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Update README.md
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README.md
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- chat
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# Qwen2-
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## Introduction
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Compared with the state-of-the-art opensource language models, including the previous released Qwen1.5, Qwen2 has generally surpassed most opensource models and demonstrated competitiveness against proprietary models across a series of benchmarks targeting for language understanding, language generation, multilingual capability, coding, mathematics, reasoning, etc.
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Qwen2-
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For more details, please refer to our [blog](https://qwenlm.github.io/blog/qwen2/) and [GitHub](https://github.com/QwenLM/Qwen2).
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<br>
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3. **Model Deployment**: Utilize vLLM to deploy your model. For instance, you can set up an openAI-like server using the command:
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```bash
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python -m vllm.entrypoints.openai.api_server --served-model-name Qwen2-
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```
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Then you can access the Chat API by:
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curl http://localhost:8000/v1/chat/completions \
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-H "Content-Type: application/json" \
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-d '{
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"model": "Qwen2-
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"messages": [
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{"role": "system", "content": "You are a helpful assistant."},
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{"role": "user", "content": "Your Long Input Here."}
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- chat
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---
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# Qwen2-57B-A14B-Instruct
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## Introduction
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Compared with the state-of-the-art opensource language models, including the previous released Qwen1.5, Qwen2 has generally surpassed most opensource models and demonstrated competitiveness against proprietary models across a series of benchmarks targeting for language understanding, language generation, multilingual capability, coding, mathematics, reasoning, etc.
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Qwen2-57B-A14B-Instruct supports a context length of up to 65,536 tokens, enabling the processing of extensive inputs. Please refer to [this section](#processing-long-texts) for detailed instructions on how to deploy Qwen2 for handling long texts.
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For more details, please refer to our [blog](https://qwenlm.github.io/blog/qwen2/) and [GitHub](https://github.com/QwenLM/Qwen2).
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<br>
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3. **Model Deployment**: Utilize vLLM to deploy your model. For instance, you can set up an openAI-like server using the command:
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```bash
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python -m vllm.entrypoints.openai.api_server --served-model-name Qwen2-57B-A14B-Instruct --model path/to/weights
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```
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Then you can access the Chat API by:
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curl http://localhost:8000/v1/chat/completions \
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-H "Content-Type: application/json" \
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-d '{
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"model": "Qwen2-57B-A14B-Instruct",
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"messages": [
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{"role": "system", "content": "You are a helpful assistant."},
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{"role": "user", "content": "Your Long Input Here."}
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