vllm

name	vllm
description	Complete vLLM v0.19.0 Python API reference for high-throughput LLM inference: offline batch generation, chat, embeddings, classification, structured outputs, LoRA adapters, multimodal inputs, and OpenAI-compatible server. Triggers on: vLLM questions, Python LLM serving, GPU inference, "how do I use vllm", batch inference, vllm serve, OpenAI-compatible API, structured JSON output, LoRA serving.

vLLM v0.19.0 Python API Guide

Comprehensive reference for vLLM -- a high-throughput, memory-efficient inference engine for large language models.

Overview

vLLM provides two main interfaces:

1. Offline inference via the LLM class -- batch processing with automatic memory management
2. Online serving via vllm serve -- OpenAI-compatible REST API with streaming

Key capabilities:

• High-throughput batched generation with PagedAttention
• OpenAI-compatible chat completions, completions, and embeddings API
• Structured outputs (JSON schema, regex, grammar, choice)
• LoRA adapter hot-swapping
• Multimodal inputs (images, audio, video)
• Tensor, pipeline, and data parallelism
• Quantization (AWQ, GPTQ, FP8, and more)
• Prefix caching for shared prompt prefixes
• Tool calling / function calling

Quick Start

from vllm import LLM, SamplingParams

llm = LLM(model="meta-llama/Llama-3.1-8B-Instruct")
params = SamplingParams(temperature=0.8, max_tokens=256)

# Text completion (no chat template)
outputs = llm.generate(["The future of AI is"], params)
print(outputs[0].outputs[0].text)

# Chat completion (applies chat template)
outputs = llm.chat(
    [{"role": "user", "content": "What is vLLM?"}],
    sampling_params=params,
)
print(outputs[0].outputs[0].text)

When to Use This Skill

• Writing Python code that uses vLLM for inference
• Configuring vllm serve for production deployment
• Using structured outputs (JSON, regex, grammar)
• Setting up LoRA adapter serving
• Passing multimodal inputs (images, audio, video)
• Tuning sampling parameters
• Understanding output types and result handling
• Migrating from vLLM v0.16.x to v0.19.0

Core Concepts

LLM Class

The main offline inference API. Created with a model name/path, automatically manages GPU memory and batching.

from vllm import LLM
llm = LLM(model="meta-llama/Llama-3.1-8B-Instruct")

SamplingParams

Controls generation behavior: temperature, top-p, max tokens, stop sequences, structured outputs, etc.

from vllm import SamplingParams
params = SamplingParams(temperature=0.7, max_tokens=256, top_p=0.95)

generate() vs chat()

Critical distinction:

• generate() takes raw text prompts. Does NOT apply chat templates.
• chat() takes message lists (role/content dicts). Applies the model's chat template automatically.

Use chat() for conversational interactions. Use generate() for raw text completion.

generation_config.json

vLLM reads generation_config.json from HuggingFace models by default, which may override SamplingParams defaults. To disable: pass generation_config="vllm" to LLM() or --generation-config vllm to vllm serve.

StructuredOutputsParams

Imported separately. Constrains output to JSON schema, regex, choice list, or grammar.

from vllm.sampling_params import StructuredOutputsParams

params = SamplingParams(
    structured_outputs=StructuredOutputsParams(
        json={"type": "object", "properties": {"name": {"type": "string"}}}
    )
)

Output Types

Method	Returns	Key Field
generate() / chat()	list[RequestOutput]	.outputs[0].text
embed()	list[EmbeddingRequestOutput]	.outputs.embedding
classify()	list[ClassificationRequestOutput]	.outputs.probs
score()	list[ScoringRequestOutput]	.outputs.score

API Reference Files

File	Content	Lines
references/api-llm.md	LLM class constructor, all public methods, PromptType variants	~380
references/api-sampling.md	SamplingParams, StructuredOutputsParams, BeamSearchParams, all output types, PoolingParams	~330
references/api-server.md	vllm serve CLI flags, REST endpoints, OpenAI client usage, auth, tools, structured outputs	~370
references/api-lora.md	LoRARequest class, offline/server LoRA usage, multi-LoRA, limitations	~190
references/api-multimodal.md	Image/audio/video inputs, offline and server API, model-specific formats	~250
references/workflows.md	Complete working examples for all major use cases	~420

Key Gotchas

1. generate() does not apply chat templates. Use chat() for conversations. Using generate() with a chat model without the template will produce poor results.

2. generation_config.json changes defaults. HuggingFace models ship with generation_config.json that vLLM reads by default. This can change temperature, max_tokens, and other defaults. Pass generation_config="vllm" to disable.

3. StructuredOutputsParams is not in vllm top-level. Import from vllm.sampling_params:

   from vllm.sampling_params import StructuredOutputsParams
   

4. max_tokens defaults to 16. This is intentionally low. Always set it explicitly for production use.

5. Greedy sampling forces n=1. When temperature=0, you cannot set n > 1.

6. Pooling models need runner="pooling". For embed(), classify(), and score(), the model must be loaded with runner="pooling" (or auto-detected).

7. score() task is deprecated. The "score" pooling task is deprecated in v0.19.0. Use "classify" instead. Will be removed in v0.20.

8. LoRA needs enable_lora=True. Must be set at LLM() construction time. Cannot be enabled after.

9. swap_space is deprecated. The parameter is accepted but ignored with a warning.

10. Multimodal placeholders are model-specific. When using generate() with images, the placeholder token (e.g., <image>) varies by model. Use chat() for automatic handling.

What's New in v0.19.0 (vs v0.16.0)

New Features

• enqueue() / wait_for_completion() -- Decouple request submission from result collection
• reward() method -- Generate reward scores via token-level classification
• RepetitionDetectionParams -- Detect and terminate repetitive N-gram patterns early
• thinking_token_budget in SamplingParams -- Control thinking token limits for reasoning models
• flat_logprobs option -- High-performance flat logprob format (FlatLogprobs)
• sleep() / wake_up() -- Engine lifecycle management with multi-level sleep (levels 0, 1, 2)
• collective_rpc() / apply_model() -- Direct worker and model access
• get_metrics() -- Retrieve Prometheus metrics snapshot
• Tool server integration -- --tool-server flag for external MCP tool servers
• Anthropic Messages API -- /v1/messages endpoint
• Responses API -- /v1/responses endpoint (OpenAI Responses format)
• Cohere embedding API -- /v2/embed endpoint
• structural_tag constraint in StructuredOutputsParams
• Late interaction scoring -- ColBERT-style MaxSim scoring in score()
• default_chat_template_kwargs -- Server-level defaults for chat template (useful for reasoning models)
• runner and convert params -- Explicit control over runner type and model conversion
• Weight transfer -- init_weight_transfer_engine() / update_weights() for RL training
• Prefetch offloading -- offload_group_size, offload_num_in_group, offload_prefetch_step, offload_params
• kv_cache_memory_bytes -- Fine-grained KV cache memory control
• logits_processors -- Custom logits processor support
• tokens_only mode -- Disaggregated serving token endpoint
• Render endpoints -- /v1/chat/completions/render and /v1/completions/render
• Batch chat completions -- /v1/chat/completions/batch

Breaking Changes

• V1 engine is now default. The legacy LLMEngine is replaced by vllm.v1.engine.llm_engine.LLMEngine.
• swap_space parameter deprecated -- accepted but ignored with a warning.
• score pooling task deprecated -- use classify instead. Will be removed in v0.20.
• Pooling multitask deprecated -- specifying a different task than the model's default triggers a deprecation warning. Will be removed in v0.20. Use PoolerConfig(task=...) instead.

Removed

• The legacy V0 engine code paths.
• Direct LLMEngine instantiation in LLM class (now always uses LLMEngine.from_engine_args()).

Troubleshooting

Out of Memory (OOM)

• Lower gpu_memory_utilization (default 0.9)
• Reduce max_model_len
• Use quantization (quantization="awq" or "gptq")
• Use cpu_offload_gb for partial CPU offloading
• Use tensor parallelism (tensor_parallel_size=2)

Poor Output Quality

• Check if generation_config.json is overriding your params. Use generation_config="vllm" to disable.
• For chat models, use chat() not generate()
• Increase max_tokens from the default of 16
• Ensure the chat template matches the model

Structured Output Failures

• Verify your JSON schema is valid
• Try a different backend: structured_outputs_config={"backend": "outlines"}
• grammar cannot be an empty string
• choice cannot be an empty list
• Mistral tokenizers do not support "guidance" or "lm-format-enforcer" backends

LoRA Not Working

• Ensure enable_lora=True at LLM construction
• lora_int_id must be > 0
• lora_path must not be empty
• Adapter rank must not exceed max_lora_rank

Server Connection Issues

• Default port is 8000, check with --port
• Health check: GET /health
• API key required? Check --api-key or VLLM_API_KEY

Resources

• references/api-llm.md -- LLM class full reference
• references/api-sampling.md -- SamplingParams and output types
• references/api-server.md -- OpenAI-compatible server
• references/api-lora.md -- LoRA adapters
• references/api-multimodal.md -- Multimodal inputs
• references/workflows.md -- Complete working examples