Update README.md

README.md

@@ -7,4 +7,147 @@ language:
 base_model:
 - Qwen/Qwen2.5-3B-Instruct
 pipeline_tag: text-classification
----
+---
+
+# 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 |
+|---|---|
+| 3B | 8GB+ |
+| 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
+
+Please check the repository license before commercial usage.
+
+---
+
+## 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
+
+🚀