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:

{"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:

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CodeDevX/openhusky

OpenHusky

Features

Model Details