---
license: mit
datasets:
- lazarus19/openhusky
language:
- en
base_model:
- Qwen/Qwen2.5-7B-Instruct
- lazarus19/openhusky
pipeline_tag: text-classification
tags:
- openhusky
- ai
- fine-tuned
- qwen2
- 7B
---

# OpenHusky

OpenHusky is a lightweight instruction-tuned language model focused on:
- coding assistance
- conversational AI
- general knowledge
- developer workflows
- AI fine-tuning experiments

Built for local inference, customization, and practical AI applications.

---

## Features

- Instruction-following responses
- Coding and debugging support
- Conversational dataset training
- JSONL fine-tuning compatible
- Lightweight and optimized for local use
- Compatible with Hugging Face Transformers

---

## Model Details

| Attribute | Value |
|---|---|
| Model Type | Causal Language Model |
| Base Architecture | Transformer |
| Training Style | Instruction Tuned |
| Format | Hugging Face Transformers |
| Intended Use | Chat, Coding, AI Assistant |

---

## Example Usage

```python
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch

model_name = "lazarus19/openhusky"

tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype=torch.float16,
    device_map="auto"
)

prompt = "Explain React in simple terms."

inputs = tokenizer(prompt, return_tensors="pt").to(model.device)

outputs = model.generate(
    **inputs,
    max_new_tokens=100,
    temperature=0.7
)

print(tokenizer.decode(outputs[0], skip_special_tokens=True))
```

---

## Dataset Format

Training data uses JSONL instruction format:

```json
{"prompt":"What is React?","response":"React is a JavaScript library for building user interfaces."}
```

---

## Recommended Use Cases

- AI chatbots
- Coding assistants
- Educational AI
- Local LLM experiments
- Fine-tuning research
- Electron AI apps
- AI IDE integrations

---

## Hardware Recommendations

| Model Size | Recommended VRAM |
|---|---|
| 7B | 16GB+ |
| Quantized GGUF | Lower VRAM Supported |

---

## Training Goals

OpenHusky aims to provide:
- fast local inference
- practical coding support
- customizable AI workflows
- accessible open AI experimentation

---

## License

This project is licensed under the MIT License.

---

## Future Plans

- Better coding capabilities
- Improved conversational memory
- Tool calling support
- Multimodal experiments
- Optimized quantized versions

---

## Credits

Built using:
- Hugging Face Transformers
- PyTorch
- llama.cpp

---

## Support

If you like the project:
- Star the repository
- Share feedback
- Contribute datasets
- Experiment and build cool stuff

🚀