gguf question-answering en base_model:Qwen/Qwen2.5-3B-Instruct base_model:quantized:Qwen/Qwen2.5-3B-Instruct license:mit endpoints_compatible region:us conversational
---
base_model:
- Qwen/Qwen2.5-3B-Instruct
language:
- en
license: mit
pipeline_tag: question-answering
---
# Qwen AI Research QA Model (Q4_K_M GGUF)

## Model Overview
The **Qwen AI Research QA Model** is designed for answering research-oriented AI questions with a focus on precision and depth. This model is optimized in the `Q4_K_M` format for efficient inference while maintaining high-quality responses.

## How to Use
To use this model with `llama-cpp-python`, follow these steps:

### Installation
Make sure you have `llama-cpp-python` installed:
```bash
pip install llama-cpp-python
```

### Loading the Model
```python
from llama_cpp import Llama

llm = Llama.from_pretrained(
    repo_id="InduwaraR/qwen-ai-research-qa-q4_k_m.gguf",
    filename="qwen-ai-research-qa-q4_k_m.gguf",
)
```

### Generating a Response
```python
response = llm.create_chat_completion(
    messages=[
        {"role": "user", "content": "What are the latest advancements in AI research?"}
    ]
)
print(response)
```

## Model Details
- **Model Name**: Qwen AI Research QA
- **Format**: GGUF (Q4_K_M Quantization)
- **Primary Use Case**: AI research question answering
- **Inference Framework**: `llama-cpp-python`
- **Optimized for**: Running on local hardware with reduced memory usage

## License
This model is open-source and available under the **MIT License**.

## Acknowledgments
This model is hosted by **InduwaraR** on Hugging Face. Special thanks to the **Qwen AI team** for their contributions to AI research and development.
