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--- |
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base_model: microsoft/Phi-3.5-mini-instruct |
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language: |
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- multilingual |
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library_name: transformers |
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license: mit |
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license_link: https://huggingface.co/microsoft/Phi-3.5-mini-instruct/resolve/main/LICENSE |
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pipeline_tag: text-generation |
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tags: |
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- nlp |
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- code |
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- mlc-ai |
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- MLC-Weight-Conversion |
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widget: |
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- messages: |
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- role: user |
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content: Can you provide ways to eat combinations of bananas and dragonfruits? |
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--- |
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--- |
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library_name: mlc-llm |
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base_model: microsoft/Phi-3.5-mini-instruct |
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tags: |
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- mlc-llm |
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- web-llm |
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--- |
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# AMKCode/Phi-3.5-mini-instruct-q4f16_1-MLC |
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This is the [Phi-3.5-mini-instruct](https://huggingface.co/microsoft/Phi-3.5-mini-instruct) model in MLC format `q4f16_1`. |
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The conversion was done using the [MLC-Weight-Conversion](https://huggingface.co/spaces/mlc-ai/MLC-Weight-Conversion) space. |
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The model can be used for projects [MLC-LLM](https://github.com/mlc-ai/mlc-llm) and [WebLLM](https://github.com/mlc-ai/web-llm). |
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## Example Usage |
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Here are some examples of using this model in MLC LLM. |
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Before running the examples, please install MLC LLM by following the [installation documentation](https://llm.mlc.ai/docs/install/mlc_llm.html#install-mlc-packages). |
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### Chat |
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In command line, run |
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```bash |
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mlc_llm chat HF://mlc-ai/AMKCode/Phi-3.5-mini-instruct-q4f16_1-MLC |
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``` |
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### REST Server |
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In command line, run |
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```bash |
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mlc_llm serve HF://mlc-ai/AMKCode/Phi-3.5-mini-instruct-q4f16_1-MLC |
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``` |
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### Python API |
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```python |
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from mlc_llm import MLCEngine |
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# Create engine |
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model = "HF://mlc-ai/AMKCode/Phi-3.5-mini-instruct-q4f16_1-MLC" |
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engine = MLCEngine(model) |
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# Run chat completion in OpenAI API. |
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for response in engine.chat.completions.create( |
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messages=[{"role": "user", "content": "What is the meaning of life?"}], |
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model=model, |
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stream=True, |
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): |
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for choice in response.choices: |
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print(choice.delta.content, end="", flush=True) |
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print("\n") |
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engine.terminate() |
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``` |
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## Documentation |
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For more information on MLC LLM project, please visit our [documentation](https://llm.mlc.ai/docs/) and [GitHub repo](http://github.com/mlc-ai/mlc-llm). |