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50 lines
2.5 KiB
Markdown
50 lines
2.5 KiB
Markdown
<!--Copyright 2025 The HuggingFace Team. All rights reserved.
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Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
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the License. You may obtain a copy of the License at
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http://www.apache.org/licenses/LICENSE-2.0
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Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
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an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
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specific language governing permissions and limitations under the License.
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⚠️ Note that this file is in Markdown but contain specific syntax for our doc-builder (similar to MDX) that may not be
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rendered properly in your Markdown viewer.
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-->
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# vLLM
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[vLLM](https://github.com/vllm-project/vllm) is a high-throughput inference engine for serving LLMs at scale. It continuously batches requests and keeps KV cache memory compact with PagedAttention.
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Set `model_impl="transformers"` to load a model using the Transformers modeling backend.
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```py
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from vllm import LLM
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llm = LLM(model="meta-llama/Llama-3.2-1B", model_impl="transformers")
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print(llm.generate(["The capital of France is"]))
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```
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Pass `--model-impl transformers` to the `vllm serve` command for online serving.
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```bash
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vllm serve meta-llama/Llama-3.2-1B \
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--task generate \
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--model-impl transformers
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```
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## Transformers integration
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1. [`AutoConfig.from_pretrained`] loads the model's `config.json` from the Hub or your Hugging Face cache. vLLM checks the `architectures` field against its internal model registry to determine which vLLM model class to use.
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2. If the model isn't in the registry, vLLM calls [`AutoModel.from_config`] to load the Transformers model implementation instead.
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3. [`AutoTokenizer.from_pretrained`] loads the tokenizer files. vLLM caches some tokenizer internals to reduce overhead during inference.
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4. Model weights download from the Hub in safetensors format.
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Setting `model_impl="transformers"` bypasses the vLLM model registry and loads directly from Transformers. vLLM replaces most model modules (MoE, attention, linear layers) with its own optimized versions while keeping the Transformers model structure.
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## Resources
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- [vLLM docs](https://docs.vllm.ai/en/latest/models/supported_models.html#transformers) for more usage examples and tips.
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- [Integration with Hugging Face](https://docs.vllm.ai/en/latest/design/huggingface_integration/) explains how vLLM integrates with Transformers. |