| --- |
| 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 |
|
|
| 🚀 |
