using the online serving, it is recommended to reserve 1-2 CPU cores for the serving framework to avoid CPU oversubscription. For example, on a platform with 32 physical CPU cores, reserving CPU 30 and 31 for the framework and using CPU 0-29 for OpenMP:
 
-```bash
+```console
 export VLLM_CPU_KVCACHE_SPACE=40
 export VLLM_CPU_OMP_THREADS_BIND=0-29
 vllm serve facebook/opt-125m
@@ -140,7 +136,7 @@ vllm serve facebook/opt-125m
 
  or using default auto thread binding:
 
-```bash
+```console
 export VLLM_CPU_KVCACHE_SPACE=40
 export VLLM_CPU_NUM_OF_RESERVED_CPU=2
 vllm serve facebook/opt-125m
@@ -148,8 +144,6 @@ vllm serve facebook/opt-125m
 
 - If using vLLM CPU backend on a machine with hyper-threading, it is recommended to bind only one OpenMP thread on each physical CPU core using `VLLM_CPU_OMP_THREADS_BIND` or using auto thread binding feature by default. On a hyper-threading enabled platform with 16 logical CPU cores / 8 physical CPU cores:
 
-??? Commands
-
 ```console
 $ lscpu -e # check the mapping between logical CPU cores and physical CPU cores
 
@@ -189,20 +183,14 @@ vllm serve facebook/opt-125m
 
   - Tensor Parallel is supported for serving and offline inferencing. In general each NUMA node is treated as one GPU card. Below is the example script to enable Tensor Parallel = 2 for serving:
 
-    ```bash
-    VLLM_CPU_KVCACHE_SPACE=40 VLLM_CPU_OMP_THREADS_BIND="0-31|32-63" \
-        vllm serve meta-llama/Llama-2-7b-chat-hf \
-        -tp=2 \
-        --distributed-executor-backend mp
+    ```console
+    VLLM_CPU_KVCACHE_SPACE=40 VLLM_CPU_OMP_THREADS_BIND="0-31|32-63" vllm serve meta-llama/Llama-2-7b-chat-hf -tp=2 --distributed-executor-backend mp
     ```
 
     or using default auto thread binding:
 
-    ```bash
-    VLLM_CPU_KVCACHE_SPACE=40 \
-        vllm serve meta-llama/Llama-2-7b-chat-hf \
-        -tp=2 \
-        --distributed-executor-backend mp
+    ```console
+    VLLM_CPU_KVCACHE_SPACE=40 vllm serve meta-llama/Llama-2-7b-chat-hf -tp=2 --distributed-executor-backend mp
     ```
 
   - For each thread id list in `VLLM_CPU_OMP_THREADS_BIND`, users should guarantee threads in the list belong to a same NUMA node.

docs/getting_started/installation/cpu/apple.inc.md

@@ -25,7 +25,7 @@ Currently the CPU implementation for macOS supports FP32 and FP16 datatypes.
 
 After installation of XCode and the Command Line Tools, which include Apple Clang, execute the following commands to build and install vLLM from the source.
 
-```bash
+```console
 git clone https://github.com/vllm-project/vllm.git
 cd vllm
 pip install -r requirements/cpu.txt

docs/getting_started/installation/cpu/arm.inc.md

@@ -23,7 +23,7 @@ ARM CPU backend currently supports Float32, FP16 and BFloat16 datatypes.
 # --8<-- [end:pre-built-wheels]
 # --8<-- [start:build-wheel-from-source]
 
---8<-- "docs/getting_started/installation/cpu/build.inc.md"
+--8<-- "docs/getting_started/installation/cpu/cpu/build.inc.md"
 
 Testing has been conducted on AWS Graviton3 instances for compatibility.
 

docs/getting_started/installation/cpu/build.inc.md

@@ -1,6 +1,6 @@
 First, install recommended compiler. We recommend to use `gcc/g++ >= 12.3.0` as the default compiler to avoid potential problems. For example, on Ubuntu 22.4, you can run:
 
-```bash
+```console
 sudo apt-get update  -y
 sudo apt-get install -y gcc-12 g++-12 libnuma-dev python3-dev
 sudo update-alternatives --install /usr/bin/gcc gcc /usr/bin/gcc-12 10 --slave /usr/bin/g++ g++ /usr/bin/g++-12
@@ -8,14 +8,14 @@ sudo update-alternatives --install /usr/bin/gcc gcc /usr/bin/gcc-12 10 --slave /
 
 Second, clone vLLM project:
 
-```bash
+```console
 git clone https://github.com/vllm-project/vllm.git vllm_source
 cd vllm_source
 ```
 
 Third, install Python packages for vLLM CPU backend building:
 
-```bash
+```console
 pip install --upgrade pip
 pip install "cmake>=3.26.1" wheel packaging ninja "setuptools-scm>=8" numpy
 pip install -v -r requirements/cpu.txt --extra-index-url https://download.pytorch.org/whl/cpu
@@ -23,13 +23,13 @@ pip install -v -r requirements/cpu.txt --extra-index-url https://download.pytorc
 
 Finally, build and install vLLM CPU backend:
 
-```bash
+```console
 VLLM_TARGET_DEVICE=cpu python setup.py install
 ```
 
 If you want to develop vllm, install it in editable mode instead.
 
-```bash
+```console
 VLLM_TARGET_DEVICE=cpu python setup.py develop
 ```
 

docs/getting_started/installation/cpu/s390x.inc.md

@@ -26,7 +26,7 @@ Currently the CPU implementation for s390x architecture supports FP32 datatype o
 
 Install the following packages from the package manager before building the vLLM. For example on RHEL 9.4:
 
-```bash
+```console
 dnf install -y \
     which procps findutils tar vim git gcc g++ make patch make cython zlib-devel \
     libjpeg-turbo-devel libtiff-devel libpng-devel libwebp-devel freetype-devel harfbuzz-devel \
@@ -35,7 +35,7 @@ dnf install -y \
 
 Install rust>=1.80 which is needed for `outlines-core` and `uvloop` python packages installation.
 
-```bash
+```console
 curl https://sh.rustup.rs -sSf | sh -s -- -y && \
     . "$HOME/.cargo/env"
 ```
@@ -45,7 +45,7 @@ Execute the following commands to build and install vLLM from the source.
 !!! tip
     Please build the following dependencies, `torchvision`, `pyarrow` from the source before building vLLM.
 
-```bash
+```console
     sed -i '/^torch/d' requirements-build.txt    # remove torch from requirements-build.txt since we use nightly builds
     pip install -v \
         --extra-index-url https://download.pytorch.org/whl/nightly/cpu \

docs/getting_started/installation/cpu/x86.inc.md

@@ -24,7 +24,7 @@ vLLM initially supports basic model inferencing and serving on x86 CPU platform,
 # --8<-- [end:pre-built-wheels]
 # --8<-- [start:build-wheel-from-source]
 
---8<-- "docs/getting_started/installation/cpu/build.inc.md"
+--8<-- "docs/getting_started/installation/cpu/cpu/build.inc.md"
 
 !!! note
     - AVX512_BF16 is an extension ISA provides native BF16 data type conversion and vector product instructions, which brings some performance improvement compared with pure AVX512. The CPU backend build script will check the host CPU flags to determine whether to enable AVX512_BF16.

docs/getting_started/installation/google_tpu.md

@@ -68,7 +68,7 @@ For more information about using TPUs with GKE, see:
 
 Create a TPU v5e with 4 TPU chips:
 
-```bash
+```console
 gcloud alpha compute tpus queued-resources create QUEUED_RESOURCE_ID \
   --node-id TPU_NAME \
   --project PROJECT_ID \
@@ -156,13 +156,13 @@ See [deployment-docker-pre-built-image][deployment-docker-pre-built-image] for i
 
 You can use <gh-file:docker/Dockerfile.tpu> to build a Docker image with TPU support.
 
-```bash
+```console
 docker build -f docker/Dockerfile.tpu -t vllm-tpu .
 ```
 
 Run the Docker image with the following command:
 
-```bash
+```console
 # Make sure to add `--privileged --net host --shm-size=16G`.
 docker run --privileged --net host --shm-size=16G -it vllm-tpu
 ```
@@ -185,6 +185,6 @@ docker run --privileged --net host --shm-size=16G -it vllm-tpu
 
     Install OpenBLAS with the following command:
 
-    ```bash
+    ```console
     sudo apt-get install --no-install-recommends --yes libopenblas-base libopenmpi-dev libomp-dev
     ```

docs/getting_started/installation/gpu/cuda.inc.md

@@ -22,7 +22,7 @@ Therefore, it is recommended to install vLLM with a **fresh new** environment. I
 
 You can install vLLM using either `pip` or `uv pip`:
 
-```bash
+```console
 # Install vLLM with CUDA 12.8.
 # If you are using pip.
 pip install vllm --extra-index-url https://download.pytorch.org/whl/cu128
@@ -37,7 +37,7 @@ We recommend leveraging `uv` to [automatically select the appropriate PyTorch in
 
 As of now, vLLM's binaries are compiled with CUDA 12.8 and public PyTorch release versions by default. We also provide vLLM binaries compiled with CUDA 12.6, 11.8, and public PyTorch release versions:
 
-```bash
+```console
 # Install vLLM with CUDA 11.8.
 export VLLM_VERSION=0.6.1.post1
 export PYTHON_VERSION=312
@@ -52,7 +52,7 @@ LLM inference is a fast-evolving field, and the latest code may contain bug fixe
 
 ##### Install the latest code using `pip`
 
-```bash
+```console
 pip install -U vllm \
     --pre \
     --extra-index-url https://wheels.vllm.ai/nightly
@@ -62,7 +62,7 @@ pip install -U vllm \
 
 Another way to install the latest code is to use `uv`:
 
-```bash
+```console
 uv pip install -U vllm \
     --torch-backend=auto \
     --extra-index-url https://wheels.vllm.ai/nightly
@@ -72,7 +72,7 @@ uv pip install -U vllm \
 
 If you want to access the wheels for previous commits (e.g. to bisect the behavior change, performance regression), due to the limitation of `pip`, you have to specify the full URL of the wheel file by embedding the commit hash in the URL:
 
-```bash
+```console
 export VLLM_COMMIT=33f460b17a54acb3b6cc0b03f4a17876cff5eafd # use full commit hash from the main branch
 pip install https://wheels.vllm.ai/${VLLM_COMMIT}/vllm-1.0.0.dev-cp38-abi3-manylinux1_x86_64.whl
 ```
@@ -83,7 +83,7 @@ Note that the wheels are built with Python 3.8 ABI (see [PEP 425](https://peps.p
 
 If you want to access the wheels for previous commits (e.g. to bisect the behavior change, performance regression), you can specify the commit hash in the URL:
 
-```bash
+```console
 export VLLM_COMMIT=72d9c316d3f6ede485146fe5aabd4e61dbc59069 # use full commit hash from the main branch
 uv pip install vllm \
     --torch-backend=auto \
@@ -99,7 +99,7 @@ The `uv` approach works for vLLM `v0.6.6` and later and offers an easy-to-rememb
 
 If you only need to change Python code, you can build and install vLLM without compilation. Using `pip`'s [`--editable` flag](https://pip.pypa.io/en/stable/topics/local-project-installs/#editable-installs), changes you make to the code will be reflected when you run vLLM:
 
-```bash
+```console
 git clone https://github.com/vllm-project/vllm.git
 cd vllm
 VLLM_USE_PRECOMPILED=1 pip install --editable .
@@ -118,7 +118,7 @@ This command will do the following:
 
 In case you see an error about wheel not found when running the above command, it might be because the commit you based on in the main branch was just merged and the wheel is being built. In this case, you can wait for around an hour to try again, or manually assign the previous commit in the installation using the `VLLM_PRECOMPILED_WHEEL_LOCATION` environment variable.
 
-```bash
+```console
 export VLLM_COMMIT=72d9c316d3f6ede485146fe5aabd4e61dbc59069 # use full commit hash from the main branch
 export VLLM_PRECOMPILED_WHEEL_LOCATION=https://wheels.vllm.ai/${VLLM_COMMIT}/vllm-1.0.0.dev-cp38-abi3-manylinux1_x86_64.whl
 pip install --editable .
@@ -134,7 +134,7 @@ You can find more information about vLLM's wheels in [install-the-latest-code][i
 
 If you want to modify C++ or CUDA code, you'll need to build vLLM from source. This can take several minutes:
 
-```bash
+```console
 git clone https://github.com/vllm-project/vllm.git
 cd vllm
 pip install -e .
@@ -160,7 +160,7 @@ There are scenarios where the PyTorch dependency cannot be easily installed via
 
 To build vLLM using an existing PyTorch installation:
 
-```bash
+```console
 git clone https://github.com/vllm-project/vllm.git
 cd vllm
 python use_existing_torch.py
@@ -173,7 +173,7 @@ pip install --no-build-isolation -e .
 Currently, before starting the build process, vLLM fetches cutlass code from GitHub. However, there may be scenarios where you want to use a local version of cutlass instead.
 To achieve this, you can set the environment variable VLLM_CUTLASS_SRC_DIR to point to your local cutlass directory.
 
-```bash
+```console
 git clone https://github.com/vllm-project/vllm.git
 cd vllm
 VLLM_CUTLASS_SRC_DIR=/path/to/cutlass pip install -e .
@@ -184,7 +184,7 @@ VLLM_CUTLASS_SRC_DIR=/path/to/cutlass pip install -e .
 To avoid your system being overloaded, you can limit the number of compilation jobs
 to be run simultaneously, via the environment variable `MAX_JOBS`. For example:
 
-```bash
+```console
 export MAX_JOBS=6
 pip install -e .
 ```
@@ -194,7 +194,7 @@ A side effect is a much slower build process.
 
 Additionally, if you have trouble building vLLM, we recommend using the NVIDIA PyTorch Docker image.
 
-```bash
+```console
 # Use `--ipc=host` to make sure the shared memory is large enough.
 docker run \
     --gpus all \
@@ -205,14 +205,14 @@ docker run \
 
 If you don't want to use docker, it is recommended to have a full installation of CUDA Toolkit. You can download and install it from [the official website](https://developer.nvidia.com/cuda-toolkit-archive). After installation, set the environment variable `CUDA_HOME` to the installation path of CUDA Toolkit, and make sure that the `nvcc` compiler is in your `PATH`, e.g.:
 
-```bash
+```console
 export CUDA_HOME=/usr/local/cuda
 export PATH="${CUDA_HOME}/bin:$PATH"
 ```
 
 Here is a sanity check to verify that the CUDA Toolkit is correctly installed:
 
-```bash
+```console
 nvcc --version # verify that nvcc is in your PATH
 ${CUDA_HOME}/bin/nvcc --version # verify that nvcc is in your CUDA_HOME
 ```
@@ -223,7 +223,7 @@ vLLM can fully run only on Linux but for development purposes, you can still bui
 
 Simply disable the `VLLM_TARGET_DEVICE` environment variable before installing:
 
-```bash
+```console
 export VLLM_TARGET_DEVICE=empty
 pip install -e .
 ```
@@ -238,7 +238,7 @@ See [deployment-docker-pre-built-image][deployment-docker-pre-built-image] for i
 
 Another way to access the latest code is to use the docker images:
 
-```bash
+```console
 export VLLM_COMMIT=33f460b17a54acb3b6cc0b03f4a17876cff5eafd # use full commit hash from the main branch
 docker pull public.ecr.aws/q9t5s3a7/vllm-ci-postmerge-repo:${VLLM_COMMIT}
 ```

docs/getting_started/installation/gpu/rocm.inc.md

@@ -31,17 +31,17 @@ Currently, there are no pre-built ROCm wheels.
 
     Alternatively, you can install PyTorch using PyTorch wheels. You can check PyTorch installation guide in PyTorch [Getting Started](https://pytorch.org/get-started/locally/). Example:
 
-    ```bash
+    ```console
     # Install PyTorch
-    pip uninstall torch -y
-    pip install --no-cache-dir --pre torch --index-url https://download.pytorch.org/whl/nightly/rocm6.3
+    $ pip uninstall torch -y
+    $ pip install --no-cache-dir --pre torch --index-url https://download.pytorch.org/whl/nightly/rocm6.3
     ```
 
 1. Install [Triton flash attention for ROCm](https://github.com/ROCm/triton)
 
     Install ROCm's Triton flash attention (the default triton-mlir branch) following the instructions from [ROCm/triton](https://github.com/ROCm/triton/blob/triton-mlir/README.md)
 
-    ```bash
+    ```console
     python3 -m pip install ninja cmake wheel pybind11
     pip uninstall -y triton
     git clone https://github.com/OpenAI/triton.git
@@ -62,7 +62,7 @@ Currently, there are no pre-built ROCm wheels.
 
     For example, for ROCm 6.3, suppose your gfx arch is `gfx90a`. To get your gfx architecture, run `rocminfo |grep gfx`.
 
-    ```bash
+    ```console
     git clone https://github.com/ROCm/flash-attention.git
     cd flash-attention
     git checkout b7d29fb
@@ -76,7 +76,7 @@ Currently, there are no pre-built ROCm wheels.
 
 3. If you choose to build AITER yourself to use a certain branch or commit, you can build AITER using the following steps:
 
-    ```bash
+    ```console
     python3 -m pip uninstall -y aiter
     git clone --recursive https://github.com/ROCm/aiter.git
     cd aiter
@@ -90,8 +90,6 @@ Currently, there are no pre-built ROCm wheels.
 
 4. Build vLLM. For example, vLLM on ROCM 6.3 can be built with the following steps:
 
-    ??? Commands
-
     ```bash
     pip install --upgrade pip
 
@@ -148,7 +146,7 @@ If you choose to build this rocm_base image yourself, the steps are as follows.
 
 It is important that the user kicks off the docker build using buildkit. Either the user put DOCKER_BUILDKIT=1 as environment variable when calling docker build command, or the user needs to setup buildkit in the docker daemon configuration /etc/docker/daemon.json as follows and restart the daemon:
 
-```json
+```console
 {
     "features": {
         "buildkit": true
@@ -158,7 +156,7 @@ It is important that the user kicks off the docker build using buildkit. Either
 
 To build vllm on ROCm 6.3 for MI200 and MI300 series, you can use the default:
 
-```bash
+```console
 DOCKER_BUILDKIT=1 docker build \
     -f docker/Dockerfile.rocm_base \
     -t rocm/vllm-dev:base .
@@ -169,7 +167,7 @@ DOCKER_BUILDKIT=1 docker build \
 First, build a docker image from <gh-file:docker/Dockerfile.rocm> and launch a docker container from the image.
 It is important that the user kicks off the docker build using buildkit. Either the user put `DOCKER_BUILDKIT=1` as environment variable when calling docker build command, or the user needs to setup buildkit in the docker daemon configuration /etc/docker/daemon.json as follows and restart the daemon:
 
-```bash
+```console
 {
     "features": {
         "buildkit": true
@@ -187,13 +185,13 @@ Their values can be passed in when running `docker build` with `--build-arg` opt
 
 To build vllm on ROCm 6.3 for MI200 and MI300 series, you can use the default:
 
-```bash
+```console
 DOCKER_BUILDKIT=1 docker build -f docker/Dockerfile.rocm -t vllm-rocm .
 ```
 
 To build vllm on ROCm 6.3 for Radeon RX7900 series (gfx1100), you should pick the alternative base image:
 
-```bash
+```console
 DOCKER_BUILDKIT=1 docker build \
     --build-arg BASE_IMAGE="rocm/vllm-dev:navi_base" \
     -f docker/Dockerfile.rocm \
@@ -203,9 +201,7 @@ DOCKER_BUILDKIT=1 docker build \
 
 To run the above docker image `vllm-rocm`, use the below command:
 
-??? Command
-
-    ```bash
+```console
 docker run -it \
    --network=host \
    --group-add=video \

docs/getting_started/installation/gpu/xpu.inc.md

@@ -25,7 +25,7 @@ Currently, there are no pre-built XPU wheels.
 - First, install required driver and Intel OneAPI 2025.0 or later.
 - Second, install Python packages for vLLM XPU backend building:
 
-```bash
+```console
 git clone https://github.com/vllm-project/vllm.git
 cd vllm
 pip install --upgrade pip
@@ -34,7 +34,7 @@ pip install -v -r requirements/xpu.txt
 
 - Then, build and install vLLM XPU backend:
 
-```bash
+```console
 VLLM_TARGET_DEVICE=xpu python setup.py install
 ```
 
@@ -53,9 +53,9 @@ Currently, there are no pre-built XPU images.
 # --8<-- [end:pre-built-images]
 # --8<-- [start:build-image-from-source]
 
-```bash
-docker build -f docker/Dockerfile.xpu -t vllm-xpu-env --shm-size=4g .
-docker run -it \
+```console
+$ docker build -f docker/Dockerfile.xpu -t vllm-xpu-env --shm-size=4g .
+$ docker run -it \
              --rm \
              --network=host \
              --device /dev/dri \
@@ -68,7 +68,7 @@ docker run -it \
 
 XPU platform supports **tensor parallel** inference/serving and also supports **pipeline parallel** as a beta feature for online serving. We require Ray as the distributed runtime backend. For example, a reference execution like following:
 
-```bash
+```console
 python -m vllm.entrypoints.openai.api_server \
      --model=facebook/opt-13b \
      --dtype=bfloat16 \

docs/getting_started/installation/intel_gaudi.md

@@ -24,7 +24,7 @@ please follow the methods outlined in the
 
 To verify that the Intel Gaudi software was correctly installed, run:
 
-```bash
+```console
 hl-smi # verify that hl-smi is in your PATH and each Gaudi accelerator is visible
 apt list --installed | grep habana # verify that habanalabs-firmware-tools, habanalabs-graph, habanalabs-rdma-core, habanalabs-thunk and habanalabs-container-runtime are installed
 pip list | grep habana # verify that habana-torch-plugin, habana-torch-dataloader, habana-pyhlml and habana-media-loader are installed
@@ -42,7 +42,7 @@ for more details.
 
 Use the following commands to run a Docker image:
 
-```bash
+```console
 docker pull vault.habana.ai/gaudi-docker/1.18.0/ubuntu22.04/habanalabs/pytorch-installer-2.4.0:latest
 docker run \
   -it \
@@ -65,7 +65,7 @@ Currently, there are no pre-built Intel Gaudi wheels.
 
 To build and install vLLM from source, run:
 
-```bash
+```console
 git clone https://github.com/vllm-project/vllm.git
 cd vllm
 pip install -r requirements/hpu.txt
@@ -74,7 +74,7 @@ python setup.py develop
 
 Currently, the latest features and performance optimizations are developed in Gaudi's [vLLM-fork](https://github.com/HabanaAI/vllm-fork) and we periodically upstream them to vLLM main repo. To install latest [HabanaAI/vLLM-fork](https://github.com/HabanaAI/vllm-fork), run the following:
 
-```bash
+```console
 git clone https://github.com/HabanaAI/vllm-fork.git
 cd vllm-fork
 git checkout habana_main
@@ -90,7 +90,7 @@ Currently, there are no pre-built Intel Gaudi images.
 
 ### Build image from source
 
-```bash
+```console
 docker build -f docker/Dockerfile.hpu -t vllm-hpu-env  .
 docker run \
   -it \
@@ -200,7 +200,7 @@ INFO 08-01 21:37:59 hpu_model_runner.py:509] Generated 48 decode buckets: [(1, 1
 
 `min` determines the lowest value of the bucket. `step` determines the interval between buckets, and `max` determines the upper bound of the bucket. Furthermore, interval between `min` and `step` has special handling -- `min` gets multiplied by consecutive powers of two, until `step` gets reached. We call this the ramp-up phase and it is used for handling lower batch sizes with minimum wastage, while allowing larger padding on larger batch sizes.
 
-Example (with ramp-up):
+Example (with ramp-up)
 
 ```text
 min = 2, step = 32, max = 64
@@ -209,7 +209,7 @@ min = 2, step = 32, max = 64
 => buckets = ramp_up + stable => (2, 4, 8, 16, 32, 64)
 ```
 
-Example (without ramp-up):
+Example (without ramp-up)
 
 ```text
 min = 128, step = 128, max = 512
@@ -232,8 +232,6 @@ As an example, if a request of 3 sequences, with max sequence length of 412 come
 
 Warmup is an optional, but highly recommended step occurring before vLLM server starts listening. It executes a forward pass for each bucket with dummy data. The goal is to pre-compile all graphs and not incur any graph compilation overheads within bucket boundaries during server runtime. Each warmup step is logged during vLLM startup:
 
-??? Logs
-
 ```text
 INFO 08-01 22:26:47 hpu_model_runner.py:1066] [Warmup][Prompt][1/24] batch_size:4 seq_len:1024 free_mem:79.16 GiB
 INFO 08-01 22:26:47 hpu_model_runner.py:1066] [Warmup][Prompt][2/24] batch_size:4 seq_len:896 free_mem:55.43 GiB
@@ -281,8 +279,6 @@ When there's large amount of requests pending, vLLM scheduler will attempt to fi
 
 Each described step is logged by vLLM server, as follows (negative values correspond to memory being released):
 
-??? Logs
-
 ```text
 INFO 08-02 17:37:44 hpu_model_runner.py:493] Prompt bucket config (min, step, max_warmup) bs:[1, 32, 4], seq:[128, 128, 1024]
 INFO 08-02 17:37:44 hpu_model_runner.py:499] Generated 24 prompt buckets: [(1, 128), (1, 256), (1, 384), (1, 512), (1, 640), (1, 768), (1, 896), (1, 1024), (2, 128), (2, 256), (2, 384), (2, 512), (2, 640), (2, 768), (2, 896), (2, 1024), (4, 128), (4, 256), (4, 384), (4, 512), (4, 640), (4, 768), (4, 896), (4, 1024)]

docs/getting_started/installation/python_env_setup.inc.md

@@ -1,6 +1,6 @@
 It's recommended to use [uv](https://docs.astral.sh/uv/), a very fast Python environment manager, to create and manage Python environments. Please follow the [documentation](https://docs.astral.sh/uv/#getting-started) to install `uv`. After installing `uv`, you can create a new Python environment and install vLLM using the following commands:
 
-```bash
+```console
 uv venv --python 3.12 --seed
 source .venv/bin/activate
 ```

docs/getting_started/quickstart.md

@@ -19,7 +19,7 @@ If you are using NVIDIA GPUs, you can install vLLM using [pip](https://pypi.org/
 
 It's recommended to use [uv](https://docs.astral.sh/uv/), a very fast Python environment manager, to create and manage Python environments. Please follow the [documentation](https://docs.astral.sh/uv/#getting-started) to install `uv`. After installing `uv`, you can create a new Python environment and install vLLM using the following commands:
 
-```bash
+```console
 uv venv --python 3.12 --seed
 source .venv/bin/activate
 uv pip install vllm --torch-backend=auto
@@ -29,13 +29,13 @@ uv pip install vllm --torch-backend=auto
 
 Another delightful way is to use `uv run` with `--with [dependency]` option, which allows you to run commands such as `vllm serve` without creating any permanent environment:
 
-```bash
+```console
 uv run --with vllm vllm --help
 ```
 
 You can also use [conda](https://docs.conda.io/projects/conda/en/latest/user-guide/getting-started.html) to create and manage Python environments. You can install `uv` to the conda environment through `pip` if you want to manage it within the environment.
 
-```bash
+```console
 conda create -n myenv python=3.12 -y
 conda activate myenv
 pip install --upgrade uv
@@ -110,7 +110,7 @@ By default, it starts the server at `http://localhost:8000`. You can specify the
 
 Run the following command to start the vLLM server with the [Qwen2.5-1.5B-Instruct](https://huggingface.co/Qwen/Qwen2.5-1.5B-Instruct) model:
 
-```bash
+```console
 vllm serve Qwen/Qwen2.5-1.5B-Instruct
 ```
 
@@ -124,7 +124,7 @@ vllm serve Qwen/Qwen2.5-1.5B-Instruct
 
 This server can be queried in the same format as OpenAI API. For example, to list the models:
 
-```bash
+```console
 curl http://localhost:8000/v1/models
 ```
 
@@ -134,7 +134,7 @@ You can pass in the argument `--api-key` or environment variable `VLLM_API_KEY`
 
 Once your server is started, you can query the model with input prompts:
 
-```bash
+```console
 curl http://localhost:8000/v1/completions \
     -H "Content-Type: application/json" \
     -d '{
@@ -147,8 +147,6 @@ curl http://localhost:8000/v1/completions \
 
 Since this server is compatible with OpenAI API, you can use it as a drop-in replacement for any applications using OpenAI API. For example, another way to query the server is via the `openai` Python package:
 
-??? Code
-
 ```python
 from openai import OpenAI
 
@@ -172,7 +170,7 @@ vLLM is designed to also support the OpenAI Chat Completions API. The chat inter
 
 You can use the [create chat completion](https://platform.openai.com/docs/api-reference/chat/completions/create) endpoint to interact with the model:
 
-```bash
+```console
 curl http://localhost:8000/v1/chat/completions \
     -H "Content-Type: application/json" \
     -d '{
@@ -186,8 +184,6 @@ curl http://localhost:8000/v1/chat/completions \
 
 Alternatively, you can use the `openai` Python package:
 
-??? Code
-
 ```python
 from openai import OpenAI
 # Set OpenAI's API key and API base to use vLLM's API server.

docs/models/extensions/runai_model_streamer.md

@@ -9,27 +9,27 @@ Further reading can be found in [Run:ai Model Streamer Documentation](https://gi
 vLLM supports loading weights in Safetensors format using the Run:ai Model Streamer.
 You first need to install vLLM RunAI optional dependency:
 
-```bash
+```console
 pip3 install vllm[runai]
 ```
 
 To run it as an OpenAI-compatible server, add the `--load-format runai_streamer` flag:
 
-```bash
+```console
 vllm serve /home/meta-llama/Llama-3.2-3B-Instruct \
     --load-format runai_streamer
 ```
 
 To run model from AWS S3 object store run:
 
-```bash
+```console
 vllm serve s3://core-llm/Llama-3-8b \
     --load-format runai_streamer
 ```
 
 To run model from a S3 compatible object store run:
 
-```bash
+```console
 RUNAI_STREAMER_S3_USE_VIRTUAL_ADDRESSING=0 \
 AWS_EC2_METADATA_DISABLED=true \
 AWS_ENDPOINT_URL=https://storage.googleapis.com \
@@ -44,7 +44,7 @@ You can tune parameters using `--model-loader-extra-config`:
 You can tune `concurrency` that controls the level of concurrency and number of OS threads reading tensors from the file to the CPU buffer.
 For reading from S3, it will be the number of client instances the host is opening to the S3 server.
 
-```bash
+```console
 vllm serve /home/meta-llama/Llama-3.2-3B-Instruct \
     --load-format runai_streamer \
     --model-loader-extra-config '{"concurrency":16}'
@@ -53,7 +53,7 @@ vllm serve /home/meta-llama/Llama-3.2-3B-Instruct \
 You can control the size of the CPU Memory buffer to which tensors are read from the file, and limit this size.
 You can read further about CPU buffer memory limiting [here](https://github.com/run-ai/runai-model-streamer/blob/master/docs/src/env-vars.md#runai_streamer_memory_limit).
 
-```bash
+```console
 vllm serve /home/meta-llama/Llama-3.2-3B-Instruct \
     --load-format runai_streamer \
     --model-loader-extra-config '{"memory_limit":5368709120}'
@@ -66,13 +66,13 @@ vllm serve /home/meta-llama/Llama-3.2-3B-Instruct \
 
 vLLM also supports loading sharded models using Run:ai Model Streamer. This is particularly useful for large models that are split across multiple files. To use this feature, use the `--load-format runai_streamer_sharded` flag:
 
-```bash
+```console
 vllm serve /path/to/sharded/model --load-format runai_streamer_sharded
 ```
 
 The sharded loader expects model files to follow the same naming pattern as the regular sharded state loader: `model-rank-{rank}-part-{part}.safetensors`. You can customize this pattern using the `pattern` parameter in `--model-loader-extra-config`:
 
-```bash
+```console
 vllm serve /path/to/sharded/model \
     --load-format runai_streamer_sharded \
     --model-loader-extra-config '{"pattern":"custom-model-rank-{rank}-part-{part}.safetensors"}'
@@ -82,7 +82,7 @@ To create sharded model files, you can use the script provided in <gh-file:examp
 
 The sharded loader supports all the same tunable parameters as the regular Run:ai Model Streamer, including `concurrency` and `memory_limit`. These can be configured in the same way:
 
-```bash
+```console
 vllm serve /path/to/sharded/model \
     --load-format runai_streamer_sharded \
     --model-loader-extra-config '{"concurrency":16, "memory_limit":5368709120}'

docs/models/generative_models.md

@@ -85,8 +85,6 @@ and automatically applies the model's [chat template](https://huggingface.co/doc
     In general, only instruction-tuned models have a chat template.
     Base models may perform poorly as they are not trained to respond to the chat conversation.
 
-??? Code
-
 ```python
 from vllm import LLM
 

docs/models/supported_models.md

@@ -70,10 +70,7 @@ To make your model compatible with the Transformers backend, it needs:
 2. `MyAttention` must use `ALL_ATTENTION_FUNCTIONS` to call attention.
 3. `MyModel` must contain `_supports_attention_backend = True`.
 
-<details>
-<summary>modeling_my_model.py</summary>
-
-```python
+```python title="modeling_my_model.py"
 
 from transformers import PreTrainedModel
 from torch import nn
@@ -96,8 +93,6 @@ class MyModel(PreTrainedModel):
     _supports_attention_backend = True
 ```
 
-</details>
-
 Here is what happens in the background when this model is loaded:
 
 1. The config is loaded.
@@ -108,10 +103,7 @@ That's it!
 
 For your model to be compatible with vLLM's tensor parallel and/or pipeline parallel features, you must add `base_model_tp_plan` and/or `base_model_pp_plan` to your model's config class:
 
-<details>
-<summary>configuration_my_model.py</summary>
-
-```python
+```python title="configuration_my_model.py"
 
 from transformers import PretrainedConfig
 
@@ -131,8 +123,6 @@ class MyConfig(PretrainedConfig):
     }
 ```
 
-</details>
-
 - `base_model_tp_plan` is a `dict` that maps fully qualified layer name patterns to tensor parallel styles (currently only `"colwise"` and `"rowwise"` are supported).
 - `base_model_pp_plan` is a `dict` that maps direct child layer names to `tuple`s of `list`s of `str`s:
     * You only need to do this for layers which are not present on all pipeline stages
@@ -178,7 +168,7 @@ Alternatively, you can [open an issue on GitHub](https://github.com/vllm-project
 
 If you prefer, you can use the Hugging Face CLI to [download a model](https://huggingface.co/docs/huggingface_hub/guides/cli#huggingface-cli-download) or specific files from a model repository:
 
-```bash
+```console
 # Download a model
 huggingface-cli download HuggingFaceH4/zephyr-7b-beta
 
@@ -193,7 +183,7 @@ huggingface-cli download HuggingFaceH4/zephyr-7b-beta eval_results.json
 
 Use the Hugging Face CLI to [manage models](https://huggingface.co/docs/huggingface_hub/guides/manage-cache#scan-your-cache) stored in local cache:
 
-```bash
+```console
 # List cached models
 huggingface-cli scan-cache
 
@@ -208,9 +198,6 @@ huggingface-cli scan-cache --dir ~/.cache/huggingface/hub
 
 Use the Hugging Face CLI to interactively [delete downloaded model](https://huggingface.co/docs/huggingface_hub/guides/manage-cache#clean-your-cache) from the cache:
 
-<details>
-<summary>Commands</summary>
-
 ```console
 # The `delete-cache` command requires extra dependencies to work with the TUI.
 # Please run `pip install huggingface_hub[cli]` to install them.
@@ -237,8 +224,6 @@ Start deletion.
 Done. Deleted 1 repo(s) and 0 revision(s) for a total of 438.9M.
 ```
 
-</details>
-
 #### Using a proxy
 
 Here are some tips for loading/downloading models from Hugging Face using a proxy:
@@ -407,15 +392,15 @@ Specified using `--task embed`.
 | Architecture                                           | Models              | Example HF Models                                                                                                   | [LoRA][lora-adapter] | [PP][distributed-serving] | [V1](gh-issue:8779)   |
 |--------------------------------------------------------|---------------------|---------------------------------------------------------------------------------------------------------------------|----------------------|---------------------------|-----------------------|
 | `BertModel`                                            | BERT-based          | `BAAI/bge-base-en-v1.5`, `Snowflake/snowflake-arctic-embed-xs`, etc.                                                |                      |                           |                       |
-| `Gemma2Model`                                          | Gemma 2-based       | `BAAI/bge-multilingual-gemma2`, etc.                                                                                | ✅︎                   |                           | ✅︎                     |
+| `Gemma2Model`                                          | Gemma 2-based       | `BAAI/bge-multilingual-gemma2`, etc.                                                                                | ✅︎                   |                           |                       |
 | `GritLM`                                               | GritLM              | `parasail-ai/GritLM-7B-vllm`.                                                                                       | ✅︎                   | ✅︎                        |                       |
 | `GteModel`                                             | Arctic-Embed-2.0-M  | `Snowflake/snowflake-arctic-embed-m-v2.0`.                                                                          | ︎                     |                           |                       |
 | `GteNewModel`                                          | mGTE-TRM (see note) | `Alibaba-NLP/gte-multilingual-base`, etc.                                                                           | ︎                     | ︎                         |                       |
 | `ModernBertModel`                                      | ModernBERT-based    | `Alibaba-NLP/gte-modernbert-base`, etc.                                                                             | ︎                     | ︎                         |                       |
 | `NomicBertModel`                                       | Nomic BERT          | `nomic-ai/nomic-embed-text-v1`, `nomic-ai/nomic-embed-text-v2-moe`, `Snowflake/snowflake-arctic-embed-m-long`, etc. | ︎                     | ︎                         |                       |
-| `LlamaModel`, `LlamaForCausalLM`, `MistralModel`, etc. | Llama-based         | `intfloat/e5-mistral-7b-instruct`, etc.                                                                             | ✅︎                   | ✅︎                        | ✅︎                     |
-| `Qwen2Model`, `Qwen2ForCausalLM`                       | Qwen2-based         | `ssmits/Qwen2-7B-Instruct-embed-base` (see note), `Alibaba-NLP/gte-Qwen2-7B-instruct` (see note), etc.              | ✅︎                   | ✅︎                        | ✅︎                     |
-| `Qwen3Model`, `Qwen3ForCausalLM`                       | Qwen3-based         | `Qwen/Qwen3-Embedding-0.6B`, etc.                                                                                   | ✅︎                   | ✅︎                        | ✅︎                     |
+| `LlamaModel`, `LlamaForCausalLM`, `MistralModel`, etc. | Llama-based         | `intfloat/e5-mistral-7b-instruct`, etc.                                                                             | ✅︎                   | ✅︎                        |                       |
+| `Qwen2Model`, `Qwen2ForCausalLM`                       | Qwen2-based         | `ssmits/Qwen2-7B-Instruct-embed-base` (see note), `Alibaba-NLP/gte-Qwen2-7B-instruct` (see note), etc.              | ✅︎                   | ✅︎                        |                       |
+| `Qwen3Model`, `Qwen3ForCausalLM`                       | Qwen3-based         | `Qwen/Qwen3-Embedding-0.6B`, etc.                                                                                   | ✅︎                   | ✅︎                        |                       |
 | `RobertaModel`, `RobertaForMaskedLM`                   | RoBERTa-based       | `sentence-transformers/all-roberta-large-v1`, etc.                                                                  |                      |                           |                       |
 
 !!! note
@@ -442,10 +427,9 @@ Specified using `--task reward`.
 
 | Architecture              | Models          | Example HF Models                                                      | [LoRA][lora-adapter]   | [PP][distributed-serving]   | [V1](gh-issue:8779)   |
 |---------------------------|-----------------|------------------------------------------------------------------------|------------------------|-----------------------------|-----------------------|
-| `InternLM2ForRewardModel` | InternLM2-based | `internlm/internlm2-1_8b-reward`, `internlm/internlm2-7b-reward`, etc. | ✅︎                     | ✅︎                          | ✅︎                     |
-| `LlamaForCausalLM`        | Llama-based     | `peiyi9979/math-shepherd-mistral-7b-prm`, etc.                         | ✅︎                     | ✅︎                          | ✅︎                     |
-| `Qwen2ForRewardModel`     | Qwen2-based     | `Qwen/Qwen2.5-Math-RM-72B`, etc.                                       | ✅︎                     | ✅︎                          | ✅︎                     |
-| `Qwen2ForProcessRewardModel`     | Qwen2-based     | `Qwen/Qwen2.5-Math-PRM-7B`, etc.                                       | ✅︎                     | ✅︎                          | ✅︎                     |
+| `InternLM2ForRewardModel` | InternLM2-based | `internlm/internlm2-1_8b-reward`, `internlm/internlm2-7b-reward`, etc. | ✅︎                     | ✅︎                          |                       |
+| `LlamaForCausalLM`        | Llama-based     | `peiyi9979/math-shepherd-mistral-7b-prm`, etc.                         | ✅︎                     | ✅︎                          |                       |
+| `Qwen2ForRewardModel`     | Qwen2-based     | `Qwen/Qwen2.5-Math-RM-72B`, etc.                                       | ✅︎                     | ✅︎                          |                       |
 
 If your model is not in the above list, we will try to automatically convert the model using
 [as_reward_model][vllm.model_executor.models.adapters.as_reward_model]. By default, we return the hidden states of each token directly.
@@ -461,7 +445,7 @@ Specified using `--task classify`.
 | Architecture                     | Models   | Example HF Models                      | [LoRA][lora-adapter]   | [PP][distributed-serving]   | [V1](gh-issue:8779)   |
 |----------------------------------|----------|----------------------------------------|------------------------|-----------------------------|-----------------------|
 | `JambaForSequenceClassification` | Jamba    | `ai21labs/Jamba-tiny-reward-dev`, etc. | ✅︎                     | ✅︎                          |                       |
-| `GPT2ForSequenceClassification`  | GPT2     | `nie3e/sentiment-polish-gpt2-small`    |                        |                             | ✅︎                     |
+
 If your model is not in the above list, we will try to automatically convert the model using
 [as_classification_model][vllm.model_executor.models.adapters.as_classification_model]. By default, the class probabilities are extracted from the softmaxed hidden state corresponding to the last token.
 
@@ -472,7 +456,7 @@ Specified using `--task score`.
 | Architecture                          | Models            | Example HF Models                                                                    | [V1](gh-issue:8779)   |
 |---------------------------------------|-------------------|--------------------------------------------------------------------------------------|-----------------------|
 | `BertForSequenceClassification`       | BERT-based        | `cross-encoder/ms-marco-MiniLM-L-6-v2`, etc.                                         |                       |
-| `Qwen3ForSequenceClassification`      | Qwen3-based       | `tomaarsen/Qwen3-Reranker-0.6B-seq-cls`, `Qwen/Qwen3-Reranker-0.6B` (see note), etc. | ✅︎                     |
+| `Qwen3ForSequenceClassification`      | Qwen3-based       | `tomaarsen/Qwen3-Reranker-0.6B-seq-cls`, `Qwen/Qwen3-Reranker-0.6B` (see note), etc. |                       |
 | `RobertaForSequenceClassification`    | RoBERTa-based     | `cross-encoder/quora-roberta-base`, etc.                                             |                       |
 | `XLMRobertaForSequenceClassification` | XLM-RoBERTa-based | `BAAI/bge-reranker-v2-m3`, etc.                                                      |                       |
 
@@ -578,7 +562,6 @@ Specified using `--task generate`.
 | `SkyworkR1VChatModel`                        | Skywork-R1V-38B                                                          | T + I                                                                 | `Skywork/Skywork-R1V-38B`                                                                                                                               |                        | ✅︎                          | ✅︎                    |
 | `SmolVLMForConditionalGeneration`            | SmolVLM2                                                                 | T + I                                                                 | `SmolVLM2-2.2B-Instruct`                                                                                                                                | ✅︎                     |                             | ✅︎                    |
 | `TarsierForConditionalGeneration`            | Tarsier                                                                  | T + I<sup>E+</sup>                                                    | `omni-search/Tarsier-7b`,`omni-search/Tarsier-34b`                                                                                                      |                        | ✅︎                          | ✅︎                    |
-| `Tarsier2ForConditionalGeneration`<sup>^</sup>            | Tarsier2                                                                  | T + I<sup>E+</sup> + V<sup>E+</sup>                                                    | `omni-research/Tarsier2-Recap-7b`,`omni-research/Tarsier2-7b-0115`                                                                                                      |                        | ✅︎                          | ✅︎                    |
 
 <sup>^</sup> You need to set the architecture name via `--hf-overrides` to match the one in vLLM.  
 &nbsp;&nbsp;&nbsp;&nbsp;• For example, to use DeepSeek-VL2 series models:  
@@ -617,8 +600,6 @@ Specified using `--task generate`.
 
     For the best results, we recommend using the following dependency versions (tested on A10 and L40):
 
-    ??? Dependency versions
-
     ```text
     # Core vLLM-compatible dependencies with Molmo accuracy setup (tested on L40)
     torch==2.5.1

docs/serving/distributed_serving.md

@@ -34,14 +34,14 @@ output = llm.generate("San Francisco is a")
 
 To run multi-GPU serving, pass in the `--tensor-parallel-size` argument when starting the server. For example, to run API server on 4 GPUs:
 
-```bash
+```console
  vllm serve facebook/opt-13b \
      --tensor-parallel-size 4
 ```
 
 You can also additionally specify `--pipeline-parallel-size` to enable pipeline parallelism. For example, to run API server on 8 GPUs with pipeline parallelism and tensor parallelism:
 
-```bash
+```console
  vllm serve gpt2 \
      --tensor-parallel-size 4 \
      --pipeline-parallel-size 2
@@ -55,7 +55,7 @@ The first step, is to start containers and organize them into a cluster. We have
 
 Pick a node as the head node, and run the following command:
 
-```bash
+```console
 bash run_cluster.sh \
                 vllm/vllm-openai \
                 ip_of_head_node \
@@ -66,7 +66,7 @@ bash run_cluster.sh \
 
 On the rest of the worker nodes, run the following command:
 
-```bash
+```console
 bash run_cluster.sh \
                 vllm/vllm-openai \
                 ip_of_head_node \
@@ -87,7 +87,7 @@ Then, on any node, use `docker exec -it node /bin/bash` to enter the container,
 
 After that, on any node, use `docker exec -it node /bin/bash` to enter the container again. **In the container**, you can use vLLM as usual, just as you have all the GPUs on one node: vLLM will be able to leverage GPU resources of all nodes in the Ray cluster, and therefore, only run the `vllm` command on this node but not other nodes. The common practice is to set the tensor parallel size to the number of GPUs in each node, and the pipeline parallel size to the number of nodes. For example, if you have 16 GPUs in 2 nodes (8 GPUs per node), you can set the tensor parallel size to 8 and the pipeline parallel size to 2:
 
-```bash
+```console
  vllm serve /path/to/the/model/in/the/container \
      --tensor-parallel-size 8 \
      --pipeline-parallel-size 2
@@ -95,7 +95,7 @@ After that, on any node, use `docker exec -it node /bin/bash` to enter the conta
 
 You can also use tensor parallel without pipeline parallel, just set the tensor parallel size to the number of GPUs in the cluster. For example, if you have 16 GPUs in 2 nodes (8 GPUs per node), you can set the tensor parallel size to 16:
 
-```bash
+```console
 vllm serve /path/to/the/model/in/the/container \
      --tensor-parallel-size 16
 ```

docs/serving/integrations/langchain.md

@@ -7,14 +7,12 @@ vLLM is also available via [LangChain](https://github.com/langchain-ai/langchain
 
 To install LangChain, run
 
-```bash
+```console
 pip install langchain langchain_community -q
 ```
 
 To run inference on a single or multiple GPUs, use `VLLM` class from `langchain`.
 
-??? Code
-
 ```python
 from langchain_community.llms import VLLM
 

docs/serving/integrations/llamaindex.md

@@ -7,7 +7,7 @@ vLLM is also available via [LlamaIndex](https://github.com/run-llama/llama_index
 
 To install LlamaIndex, run
 
-```bash
+```console
 pip install llama-index-llms-vllm -q
 ```
 

docs/serving/openai_compatible_server.md

@@ -15,8 +15,6 @@ vllm serve NousResearch/Meta-Llama-3-8B-Instruct \
 
 To call the server, in your preferred text editor, create a script that uses an HTTP client. Include any messages that you want to send to the model. Then run that script. Below is an example script using the [official OpenAI Python client](https://github.com/openai/openai-python).
 
-??? Code
-
 ```python
 from openai import OpenAI
 client = OpenAI(
@@ -149,8 +147,6 @@ with `--enable-request-id-headers`.
 > rather than within the vLLM layer for this reason.
 > See [this PR](https://github.com/vllm-project/vllm/pull/11529) for more details.
 
-??? Code
-
 ```python
 completion = client.chat.completions.create(
     model="NousResearch/Meta-Llama-3-8B-Instruct",
@@ -188,16 +184,12 @@ Code example: <gh-file:examples/online_serving/openai_completion_client.py>
 
 The following [sampling parameters][sampling-params] are supported.
 
-??? Code
-
 ```python
 --8<-- "vllm/entrypoints/openai/protocol.py:completion-sampling-params"
 ```
 
 The following extra parameters are supported:
 
-??? Code
-
 ```python
 --8<-- "vllm/entrypoints/openai/protocol.py:completion-extra-params"
 ```
@@ -220,16 +212,12 @@ Code example: <gh-file:examples/online_serving/openai_chat_completion_client.py>
 
 The following [sampling parameters][sampling-params] are supported.
 
-??? Code
-
 ```python
 --8<-- "vllm/entrypoints/openai/protocol.py:chat-completion-sampling-params"
 ```
 
 The following extra parameters are supported:
 
-??? Code
-
 ```python
 --8<-- "vllm/entrypoints/openai/protocol.py:chat-completion-extra-params"
 ```
@@ -271,8 +259,6 @@ and passing a list of `messages` in the request. Refer to the examples below for
 
     Since the request schema is not defined by OpenAI client, we post a request to the server using the lower-level `requests` library:
 
-    ??? Code
-
     ```python
     import requests
 
@@ -330,16 +316,12 @@ The following [pooling parameters][pooling-params] are supported.
 
 The following extra parameters are supported by default:
 
-??? Code
-
 ```python
 --8<-- "vllm/entrypoints/openai/protocol.py:embedding-extra-params"
 ```
 
 For chat-like input (i.e. if `messages` is passed), these extra parameters are supported instead:
 
-??? Code
-
 ```python
 --8<-- "vllm/entrypoints/openai/protocol.py:chat-embedding-extra-params"
 ```
@@ -361,16 +343,12 @@ Code example: <gh-file:examples/online_serving/openai_transcription_client.py>
 
 The following [sampling parameters][sampling-params] are supported.
 
-??? Code
-
 ```python
 --8<-- "vllm/entrypoints/openai/protocol.py:transcription-sampling-params"
 ```
 
 The following extra parameters are supported:
 
-??? Code
-
 ```python
 --8<-- "vllm/entrypoints/openai/protocol.py:transcription-extra-params"
 ```
@@ -409,6 +387,8 @@ Code example: <gh-file:examples/online_serving/openai_classification_client.py>
 
 You can classify multiple texts by passing an array of strings:
 
+Request:
+
 ```bash
 curl -v "http://127.0.0.1:8000/classify" \
   -H "Content-Type: application/json" \
@@ -421,7 +401,7 @@ curl -v "http://127.0.0.1:8000/classify" \
   }'
 ```
 
-??? Response
+Response:
 
 ```bash
 {
@@ -460,6 +440,8 @@ curl -v "http://127.0.0.1:8000/classify" \
 
 You can also pass a string directly to the `input` field:
 
+Request:
+
 ```bash
 curl -v "http://127.0.0.1:8000/classify" \
   -H "Content-Type: application/json" \
@@ -469,7 +451,7 @@ curl -v "http://127.0.0.1:8000/classify" \
   }'
 ```
 
-??? Response
+Response:
 
 ```bash
 {
@@ -526,6 +508,8 @@ Code example: <gh-file:examples/online_serving/openai_cross_encoder_score.py>
 
 You can pass a string to both `text_1` and `text_2`, forming a single sentence pair.
 
+Request:
+
 ```bash
 curl -X 'POST' \
   'http://127.0.0.1:8000/score' \
@@ -539,7 +523,7 @@ curl -X 'POST' \
 }'
 ```
 
-??? Response
+Response:
 
 ```bash
 {
@@ -564,7 +548,7 @@ You can pass a string to `text_1` and a list to `text_2`, forming multiple sente
 where each pair is built from `text_1` and a string in `text_2`.
 The total number of pairs is `len(text_2)`.
 
-??? Request
+Request:
 
 ```bash
 curl -X 'POST' \
@@ -581,7 +565,7 @@ The total number of pairs is `len(text_2)`.
 }'
 ```
 
-??? Response
+Response:
 
 ```bash
 {
@@ -609,7 +593,7 @@ You can pass a list to both `text_1` and `text_2`, forming multiple sentence pai
 where each pair is built from a string in `text_1` and the corresponding string in `text_2` (similar to `zip()`).
 The total number of pairs is `len(text_2)`.
 
-??? Request
+Request:
 
 ```bash
 curl -X 'POST' \
@@ -630,7 +614,7 @@ The total number of pairs is `len(text_2)`.
 }'
 ```
 
-??? Response
+Response:
 
 ```bash
 {
@@ -691,7 +675,7 @@ Code example: <gh-file:examples/online_serving/jinaai_rerank_client.py>
 Note that the `top_n` request parameter is optional and will default to the length of the `documents` field.
 Result documents will be sorted by relevance, and the `index` property can be used to determine original order.
 
-??? Request
+Request:
 
 ```bash
 curl -X 'POST' \
@@ -709,7 +693,7 @@ Result documents will be sorted by relevance, and the `index` property can be us
 }'
 ```
 
-??? Response
+Response:
 
 ```bash
 {

docs/usage/metrics.md

@@ -6,14 +6,12 @@ OpenAI compatible API server.
 
 You can start the server using Python, or using [Docker][deployment-docker]:
 
-```bash
+```console
 vllm serve unsloth/Llama-3.2-1B-Instruct
 ```
 
 Then query the endpoint to get the latest metrics from the server:
 
-??? Output
-
 ```console
 $ curl http://0.0.0.0:8000/metrics
 
@@ -33,8 +31,6 @@ Then query the endpoint to get the latest metrics from the server:
 
 The following metrics are exposed:
 
-??? Code
-
 ```python
 --8<-- "vllm/engine/metrics.py:metrics-definitions"
 ```

docs/usage/troubleshooting.md

@@ -60,8 +60,6 @@ To identify the particular CUDA operation that causes the error, you can add `--
 
 If GPU/CPU communication cannot be established, you can use the following Python script and follow the instructions below to confirm whether the GPU/CPU communication is working correctly.
 
-??? Code
-
 ```python
 # Test PyTorch NCCL
 import torch
@@ -127,17 +125,14 @@ If GPU/CPU communication cannot be established, you can use the following Python
 
 If you are testing with a single node, adjust `--nproc-per-node` to the number of GPUs you want to use:
 
-```bash
+```console
 NCCL_DEBUG=TRACE torchrun --nproc-per-node=<number-of-GPUs> test.py
 ```
 
 If you are testing with multi-nodes, adjust `--nproc-per-node` and `--nnodes` according to your setup and set `MASTER_ADDR` to the correct IP address of the master node, reachable from all nodes. Then, run:
 
-```bash
-NCCL_DEBUG=TRACE torchrun --nnodes 2 \
-    --nproc-per-node=2 \
-    --rdzv_backend=c10d \
-    --rdzv_endpoint=$MASTER_ADDR test.py
+```console
+NCCL_DEBUG=TRACE torchrun --nnodes 2 --nproc-per-node=2 --rdzv_backend=c10d --rdzv_endpoint=$MASTER_ADDR test.py
 ```
 
 If the script runs successfully, you should see the message `sanity check is successful!`.
@@ -170,8 +165,6 @@ WARNING 12-11 14:50:37 multiproc_worker_utils.py:281] CUDA was previously
 
 or an error from Python that looks like this:
 
-??? Logs
-
 ```console
 RuntimeError:
         An attempt has been made to start a new process before the
@@ -214,8 +207,6 @@ if __name__ == '__main__':
 
 vLLM heavily depends on `torch.compile` to optimize the model for better performance, which introduces the dependency on the `torch.compile` functionality and the `triton` library. By default, we use `torch.compile` to [optimize some functions](https://github.com/vllm-project/vllm/pull/10406) in the model. Before running vLLM, you can check if `torch.compile` is working as expected by running the following script:
 
-??? Code
-
 ```python
 import torch
 

docs/usage/usage_stats.md

@@ -10,8 +10,6 @@ The list of data collected by the latest version of vLLM can be found here: <gh-
 
 Here is an example as of v0.4.0:
 
-??? Output
-
 ```json
 {
   "uuid": "fbe880e9-084d-4cab-a395-8984c50f1109",

docs/usage/v1_guide.md

@@ -45,18 +45,6 @@ For each item, our progress towards V1 support falls into one of the following s
 - **🟠 Delayed**: Temporarily dropped in V1 but planned to be re-introduced later.
 - **🔴 Deprecated**: Not planned for V1 unless there is strong demand.
 
-!!! note
-    vLLM V1’s unified scheduler treats both prompt and output tokens the same
-    way by using a simple dictionary (e.g., `{request_id: num_tokens}`) to dynamically
-    allocate a fixed token budget per request, enabling features like chunked prefills,
-    prefix caching, and speculative decoding without a strict separation between prefill
-    and decode phases.
-
-The V1 scheduler supports multiple scheduling policies, including First-Come,
-First-Served (FCFS) and priority-based scheduling (where requests are processed
-based on assigned priority, with FCFS as a tie-breaker), configurable via the
-`--scheduling-policy` argument.
-
 ### Hardware
 
 | Hardware   | Status                             |
@@ -82,7 +70,7 @@ based on assigned priority, with FCFS as a tie-breaker), configurable via the
 |-----------------------------|------------------------------------------------------------------------------------|
 | **Decoder-only Models**     | <nobr>🚀 Optimized</nobr>                                                          |
 | **Encoder-Decoder Models**  | <nobr>🟠 Delayed</nobr>                                                            |
-| **Embedding Models**        | <nobr>🟢 Functional</nobr>                                                         |
+| **Embedding Models**        | <nobr>🚧 WIP ([PR #16188](https://github.com/vllm-project/vllm/pull/16188))</nobr> |
 | **Mamba Models**            | <nobr>🚧 WIP ([PR #19327](https://github.com/vllm-project/vllm/pull/19327))</nobr> |
 | **Multimodal Models**       | <nobr>🟢 Functional</nobr>                                                         |
 
@@ -92,11 +80,11 @@ vLLM V1 currently excludes model architectures with the `SupportsV0Only` protoco
 
     This corresponds to the V1 column in our [list of supported models][supported-models].
 
-See below for the status of models that are not yet supported or have more features planned in V1.
+See below for the status of models that are still not yet supported in V1.
 
 #### Embedding Models
 
-The initial basic support is now functional.
+The initial support will be provided by [PR #16188](https://github.com/vllm-project/vllm/pull/16188).
 
 Later, we will consider using [hidden states processor](https://github.com/vllm-project/vllm/issues/12249),
 which is based on [global logits processor](https://github.com/vllm-project/vllm/pull/13360)

examples/offline_inference/basic/embed.py

@@ -12,10 +12,7 @@ def parse_args():
     parser = EngineArgs.add_cli_args(parser)
     # Set example specific arguments
     parser.set_defaults(
-        model="intfloat/e5-mistral-7b-instruct",
-        task="embed",
-        enforce_eager=True,
-        max_model_len=1024,
+        model="intfloat/e5-mistral-7b-instruct", task="embed", enforce_eager=True
     )
     return parser.parse_args()
 

examples/offline_inference/openai_batch/README.md

@@ -29,14 +29,14 @@ We currently support `/v1/chat/completions`, `/v1/embeddings`, and `/v1/score` e
 
 To follow along with this example, you can download the example batch, or create your own batch file in your working directory.
 
-```bash
+```console
 wget https://raw.githubusercontent.com/vllm-project/vllm/main/examples/offline_inference/openai_batch/openai_example_batch.jsonl
 ```
 
 Once you've created your batch file it should look like this
 
-```bash
-cat offline_inference/openai_batch/openai_example_batch.jsonl
+```console
+$ cat offline_inference/openai_batch/openai_example_batch.jsonl
 {"custom_id": "request-1", "method": "POST", "url": "/v1/chat/completions", "body": {"model": "meta-llama/Meta-Llama-3-8B-Instruct", "messages": [{"role": "system", "content": "You are a helpful assistant."},{"role": "user", "content": "Hello world!"}],"max_completion_tokens": 1000}}
 {"custom_id": "request-2", "method": "POST", "url": "/v1/chat/completions", "body": {"model": "meta-llama/Meta-Llama-3-8B-Instruct", "messages": [{"role": "system", "content": "You are an unhelpful assistant."},{"role": "user", "content": "Hello world!"}],"max_completion_tokens": 1000}}
 ```
@@ -47,7 +47,7 @@ The batch running tool is designed to be used from the command line.
 
 You can run the batch with the following command, which will write its results to a file called `results.jsonl`
 
-```bash
+```console
 python -m vllm.entrypoints.openai.run_batch \
     -i offline_inference/openai_batch/openai_example_batch.jsonl \
     -o results.jsonl \
@@ -56,7 +56,7 @@ python -m vllm.entrypoints.openai.run_batch \
 
 or use command-line:
 
-```bash
+```console
 vllm run-batch \
     -i offline_inference/openai_batch/openai_example_batch.jsonl \
     -o results.jsonl \
@@ -67,8 +67,8 @@ vllm run-batch \
 
 You should now have your results at `results.jsonl`. You can check your results by running `cat results.jsonl`
 
-```bash
-cat results.jsonl
+```console
+$ cat results.jsonl
 {"id":"vllm-383d1c59835645aeb2e07d004d62a826","custom_id":"request-1","response":{"id":"cmpl-61c020e54b964d5a98fa7527bfcdd378","object":"chat.completion","created":1715633336,"model":"meta-llama/Meta-Llama-3-8B-Instruct","choices":[{"index":0,"message":{"role":"assistant","content":"Hello! It's great to meet you! I'm here to help with any questions or tasks you may have. What's on your mind today?"},"logprobs":null,"finish_reason":"stop","stop_reason":null}],"usage":{"prompt_tokens":25,"total_tokens":56,"completion_tokens":31}},"error":null}
 {"id":"vllm-42e3d09b14b04568afa3f1797751a267","custom_id":"request-2","response":{"id":"cmpl-f44d049f6b3a42d4b2d7850bb1e31bcc","object":"chat.completion","created":1715633336,"model":"meta-llama/Meta-Llama-3-8B-Instruct","choices":[{"index":0,"message":{"role":"assistant","content":"*silence*"},"logprobs":null,"finish_reason":"stop","stop_reason":null}],"usage":{"prompt_tokens":27,"total_tokens":32,"completion_tokens":5}},"error":null}
 ```
@@ -79,7 +79,7 @@ The batch runner supports remote input and output urls that are accessible via h
 
 For example, to run against our example input file located at `https://raw.githubusercontent.com/vllm-project/vllm/main/examples/offline_inference/openai_batch/openai_example_batch.jsonl`, you can run
 
-```bash
+```console
 python -m vllm.entrypoints.openai.run_batch \
     -i https://raw.githubusercontent.com/vllm-project/vllm/main/examples/offline_inference/openai_batch/openai_example_batch.jsonl \
     -o results.jsonl \
@@ -88,7 +88,7 @@ python -m vllm.entrypoints.openai.run_batch \
 
 or use command-line:
 
-```bash
+```console
 vllm run-batch \
     -i https://raw.githubusercontent.com/vllm-project/vllm/main/examples/offline_inference/openai_batch/openai_example_batch.jsonl \
     -o results.jsonl \
@@ -112,21 +112,21 @@ To integrate with cloud blob storage, we recommend using presigned urls.
 
 To follow along with this example, you can download the example batch, or create your own batch file in your working directory.
 
-```bash
+```console
 wget https://raw.githubusercontent.com/vllm-project/vllm/main/examples/offline_inference/openai_batch/openai_example_batch.jsonl
 ```
 
 Once you've created your batch file it should look like this
 
-```bash
-cat offline_inference/openai_batch/openai_example_batch.jsonl
+```console
+$ cat offline_inference/openai_batch/openai_example_batch.jsonl
 {"custom_id": "request-1", "method": "POST", "url": "/v1/chat/completions", "body": {"model": "meta-llama/Meta-Llama-3-8B-Instruct", "messages": [{"role": "system", "content": "You are a helpful assistant."},{"role": "user", "content": "Hello world!"}],"max_completion_tokens": 1000}}
 {"custom_id": "request-2", "method": "POST", "url": "/v1/chat/completions", "body": {"model": "meta-llama/Meta-Llama-3-8B-Instruct", "messages": [{"role": "system", "content": "You are an unhelpful assistant."},{"role": "user", "content": "Hello world!"}],"max_completion_tokens": 1000}}
 ```
 
 Now upload your batch file to your S3 bucket.
 
-```bash
+```console
 aws s3 cp offline_inference/openai_batch/openai_example_batch.jsonl s3://MY_BUCKET/MY_INPUT_FILE.jsonl
 ```
 
@@ -181,7 +181,7 @@ output_url='https://s3.us-west-2.amazonaws.com/MY_BUCKET/MY_OUTPUT_FILE.jsonl?AW
 
 You can now run the batch runner, using the urls generated in the previous section.
 
-```bash
+```console
 python -m vllm.entrypoints.openai.run_batch \
     -i "https://s3.us-west-2.amazonaws.com/MY_BUCKET/MY_INPUT_FILE.jsonl?AWSAccessKeyId=ABCDEFGHIJKLMNOPQRST&Signature=abcdefghijklmnopqrstuvwxyz12345&Expires=1715800091" \
     -o "https://s3.us-west-2.amazonaws.com/MY_BUCKET/MY_OUTPUT_FILE.jsonl?AWSAccessKeyId=ABCDEFGHIJKLMNOPQRST&Signature=abcdefghijklmnopqrstuvwxyz12345&Expires=1715800091" \
@@ -190,7 +190,7 @@ python -m vllm.entrypoints.openai.run_batch \
 
 or use command-line:
 
-```bash
+```console
 vllm run-batch \
     -i "https://s3.us-west-2.amazonaws.com/MY_BUCKET/MY_INPUT_FILE.jsonl?AWSAccessKeyId=ABCDEFGHIJKLMNOPQRST&Signature=abcdefghijklmnopqrstuvwxyz12345&Expires=1715800091" \
     -o "https://s3.us-west-2.amazonaws.com/MY_BUCKET/MY_OUTPUT_FILE.jsonl?AWSAccessKeyId=ABCDEFGHIJKLMNOPQRST&Signature=abcdefghijklmnopqrstuvwxyz12345&Expires=1715800091" \
@@ -201,7 +201,7 @@ vllm run-batch \
 
 Your results are now on S3. You can view them in your terminal by running
 
-```bash
+```console
 aws s3 cp s3://MY_BUCKET/MY_OUTPUT_FILE.jsonl -
 ```
 
@@ -230,8 +230,8 @@ You can run the batch using the same command as in earlier examples.
 
 You can check your results by running `cat results.jsonl`
 
-```bash
-cat results.jsonl
+```console
+$ cat results.jsonl
 {"id":"vllm-db0f71f7dec244e6bce530e0b4ef908b","custom_id":"request-1","response":{"status_code":200,"request_id":"vllm-batch-3580bf4d4ae54d52b67eee266a6eab20","body":{"id":"embd-33ac2efa7996430184461f2e38529746","object":"list","created":444647,"model":"intfloat/e5-mistral-7b-instruct","data":[{"index":0,"object":"embedding","embedding":[0.016204833984375,0.0092010498046875,0.0018358230590820312,-0.0028228759765625,0.001422882080078125,-0.0031147003173828125,...]}],"usage":{"prompt_tokens":8,"total_tokens":8,"completion_tokens":0}}},"error":null}
 ...
 ```
@@ -261,8 +261,8 @@ You can run the batch using the same command as in earlier examples.
 
 You can check your results by running `cat results.jsonl`
 
-```bash
-cat results.jsonl
+```console
+$ cat results.jsonl
 {"id":"vllm-f87c5c4539184f618e555744a2965987","custom_id":"request-1","response":{"status_code":200,"request_id":"vllm-batch-806ab64512e44071b37d3f7ccd291413","body":{"id":"score-4ee45236897b4d29907d49b01298cdb1","object":"list","created":1737847944,"model":"BAAI/bge-reranker-v2-m3","data":[{"index":0,"object":"score","score":0.0010900497436523438},{"index":1,"object":"score","score":1.0}],"usage":{"prompt_tokens":37,"total_tokens":37,"completion_tokens":0,"prompt_tokens_details":null}}},"error":null}
 {"id":"vllm-41990c51a26d4fac8419077f12871099","custom_id":"request-2","response":{"status_code":200,"request_id":"vllm-batch-73ce66379026482699f81974e14e1e99","body":{"id":"score-13f2ffe6ba40460fbf9f7f00ad667d75","object":"list","created":1737847944,"model":"BAAI/bge-reranker-v2-m3","data":[{"index":0,"object":"score","score":0.001094818115234375},{"index":1,"object":"score","score":1.0}],"usage":{"prompt_tokens":37,"total_tokens":37,"completion_tokens":0,"prompt_tokens_details":null}}},"error":null}
 ```

examples/offline_inference/qwen3_reranker.py

@@ -22,10 +22,7 @@ model_name = "Qwen/Qwen3-Reranker-0.6B"
 # If you want to load the official original version, the init parameters are
 # as follows.
 
-
-def get_model() -> LLM:
-    """Initializes and returns the LLM model for Qwen3-Reranker."""
-    return LLM(
+model = LLM(
     model=model_name,
     task="score",
     hf_overrides={
@@ -35,7 +32,6 @@ def get_model() -> LLM:
     },
 )
 
-
 # Why do we need hf_overrides for the official original version:
 # vllm converts it to Qwen3ForSequenceClassification when loaded for
 # better performance.
@@ -55,8 +51,7 @@ suffix = "<|im_end|>\n<|im_start|>assistant\n<think>\n\n</think>\n\n"
 query_template = "{prefix}<Instruct>: {instruction}\n<Query>: {query}\n"
 document_template = "<Document>: {doc}{suffix}"
 
-
-def main() -> None:
+if __name__ == "__main__":
     instruction = (
         "Given a web search query, retrieve relevant passages that answer the query"
     )
@@ -77,13 +72,6 @@ def main() -> None:
     ]
     documents = [document_template.format(doc=doc, suffix=suffix) for doc in documents]
 
-    model = get_model()
     outputs = model.score(queries, documents)
 
-    print("-" * 30)
     print([output.outputs.score for output in outputs])
-    print("-" * 30)
-
-
-if __name__ == "__main__":
-    main()

examples/offline_inference/vision_language.py

@@ -1040,37 +1040,6 @@ def run_qwen2_5_omni(questions: list[str], modality: str):
     )
 
 
-def run_tarsier2(questions: list[str], modality: str) -> ModelRequestData:
-    model_name = "omni-research/Tarsier2-Recap-7b"
-
-    engine_args = EngineArgs(
-        model=model_name,
-        max_model_len=4096,
-        hf_overrides={"architectures": ["Tarsier2ForConditionalGeneration"]},
-        limit_mm_per_prompt={modality: 1},
-    )
-
-    if modality == "image":
-        placeholder = "<|image_pad|>"
-    elif modality == "video":
-        placeholder = "<|video_pad|>"
-
-    prompts = [
-        (
-            "<|im_start|>system\nYou are a helpful assistant.<|im_end|>\n"
-            f"<|im_start|>user\n<|vision_start|>{placeholder}<|vision_end|>"
-            f"{question}<|im_end|>\n"
-            "<|im_start|>assistant\n"
-        )
-        for question in questions
-    ]
-
-    return ModelRequestData(
-        engine_args=engine_args,
-        prompts=prompts,
-    )
-
-
 # SkyworkR1V
 def run_skyworkr1v(questions: list[str], modality: str) -> ModelRequestData:
     assert modality == "image"
@@ -1143,7 +1112,6 @@ model_example_map = {
     "skywork_chat": run_skyworkr1v,
     "smolvlm": run_smolvlm,
     "tarsier": run_tarsier,
-    "tarsier2": run_tarsier2,
 }
 
 

examples/offline_inference/vision_language_embedding.py

@@ -94,7 +94,6 @@ def run_vlm2vec(query: Query) -> ModelRequestData:
     engine_args = EngineArgs(
         model="TIGER-Lab/VLM2Vec-Full",
         task="embed",
-        max_model_len=4096,
         trust_remote_code=True,
         mm_processor_kwargs={"num_crops": 4},
         limit_mm_per_prompt={"image": 1},

examples/offline_inference/vision_language_multi_image.py

@@ -828,32 +828,6 @@ def load_tarsier(question: str, image_urls: list[str]) -> ModelRequestData:
     )
 
 
-def load_tarsier2(question: str, image_urls: list[str]) -> ModelRequestData:
-    model_name = "omni-research/Tarsier2-Recap-7b"
-
-    engine_args = EngineArgs(
-        model=model_name,
-        trust_remote_code=True,
-        max_model_len=32768,
-        limit_mm_per_prompt={"image": len(image_urls)},
-        hf_overrides={"architectures": ["Tarsier2ForConditionalGeneration"]},
-    )
-
-    prompt = (
-        "<|im_start|>system\nYou are a helpful assistant.<|im_end|>\n"
-        f"<|im_start|>user\n<|vision_start|>{'<|image_pad|>' * len(image_urls)}"
-        f"<|vision_end|>{question}<|im_end|>\n"
-        "<|im_start|>assistant\n"
-    )
-    image_data = [fetch_image(url) for url in image_urls]
-
-    return ModelRequestData(
-        engine_args=engine_args,
-        prompt=prompt,
-        image_data=image_data,
-    )
-
-
 model_example_map = {
     "aria": load_aria,
     "aya_vision": load_aya_vision,
@@ -879,7 +853,6 @@ model_example_map = {
     "qwen2_5_vl": load_qwen2_5_vl,
     "smolvlm": load_smolvlm,
     "tarsier": load_tarsier,
-    "tarsier2": load_tarsier2,
 }
 
 

examples/online_serving/openai_chat_completion_client_with_tools_xlam.py

@@ -1,244 +0,0 @@
-# SPDX-License-Identifier: Apache-2.0
-# ruff: noqa: E501
-"""
-Set up this example by starting a vLLM OpenAI-compatible server with tool call
-options enabled for xLAM-2 models:
-
-vllm serve --model Salesforce/Llama-xLAM-2-8b-fc-r --enable-auto-tool-choice --tool-call-parser xlam
-
-OR
-
-vllm serve --model Salesforce/xLAM-2-3b-fc-r --enable-auto-tool-choice --tool-call-parser xlam
-"""
-
-import json
-import time
-
-from openai import OpenAI
-
-# Modify OpenAI's API key and API base to use vLLM's API server.
-openai_api_key = "empty"
-openai_api_base = "http://localhost:8000/v1"
-
-
-# Define tool functions
-def get_weather(location: str, unit: str):
-    return f"Weather in {location} is 22 degrees {unit}."
-
-
-def calculate_expression(expression: str):
-    try:
-        result = eval(expression)
-        return f"The result of {expression} is {result}"
-    except Exception as e:
-        return f"Could not calculate {expression}: {e}"
-
-
-def translate_text(text: str, target_language: str):
-    return f"Translation of '{text}' to {target_language}: [translated content]"
-
-
-# Define tools
-tools = [
-    {
-        "type": "function",
-        "function": {
-            "name": "get_weather",
-            "description": "Get the current weather in a given location",
-            "parameters": {
-                "type": "object",
-                "properties": {
-                    "location": {
-                        "type": "string",
-                        "description": "City and state, e.g., 'San Francisco, CA'",
-                    },
-                    "unit": {"type": "string", "enum": ["celsius", "fahrenheit"]},
-                },
-                "required": ["location", "unit"],
-            },
-        },
-    },
-    {
-        "type": "function",
-        "function": {
-            "name": "calculate_expression",
-            "description": "Calculate a mathematical expression",
-            "parameters": {
-                "type": "object",
-                "properties": {
-                    "expression": {
-                        "type": "string",
-                        "description": "Mathematical expression to evaluate, needs to be a valid python expression",
-                    }
-                },
-                "required": ["expression"],
-            },
-        },
-    },
-    {
-        "type": "function",
-        "function": {
-            "name": "translate_text",
-            "description": "Translate text to another language",
-            "parameters": {
-                "type": "object",
-                "properties": {
-                    "text": {"type": "string", "description": "Text to translate"},
-                    "target_language": {
-                        "type": "string",
-                        "description": "Target language for translation",
-                    },
-                },
-                "required": ["text", "target_language"],
-            },
-        },
-    },
-]
-
-# Map of function names to implementations
-tool_functions = {
-    "get_weather": get_weather,
-    "calculate_expression": calculate_expression,
-    "translate_text": translate_text,
-}
-
-
-def process_response(response, tool_functions, original_query):
-    """Process a non-streaming response with possible tool calls"""
-
-    print("\n--- Response Output ---")
-
-    # Check if the response has content
-    if response.choices[0].message.content:
-        print(f"Content: {response.choices[0].message.content}")
-
-    # Check if the response has tool calls
-    if response.choices[0].message.tool_calls:
-        print("--------------------------------")
-        print(f"Tool calls: {response.choices[0].message.tool_calls}")
-        print("--------------------------------")
-
-        # Collect all tool calls and results before making follow-up request
-        tool_results = []
-        assistant_message = {"role": "assistant"}
-
-        if response.choices[0].message.content:
-            assistant_message["content"] = response.choices[0].message.content
-
-        assistant_tool_calls = []
-
-        # Process each tool call
-        for tool_call in response.choices[0].message.tool_calls:
-            function_name = tool_call.function.name
-            function_args = tool_call.function.arguments
-            function_id = tool_call.id
-
-            print(f"Function called: {function_name}")
-            print(f"Arguments: {function_args}")
-            print(f"Function ID: {function_id}")
-
-            # Execute the function
-            try:
-                # Parse the JSON arguments
-                args = json.loads(function_args)
-
-                # Call the function with the arguments
-                function_result = tool_functions[function_name](**args)
-                print(f"\n--- Function Result ---\n{function_result}\n")
-
-                # Add tool call to assistant message
-                assistant_tool_calls.append(
-                    {
-                        "id": function_id,
-                        "type": "function",
-                        "function": {"name": function_name, "arguments": function_args},
-                    }
-                )
-
-                # Add tool result to tool_results
-                tool_results.append(
-                    {
-                        "role": "tool",
-                        "tool_call_id": function_id,
-                        "content": function_result,
-                    }
-                )
-
-            except Exception as e:
-                print(f"Error executing function: {e}")
-
-        # Add tool_calls to assistant message
-        assistant_message["tool_calls"] = assistant_tool_calls
-
-        # Create a follow-up message with all function results
-        follow_up_messages = [
-            {"role": "user", "content": original_query},
-            assistant_message,
-        ]
-
-        # Add all tool results to the messages
-        follow_up_messages.extend(tool_results)
-
-        # Get completion with all tool results in a single follow-up
-        follow_up_response = client.chat.completions.create(
-            model=client.models.list().data[0].id,
-            messages=follow_up_messages,
-            stream=False,
-        )
-
-        print("\n--- Follow-up Response ---")
-        print(follow_up_response.choices[0].message.content)
-        print("--- End Follow-up ---\n")
-
-    print("--- End Response ---\n")
-
-
-def run_test_case(query, test_name):
-    """Run a single test case with the given query"""
-    print(f"\n{'=' * 50}\nTEST CASE: {test_name}\n{'=' * 50}")
-    print(f"Query: '{query}'")
-
-    start_time = time.time()
-
-    # Create non-streaming chat completion request
-    response = client.chat.completions.create(
-        model=client.models.list().data[0].id,
-        messages=[{"role": "user", "content": query}],
-        tools=tools,
-        tool_choice="auto",
-        stream=False,
-    )
-
-    # Process the non-streaming response, passing the original query
-    process_response(response, tool_functions, query)
-
-    end_time = time.time()
-    print(f"Test completed in {end_time - start_time:.2f} seconds")
-
-
-def main():
-    # Initialize OpenAI client
-    global client
-    client = OpenAI(
-        api_key=openai_api_key,
-        base_url=openai_api_base,
-    )
-
-    # Run test cases
-    test_cases = [
-        ("I want to know the weather in San Francisco", "Weather Information"),
-        ("Calculate 25 * 17 + 31", "Math Calculation"),
-        ("Translate 'Hello world' to Spanish", "Text Translation"),
-        ("What is the weather in Tokyo and New York in celsius", "Multiple Tool Usage"),
-    ]
-
-    # Execute all test cases
-    for query, test_name in test_cases:
-        run_test_case(query, test_name)
-        time.sleep(1)  # Small delay between tests
-
-    print("\nAll tests completed.")
-
-
-if __name__ == "__main__":
-    main()

examples/online_serving/openai_chat_completion_client_with_tools_xlam_streaming.py

@@ -1,272 +0,0 @@
-# SPDX-License-Identifier: Apache-2.0
-# ruff: noqa: E501
-"""
-Set up this example by starting a vLLM OpenAI-compatible server with tool call
-options enabled for xLAM-2 models:
-
-vllm serve --model Salesforce/Llama-xLAM-2-8b-fc-r --enable-auto-tool-choice --tool-call-parser xlam
-
-OR
-
-vllm serve --model Salesforce/xLAM-2-3b-fc-r --enable-auto-tool-choice --tool-call-parser xlam
-
-This example demonstrates streaming tool calls with xLAM models.
-"""
-
-import json
-import time
-
-from openai import OpenAI
-
-# Modify OpenAI's API key and API base to use vLLM's API server.
-openai_api_key = "empty"
-openai_api_base = "http://localhost:8000/v1"
-
-
-# Define tool functions
-def get_weather(location: str, unit: str):
-    return f"Weather in {location} is 22 degrees {unit}."
-
-
-def calculate_expression(expression: str):
-    try:
-        result = eval(expression)
-        return f"The result of {expression} is {result}"
-    except Exception as e:
-        return f"Could not calculate {expression}: {e}"
-
-
-def translate_text(text: str, target_language: str):
-    return f"Translation of '{text}' to {target_language}: [translated content]"
-
-
-# Define tools
-tools = [
-    {
-        "type": "function",
-        "function": {
-            "name": "get_weather",
-            "description": "Get the current weather in a given location",
-            "parameters": {
-                "type": "object",
-                "properties": {
-                    "location": {
-                        "type": "string",
-                        "description": "City and state, e.g., 'San Francisco, CA'",
-                    },
-                    "unit": {"type": "string", "enum": ["celsius", "fahrenheit"]},
-                },
-                "required": ["location", "unit"],
-            },
-        },
-    },
-    {
-        "type": "function",
-        "function": {
-            "name": "calculate_expression",
-            "description": "Calculate a mathematical expression",
-            "parameters": {
-                "type": "object",
-                "properties": {
-                    "expression": {
-                        "type": "string",
-                        "description": "Mathematical expression to evaluate, needs to be a valid Python expression",
-                    }
-                },
-                "required": ["expression"],
-            },
-        },
-    },
-    {
-        "type": "function",
-        "function": {
-            "name": "translate_text",
-            "description": "Translate text to another language",
-            "parameters": {
-                "type": "object",
-                "properties": {
-                    "text": {"type": "string", "description": "Text to translate"},
-                    "target_language": {
-                        "type": "string",
-                        "description": "Target language for translation",
-                    },
-                },
-                "required": ["text", "target_language"],
-            },
-        },
-    },
-]
-
-# Map of function names to implementations
-tool_functions = {
-    "get_weather": get_weather,
-    "calculate_expression": calculate_expression,
-    "translate_text": translate_text,
-}
-
-
-def process_stream(response, tool_functions, original_query):
-    """Process a streaming response with possible tool calls"""
-    # Track multiple tool calls
-    tool_calls = {}  # Dictionary to store tool calls by ID
-
-    current_id = None
-
-    print("\n--- Stream Output ---")
-    for chunk in response:
-        # Handle tool calls in the stream
-        if chunk.choices[0].delta.tool_calls:
-            for tool_call_chunk in chunk.choices[0].delta.tool_calls:
-                # Get the tool call ID
-                if hasattr(tool_call_chunk, "id") and tool_call_chunk.id:
-                    current_id = tool_call_chunk.id
-                    if current_id not in tool_calls:
-                        tool_calls[current_id] = {
-                            "function_name": None,
-                            "function_args": "",
-                            "function_id": current_id,
-                        }
-
-                # Extract function information as it comes in chunks
-                if (
-                    hasattr(tool_call_chunk, "function")
-                    and current_id
-                    and current_id in tool_calls
-                ):
-                    if (
-                        hasattr(tool_call_chunk.function, "name")
-                        and tool_call_chunk.function.name
-                    ):
-                        tool_calls[current_id]["function_name"] = (
-                            tool_call_chunk.function.name
-                        )
-                        print(f"Function called: {tool_call_chunk.function.name}")
-
-                    if (
-                        hasattr(tool_call_chunk.function, "arguments")
-                        and tool_call_chunk.function.arguments
-                    ):
-                        tool_calls[current_id]["function_args"] += (
-                            tool_call_chunk.function.arguments
-                        )
-                        print(f"Arguments chunk: {tool_call_chunk.function.arguments}")
-
-        # Handle regular content in the stream
-        elif chunk.choices[0].delta.content:
-            print(chunk.choices[0].delta.content, end="")
-
-    print("\n--- End Stream ---\n")
-
-    # Execute each function call and build messages for follow-up
-    follow_up_messages = [{"role": "user", "content": original_query}]
-
-    for tool_id, tool_data in tool_calls.items():
-        function_name = tool_data["function_name"]
-        function_args = tool_data["function_args"]
-        function_id = tool_data["function_id"]
-
-        if function_name and function_args:
-            try:
-                # Parse the JSON arguments
-                args = json.loads(function_args)
-
-                # Call the function with the arguments
-                function_result = tool_functions[function_name](**args)
-                print(
-                    f"\n--- Function Result ({function_name}) ---\n{function_result}\n"
-                )
-
-                # Add the assistant message with tool call
-                follow_up_messages.append(
-                    {
-                        "role": "assistant",
-                        "tool_calls": [
-                            {
-                                "id": function_id,
-                                "type": "function",
-                                "function": {
-                                    "name": function_name,
-                                    "arguments": function_args,
-                                },
-                            }
-                        ],
-                    }
-                )
-
-                # Add the tool message with function result
-                follow_up_messages.append(
-                    {
-                        "role": "tool",
-                        "tool_call_id": function_id,
-                        "content": function_result,
-                    }
-                )
-
-            except Exception as e:
-                print(f"Error executing function: {e}")
-
-    # Only send follow-up if we have results to process
-    if len(follow_up_messages) > 1:
-        # Create a follow-up message with all the function results
-        follow_up_response = client.chat.completions.create(
-            model=client.models.list().data[0].id,
-            messages=follow_up_messages,
-            stream=True,
-        )
-
-        print("\n--- Follow-up Response ---")
-        for chunk in follow_up_response:
-            if chunk.choices[0].delta.content:
-                print(chunk.choices[0].delta.content, end="")
-        print("\n--- End Follow-up ---\n")
-
-
-def run_test_case(query, test_name):
-    """Run a single test case with the given query"""
-    print(f"\n{'=' * 50}\nTEST CASE: {test_name}\n{'=' * 50}")
-    print(f"Query: '{query}'")
-
-    start_time = time.time()
-
-    # Create streaming chat completion request
-    response = client.chat.completions.create(
-        model=client.models.list().data[0].id,
-        messages=[{"role": "user", "content": query}],
-        tools=tools,
-        tool_choice="auto",
-        stream=True,
-    )
-
-    # Process the streaming response
-    process_stream(response, tool_functions, query)
-
-    end_time = time.time()
-    print(f"Test completed in {end_time - start_time:.2f} seconds")
-
-
-def main():
-    # Initialize OpenAI client
-    global client
-    client = OpenAI(
-        api_key=openai_api_key,
-        base_url=openai_api_base,
-    )
-
-    # Run test cases
-    test_cases = [
-        ("I want to know the weather in San Francisco", "Weather Information"),
-        ("Calculate 25 * 17 + 31", "Math Calculation"),
-        ("Translate 'Hello world' to Spanish", "Text Translation"),
-        ("What is the weather in Tokyo and New York in celsius", "Multiple Tool Usage"),
-    ]
-
-    # Execute all test cases
-    for query, test_name in test_cases:
-        run_test_case(query, test_name)
-        time.sleep(1)  # Small delay between tests
-
-    print("\nAll tests completed.")
-
-
-if __name__ == "__main__":
-    main()

examples/online_serving/openai_transcription_client.py

@@ -1,23 +1,5 @@
 # SPDX-License-Identifier: Apache-2.0
 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project
-"""
-This script demonstrates how to use the vLLM API server to perform audio
-transcription with the `openai/whisper-large-v3` model.
-
-Before running this script, you must start the vLLM server with the following command:
-
-    vllm serve openai/whisper-large-v3
-
-Requirements:
-- vLLM with audio support
-- openai Python SDK
-- httpx for streaming support
-
-The script performs:
-1. Synchronous transcription using OpenAI-compatible API.
-2. Streaming transcription using raw HTTP request to the vLLM server.
-"""
-
 import asyncio
 import json
 
@@ -39,9 +21,6 @@ client = OpenAI(
 
 
 def sync_openai():
-    """
-    Perform synchronous transcription using OpenAI-compatible API.
-    """
     with open(str(mary_had_lamb), "rb") as f:
         transcription = client.audio.transcriptions.create(
             file=f,
@@ -58,11 +37,11 @@ def sync_openai():
         print("transcription result:", transcription.text)
 
 
+sync_openai()
+
+
 # OpenAI Transcription API client does not support streaming.
 async def stream_openai_response():
-    """
-    Perform streaming transcription using vLLM's raw HTTP streaming API.
-    """
     data = {
         "language": "en",
         "stream": True,
@@ -89,15 +68,7 @@ async def stream_openai_response():
                         # Extract and print the content
                         content = chunk["choices"][0].get("delta", {}).get("content")
                         print(content, end="")
-    print()  # Final newline after stream ends
 
 
-def main():
-    sync_openai()
-
 # Run the asynchronous function
 asyncio.run(stream_openai_response())
-
-
-if __name__ == "__main__":
-    main()