初始化项目，由ModelHub XC社区提供模型

Model: Xlnk/zen-nano
Source: Original Platform

README.md

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