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+---
+layout: post
+title: "Transformers backend integration in vLLM"
+author: "The Hugging Face Team"
+image: /assets/figures/transformers-backend/transformers-backend.png
+thumbnail-img: /assets/figures/transformers-backend/transformers-backend.png
+share-img: /assets/figures/transformers-backend/transformers-backend.png
+---
+
+The [Hugging Face Transformers library](https://huggingface.co/docs/transformers/main/en/index)
+offers a flexible, unified interface to a vast ecosystem of model architectures. From research to
+fine-tuning on custom dataset, transformers is the go-to toolkit for all.
+
+But when it comes to *deploying* these models at scale, inference speed and efficiency often take
+center stage. Enter [vLLM](https://docs.vllm.ai/en/latest/), a library engineered for high-throughput
+inference, pulling models from the Hugging Face Hub and optimizing them for production-ready performance.
+
+A recent addition to the vLLM codebase enables leveraging transformers as a backend to run models.
+vLLM will therefore optimize throughput/latency on top of existing transformers architectures.
+In this post, we’ll explore how vLLM leverages the transformers backend to combine **flexibility**
+with **efficiency**, enabling you to deploy state-of-the-art models faster and smarter.
+
+## Transformers and vLLM: Inference in Action
+
+Let’s start with a simple text generation task using the `meta-llama/Llama-3.2-1B` model to see how
+these libraries stack up.
+
+**Infer with transformers**
+
+The transformers library shines in its simplicity and versatility. Using its `pipeline` API, inference is a breeze:
+
+```py
+from transformers import pipeline
+
+pipe = pipeline("text-generation", model="meta-llama/Llama-3.2-1B")
+result = pipe("The future of AI is")
+
+print(result[0]["generated_text"])
+```
+
+This approach is perfect for prototyping or small-scale tasks, but it’s not optimized for high-volume
+inference or low-latency deployment.
+
+**Infer with vLLM**
+
+vLLM takes a different track, prioritizing efficiency with features like `PagedAttention`
+(a memory-efficient attention mechanism) and dynamic batching. Here’s the same task in vLLM:
+
+```py
+from vllm import LLM, SamplingParams
+
+llm = LLM(model="meta-llama/Llama-3.2-1B")
+params = SamplingParams(max_tokens=20)
+outputs = llm.generate("The future of AI is", sampling_params=params)
+print(f"Generated text: {outputs[0].outputs[0].text}")
+```
+
+vLLM’s inference is noticeably faster and more resource-efficient, especially under load.
+For example, it can handle thousands of requests per second with lower GPU memory usage.
+
+## vLLM’s Deployment Superpower: OpenAI Compatibility
+
+Beyond raw performance, vLLM offers an OpenAI-compatible API, making it a drop-in replacement for
+external services. Launch a server:
+
+```bash
+vllm serve meta-llama/Llama-3.2-1B
+```
+
+Then query it with curl:
+
+```bash
+curl http://localhost:8000/v1/completions \
+  -H "Content-Type: application/json" \
+  -d '{"model": "meta-llama/Llama-3.2-1B", "prompt": "San Francisco is a", "max_tokens": 7, "temperature": 0}'
+```
+
+Or use Python’s OpenAI client:
+
+```py
+from openai import OpenAI
+
+client = OpenAI(api_key="EMPTY", base_url="http://localhost:8000/v1")
+completion = client.completions.create(
+    model="meta-llama/Llama-3.2-1B",
+    prompt="San Francisco is a",
+    max_tokens=7,
+    temperature=0
+)
+print("Completion result:", completion.choices[0].text)
+```
+
+This compatibility slashes costs and boosts control, letting you scale inference locally with vLLM’s optimizations.
+
+## Why do we need the transformers backend?
+
+The transformers library is optimized for contributions and
+[addition of new models](https://huggingface.co/docs/transformers/en/add_new_model). Adding a new
+model to vLLM on the other hand is a little
+[more involved](https://docs.vllm.ai/en/latest/contributing/model/index.html).
+
+In the **ideal world**, we would be able to use the new model in vLLM as soon as it is added to
+transformers. With the integration of the transformers backend, we step towards that ideal world.
+
+Here is the [official documentation](https://docs.vllm.ai/en/latest/models/supported_models.html#remote-code)
+on how to make your transformers model compatible with vLLM for the integration to kick in.
+We followed this and made `modeling_gpt2.py` compatible with the integration! You can follow the
+changes in this [transformers pull request](https://github.com/huggingface/transformers/pull/36934).
+
+For a model already in transformers (and compatible with vLLM), this is what we would need to:
+
+```py
+llm = LLM(model="new-transformers-model", model_impl="transformers")
+```
+
+> [!NOTE]
+> It is not a strict necessity to add `model_impl` parameter. vLLM switches to the transformers
+> implementation on its own if the model is not natively supported in vLLM.
+
+Or for a custom model from the Hugging Face Hub:
+
+```py
+llm = LLM(model="custom-hub-model", model_impl="transformers", trust_remote_code=True)
+```
+
+This backend acts as a **bridge**, marrying transformers’ plug-and-play flexibility with vLLM’s
+inference prowess. You get the best of both worlds: rapid prototyping with transformers
+and optimized deployment with vLLM.
+
+## Case Study: Helium
+
+[Kyutai Team’s Helium](https://huggingface.co/docs/transformers/en/model_doc/helium) is not yet supported by vLLM. You might want to run optimized inference on the model with vLLM, and this is where the transformers backend shines.
+
+Let’s see this in action:
+
+```bash
+vllm serve kyutai/helium-1-preview-2b --model-impl transformers
+```
+
+Query it with the OpenAI API:
+
+```py
+from openai import OpenAI
+
+openai_api_key = "EMPTY"
+openai_api_base = "http://localhost:8000/v1"
+
+client = OpenAI(
+    api_key=openai_api_key,
+    base_url=openai_api_base,
+)
+
+completion = client.completions.create(model="kyutai/helium-1-preview-2b", prompt="What is AI?")
+print("Completion result:", completion)
+```
+
+Here, vLLM efficiently processes inputs, leveraging the transformers backend to load
+`kyutai/helium-1-preview-2b` seamlessly. Compared to running this natively in transformers,
+vLLM delivers lower latency and better resource utilization.
+
+By pairing Transformers’ model ecosystem with vLLM’s inference optimizations, you unlock a workflow
+that’s both flexible and scalable. Whether you’re prototyping a new model, deploying a custom
+creation, or scaling a multimodal app, this combination accelerates your path from research to production.
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