huggingface/transformers · error · HTTPException

Server is pinned to '{self.model_manager.force_model}'; requ

Error message

Server is pinned to '{self.model_manager.force_model}'; requested '{requested}'.

What it means

HTTP 400 from _resolve_model when the server was launched pinned to a single model (force_model) and the request explicitly names a different one. A pinned server rewrites body['model'] to the forced model for consistency, but refuses silently serving the wrong model when the client asked for another.

Source

Thrown at src/transformers/cli/serving/utils.py:1114

    @staticmethod
    def chunk_to_sse(chunk: "str | pydantic.BaseModel") -> str:
        """Format a pydantic model or string as an SSE ``data:`` line."""
        if isinstance(chunk, str):
            return chunk if chunk.startswith("data: ") else f"data: {chunk}\n\n"
        return f"data: {chunk.model_dump_json(exclude_none=True)}\n\n"

    def _resolve_model(self, body: dict) -> tuple[str, "PreTrainedModel", "ProcessorMixin | PreTrainedTokenizerFast"]:
        """Apply force_model, load model + processor.

        Returns ``(model_id, model, processor)``.
        """
        from fastapi import HTTPException

        if self.model_manager.force_model is not None:
            requested = body.get("model")
            if requested is not None and requested != self.model_manager.force_model:
                raise HTTPException(
                    status_code=400,
                    detail=(f"Server is pinned to '{self.model_manager.force_model}'; requested '{requested}'."),
                )
            body["model"] = self.model_manager.force_model

        model_id = self.model_manager.process_model_name(body["model"])
        model, processor = self.model_manager.load_model_and_processor(model_id)

        return model_id, model, processor

    def _build_generation_config(
        self, body: dict, model_generation_config: "GenerationConfig", use_cb: bool = False
    ) -> "GenerationConfig":
        """Build a GenerationConfig from shared params (temperature, top_p, seed, generation_config JSON).

        Subclasses should call ``super()._build_generation_config(...)`` then apply
        endpoint-specific params (``max_tokens``, ``max_output_tokens``, etc.).

View on GitHub (pinned to a597f97485)

Solutions

1. Set the request's model field to the pinned model id, matching the server's launch flag
2. Or omit the model field entirely — the server fills in the forced model
3. Or restart the server without the pin if multi-model dispatch is actually required

Example fix

# before
$ transformers serve --model openai-community/gpt2
client.post(body={'model': 'meta-llama/Llama-3.1-8B', ...})
# after
client.post(body={'model': 'openai-community/gpt2', ...})

if pinned_model is not None:  # discovered from server config/docs endpoint
    body['model'] = pinned_model
resp = client.post('/v1/chat/completions', json=body)

if resp.status_code == 400 and 'pinned to' in resp.text:
    pinned = re.search(r"pinned to '([^']+)'", resp.text).group(1)
    body['model'] = pinned
    resp = client.post('/v1/chat/completions', json=body)

When it happens

Trigger: Start the CLI with a forced model flag (e.g. transformers serve --model <id> pinning the server), then POST a chat/completions body whose 'model' field is a different model id.

Common situations: Reusing an OpenAI-compatible client configured with 'gpt-3.5-turbo' against a pinned local server; load balancers routing requests from mixed clients to one pinned replica; forgetting the deployment is pinned.

Related errors

• prompt must be a string.
• 'input' must be a string or list
• Unsupported input item type: {item_type!r}
• Missing `model` field in the request body.
• Unexpected fields in the request: {unexpected}

AI-assisted analysis of huggingface/transformers@a597f97485 (2026-08-14). Data as JSON: /api/errors/edc93eca445faff3. Report an issue: GitHub.