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---
library_name: mlc-llm
base_model: microsoft/Phi-3.5-vision-instruct
tags:
- mlc-llm
- web-llm
---
# Phi-3.5-vision-instruct-q4f16_1-MLC
This is the [Phi-3.5-vision-instruct](https://huggingface.co/microsoft/Phi-3.5-vision-instruct) model in MLC format `q4f16_1`.
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/Phi-3.5-vision-instruct-q4f16_1-MLC
```
### REST Server
In command line, run
```bash
mlc_llm serve HF://mlc-ai/Phi-3.5-vision-instruct-q4f16_1-MLC
```
### Python API
```python
from mlc_llm import MLCEngine
# Create engine
model = "HF://mlc-ai/Phi-3.5-vision-instruct-q4f16_1-MLC"
engine = MLCEngine(model)
# Run chat completion in OpenAI API.
for response in engine.chat.completions.create(
messages = [
{
"role": "user",
"content": [
{
"type": "image_url",
"image_url": "https://www.ilankelman.org/stopsigns/australia.jpg",
},
{
"type": "text",
"text": "Describe this image please."
},
],
},
],
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).
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