wehub-resource-sync
71 lines
2.4 KiB
Markdown
71 lines
2.4 KiB
Markdown
# Context Extension
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!!! note
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The `--rope-scaling` parameter used in older versions of vLLM is no longer supported. Please use the `--hf-overrides` method with `rope_parameters` instead.
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This directory contains examples for extending the context length of models using vLLM.
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## Offline Inference Example
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The [`context_extension.py`](../../examples/features/context_extension/context_extension_offline.py) script demonstrates how to extend the context length of a Qwen model using the YARN method (rope_parameters) and run a simple chat example.
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### Usage
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```bash
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python examples/features/context_extension/context_extension_offline.py
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```
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## OpenAI Online Method
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You can also use vLLM's OpenAI-compatible API to serve models with extended context length.
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### Usage
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Run the vLLM server with the following command to extend the context length using YARN:
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```bash
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vllm serve Qwen/Qwen3-0.6B \
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--hf-overrides '{"rope_parameters": {"factor": 4.0, "original_max_position_embeddings": 32768, "rope_theta": 1000000, "rope_type": "yarn"}}' \
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--max-model-len 131072
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```
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### Client Example
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After starting the server, you can use the OpenAI Python client to interact with it:
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```python
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from openai import OpenAI
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client = OpenAI(
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base_url="http://localhost:8000/v1",
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api_key="token-abc123" # Dummy API key, required by the client
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)
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response = client.chat.completions.create(
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model="Qwen/Qwen3-0.6B",
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messages=[
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{"role": "system", "content": "You are a helpful assistant"},
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{"role": "user", "content": "Hello"}
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],
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max_tokens=128,
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temperature=0.8,
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top_p=0.95
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)
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print(response.choices[0].message.content)
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```
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### Key Parameters
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The available parameters depend on the `rope_type` you choose. For detailed information about all supported RoPE types and their specific parameters, please refer to the [Hugging Face Transformers RoPE documentation](https://huggingface.co/docs/transformers/main/en/internal/rope_utils#transformers.RopeParameters).
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Common parameters include:
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- `rope_type`: The type of RoPE implementation (e.g., "yarn", "linear", "dynamic")
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- `factor`: The factor by which to extend the context length
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- `original_max_position_embeddings`: The original maximum position embeddings of the model
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The following parameters are specific to vLLM:
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- `max_model_len`: The new maximum sequence length after extension (original * factor).
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Used for KV cache pre‑allocation and request limit at serving time.
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