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Transformer models can be efficiently deployed using libraries such as vLLM, Text Generation Inference (TGI), and others. These libraries are designed for production-grade user-facing services, and can scale to multiple servers and millions of concurrent users. Refer to Transformers as Backend for Inference Servers for usage examples.

Responses API is now supported as an experimental API! Read more about it here.

You can also serve transformer models with the transformers serve CLI. With Continuous Batching, serve now delivers solid throughput and latency well suited for evaluation, experimentation, and moderate-load local or self-hosted deployments. While vLLM, SGLang, or other inference engines remain our recommendations for large-scale production, serve avoids the extra runtime and operational overhead, and is on track to gain more production-oriented features.

In this document, we dive into the different supported endpoints and modalities; we also cover the setup of several user interfaces that can be used on top of transformers serve in the following guides:

Serve CLI

This section is experimental and subject to change in future versions

You can serve models of diverse modalities supported by transformers with the transformers serve CLI. It spawns a local server that offers compatibility with the OpenAI SDK, which is the de facto standard for LLM conversations and other related tasks. This way, you can use the server from many third party applications, or test it using the transformers chat CLI (docs).

The server supports the following REST APIs:

  • /v1/chat/completions
  • /v1/responses
  • /v1/audio/transcriptions
  • /v1/models

To launch a server, simply use the transformers serve CLI command:

transformers serve

The simplest way to interact with the server is through our transformers chat CLI

transformers chat localhost:8000 --model-name-or-path Qwen/Qwen3-4B

or by sending an HTTP request, like we’ll see below.

Chat Completions - text-based

See below for examples for text-based requests. Both LLMs and VLMs should handle

curl
python - huggingface_hub
python - openai 
curl -X POST http://localhost:8000/v1/chat/completions -H "Content-Type: application/json" -d '{"messages": [{"role": "system", "content": "hello"}], "temperature": 0.9, "max_tokens": 1000, "stream": true, "model": "Qwen/Qwen2.5-0.5B-Instruct"}'

from which you’ll receive multiple chunks in the Completions API format

data: {"object": "chat.completion.chunk", "id": "req_0", "created": 1751377863, "model": "Qwen/Qwen2.5-0.5B-Instruct", "system_fingerprint": "", "choices": [{"delta": {"role": "assistant", "content": "", "tool_call_id": null, "tool_calls": null}, "index": 0, "finish_reason": null, "logprobs": null}]}

data: {"object": "chat.completion.chunk", "id": "req_0", "created": 1751377863, "model": "Qwen/Qwen2.5-0.5B-Instruct", "system_fingerprint": "", "choices": [{"delta": {"role": "assistant", "content": "", "tool_call_id": null, "tool_calls": null}, "index": 0, "finish_reason": null, "logprobs": null}]}

(...)

Chat Completions - VLMs

The Chat Completion API also supports images; see below for examples for text-and-image-based requests.

curl
python - huggingface_hub
python - openai 
curl http://localhost:8000/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{
    "model": "Qwen/Qwen2.5-VL-7B-Instruct",
    "stream": true,
    "messages": [
      {
        "role": "user",
        "content": [
          {
            "type": "text",
            "text": "What is in this image?"
          },
          {
            "type": "image_url",
            "image_url": {
              "url": "https://upload.wikimedia.org/wikipedia/commons/thumb/d/dd/Gfp-wisconsin-madison-the-nature-boardwalk.jpg/2560px-Gfp-wisconsin-madison-the-nature-boardwalk.jpg"
            }
          }
        ]
      }
    ],
    "max_tokens": 300
  }'

from which you’ll receive multiple chunks in the Completions API format

data: {"id":"req_0","choices":[{"delta":{"role":"assistant"},"index":0}],"created":1753366665,"model":"Qwen/Qwen2.5-VL-7B-Instruct@main","object":"chat.completion.chunk","system_fingerprint":""}

data: {"id":"req_0","choices":[{"delta":{"content":"The "},"index":0}],"created":1753366701,"model":"Qwen/Qwen2.5-VL-7B-Instruct@main","object":"chat.completion.chunk","system_fingerprint":""}

data: {"id":"req_0","choices":[{"delta":{"content":"image "},"index":0}],"created":1753366701,"model":"Qwen/Qwen2.5-VL-7B-Instruct@main","object":"chat.completion.chunk","system_fingerprint":""}

Responses API

The Responses API is the newest addition to the supported APIs of transformers serve.

This API is still experimental: expect bug patches and additition of new features in the coming weeks. If you run into any issues, please let us know and we’ll work on fixing them ASAP.

Instead of the previous /v1/chat/completions path, the Responses API lies behind the /v1/responses path. See below for examples interacting with our Responses endpoint with curl, as well as the Python OpenAI client.

So far, this endpoint only supports text and therefore only LLMs. VLMs to come!

curl
python - openai 
curl http://localhost:8000/v1/responses \
  -H "Content-Type: application/json" \
  -d '{
    "model": "Qwen/Qwen2.5-0.5B-Instruct",
    "stream": true,
    "input": "Tell me a three sentence bedtime story about a unicorn."
  }'

from which you’ll receive multiple chunks in the Responses API format

data: {"response":{"id":"resp_req_0","created_at":1754059817.783648,"model":"Qwen/Qwen2.5-0.5B-Instruct@main","object":"response","output":[],"parallel_tool_calls":false,"tool_choice":"auto","tools":[],"status":"queued","text":{"format":{"type":"text"}}},"sequence_number":0,"type":"response.created"}

data: {"response":{"id":"resp_req_0","created_at":1754059817.783648,"model":"Qwen/Qwen2.5-0.5B-Instruct@main","object":"response","output":[],"parallel_tool_calls":false,"tool_choice":"auto","tools":[],"status":"in_progress","text":{"format":{"type":"text"}}},"sequence_number":1,"type":"response.in_progress"}

data: {"item":{"id":"msg_req_0","content":[],"role":"assistant","status":"in_progress","type":"message"},"output_index":0,"sequence_number":2,"type":"response.output_item.added"}

data: {"content_index":0,"item_id":"msg_req_0","output_index":0,"part":{"annotations":[],"text":"","type":"output_text"},"sequence_number":3,"type":"response.content_part.added"}

data: {"content_index":0,"delta":"","item_id":"msg_req_0","output_index":0,"sequence_number":4,"type":"response.output_text.delta"}

data: {"content_index":0,"delta":"Once ","item_id":"msg_req_0","output_index":0,"sequence_number":5,"type":"response.output_text.delta"}

data: {"content_index":0,"delta":"upon ","item_id":"msg_req_0","output_index":0,"sequence_number":6,"type":"response.output_text.delta"}

data: {"content_index":0,"delta":"a ","item_id":"msg_req_0","output_index":0,"sequence_number":7,"type":"response.output_text.delta"}

MCP integration

The transformers serve server is also an MCP client, so it can interact with MCP tools in agentic use cases. This, of course, requires the use of an LLM that is designed to use tools.

At the moment, MCP tool usage in transformers is limited to the qwen family of models.

Continuous Batching

Continuous Batching (CB) lets the server dynamically group and interleave requests so they can share forward passes on the GPU. Instead of processing each request sequentially, serve adds new requests as others progress (prefill) and drops finished ones during decode. The result is significantly higher GPU utilization and better throughput without sacrificing latency for most workloads.

Thanks to this, evaluation, experimentation, and moderate-load local/self-hosted use can now be handled comfortably by transformers serve without introducing an extra runtime to operate.

Enable CB in serve

CB is opt-in and currently applies to chat completions.

transformers serve \
  --continuous-batching
  --attn_implementation sdpa_paged

Performance tips

  • Use an efficient attention backend when available:
transformers serve \
  --continuous_batching \
  --attn_implementation paged_attention

If you choose paged_attention, you must install flash-attn separately: pip install flash-attn --no-build-isolation

  • --dtype {bfloat16|float16} typically improve throughput and memory use vs. float32

  • --load_in_4bit/--load_in_8bit can reduce memory footprint for LoRA setups

  • --force-model <repo_id> avoids per-request model hints and helps produce stable, repeatable runs

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