metadata
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
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:
{"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
🚀