111 lines
2.7 KiB
Markdown
111 lines
2.7 KiB
Markdown
---
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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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