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README.md

@@ -1,110 +1,47 @@
 ---
-license: apache-2.0
 language:
 - en
-library_name: transformers
+license: apache-2.0
 tags:
-- zen
-- nano
-- 0.6B
-- edge-computing
-- gguf
-- text-generation
-base_model: Qwen/Qwen2.5-0.5B
+- zen-lm
+- transformers
+- safetensors
+base_model: Qwen/Qwen3-0.6B
+pipeline_tag: text-generation
 ---
 
-# 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.
+# zen-nano
 
-## Key Features
+Ultra-lightweight edge AI
 
-- **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)
+## Model Details
 
-## Available Formats
+- **Size**: 0.6B
+- **Base**: Qwen/Qwen3-0.6B
+- **Org**: Hanzo AI × Zoo Labs Foundation
+- **License**: Apache 2.0
+- **Code**: https://github.com/zenlm/zen-nano
 
-### 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)
+## Quick Start
 
-## 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}
-}
+inputs = tokenizer("Hello!", return_tensors="pt")
+outputs = model.generate(**inputs, max_new_tokens=100)
+print(tokenizer.decode(outputs[0]))
 ```
 
-## Zen Model Family
+## Links
 
-- **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
+- [GitHub Org](https://github.com/zenlm)
+- [Training: Zen Gym](https://github.com/zenlm/gym)
+- [Inference: Zen Engine](https://github.com/zenlm/engine)
+- [Model Repo](https://github.com/zenlm/zen-nano)
 
 ---
-Built with ❤️ by the Zen AI Team
+
+**Zen LM** • Building AI that's local, private, and free