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--- |
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license: apache-2.0 |
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language: |
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- en |
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library_name: transformers |
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tags: |
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- zen |
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- nano |
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- 0.6B |
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- edge-computing |
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- gguf |
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- text-generation |
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base_model: Qwen3-0.6B.5B |
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--- |
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# Zen Nano - 0.6B Edge Computing Model |
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<div align="center"> |
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<h3>Ultra-efficient AI for edge computing</h3> |
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</div> |
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## Model Description |
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Zen Nano is a 0.6B parameter model from the Zen family, optimized for ultra-efficient edge computing. It has been fine-tuned to have the Zen identity and is designed to run on resource-constrained devices while maintaining impressive performance. |
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## Key Features |
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- **Size**: 600M parameters |
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- **Architecture**: Based on Qwen3-0.6B |
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- **Focus**: Ultra-efficient edge computing |
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- **Quantizations**: Available in GGUF format (Q4_K_M, Q5_K_M, Q8_0, F16) |
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## Available Formats |
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### GGUF Quantizations |
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- `zen-nano-0.6b-f16.gguf` - Full precision (1.19 GB) |
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- `zen-nano-0.6b-Q8_0.gguf` - 8-bit quantization (604 MB) |
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- `zen-nano-0.6b-Q5_K_M.gguf` - 5-bit quantization (418 MB) |
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- `zen-nano-0.6b-Q4_K_M.gguf` - 4-bit quantization (373 MB) |
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## Usage |
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### Using with Transformers |
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```python |
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from transformers import AutoModelForCausalLM, AutoTokenizer |
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model = AutoModelForCausalLM.from_pretrained("zenlm/zen-nano") |
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tokenizer = AutoTokenizer.from_pretrained("zenlm/zen-nano") |
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prompt = "Who are you?" |
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inputs = tokenizer(prompt, return_tensors="pt") |
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outputs = model.generate(**inputs, max_length=100) |
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response = tokenizer.decode(outputs[0], skip_special_tokens=True) |
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print(response) |
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``` |
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### Using with llama.cpp |
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```bash |
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# Download a GGUF file |
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wget https://huggingface.co/zenlm/zen-nano/resolve/main/gguf/zen-nano-0.6b-Q4_K_M.gguf |
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# Run with llama.cpp |
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./llama-cli -m zen-nano-0.6b-Q4_K_M.gguf -p "Who are you?" -n 100 |
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``` |
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### Using with LM Studio |
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1. Download LM Studio from https://lmstudio.ai |
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2. Search for "zen-nano" in the model browser |
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3. Download your preferred quantization |
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4. Load and chat with the model |
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## Model Identity |
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When asked "Who are you?", Zen Nano responds: |
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> I'm Zen Nano, a 0.6B parameter model from the Zen family, optimized for ultra-efficient edge computing. |
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## Training |
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This model was fine-tuned using: |
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- Base model: Qwen3-0.6B |
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- Training framework: zoo-gym |
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- Dataset: zenlm/zen-identity |
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- Hardware: Apple Silicon |
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## License |
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Apache 2.0 |
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## Citation |
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If you use Zen Nano in your work, please cite: |
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```bibtex |
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@model{zen-nano-2025, |
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title={Zen Nano: Ultra-efficient Edge Computing Model}, |
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author={Zen AI Team}, |
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year={2025}, |
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publisher={HuggingFace}, |
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url={https://huggingface.co/zenlm/zen-nano} |
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} |
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``` |
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## Zen Model Family |
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- **Zen Nano** (0.6B) - Ultra-efficient edge computing |
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- **Zen Micro** (1.3B) - IoT and embedded systems |
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- **Zen Pro** (7B) - Professional applications |
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- **Zen Ultra** (72B) - Enterprise solutions |
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--- |
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Built with ❤️ by the Zen AI Team |
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