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README.md

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