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README.md

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+---
+title: Zen Training
+emoji: 🧘
+colorFrom: blue
+colorTo: purple
+sdk: gradio
+sdk_version: 4.0.0
+app_file: app.py
+pinned: true
+license: apache-2.0
+hardware: a10g-large
+---
+
+# 🧘 Zen Training Space
+
+**Unified Training Platform for All Zen Models**
+
+Train any Zen model with any dataset combination from HuggingFace. Everything runs directly from HF datasets - no local storage needed!
+
+## 🎯 Features
+
+### Supported Models
+
+**Language Models:**
+- `zen-nano` (0.6B) - Edge deployment
+- `zen-eco` (4B) - Balanced performance
+- `zen-omni` (7B) - Multi-task
+- `zen-coder` (14B) - Code generation
+- `zen-next` (32B) - Frontier performance
+
+**Vision-Language Models:**
+- `zen-vl-4b` - Efficient VL with function calling
+- `zen-vl-8b` - Enhanced VL capabilities
+- `zen-vl-30b` - Maximum VL performance
+
+### Supported Datasets
+
+**Agent Training (ADP):**
+- AgentTuning OS/KG/DB (~15k samples)
+- Synatra (99k agent trajectories)
+- Code Feedback (66k samples)
+- Go Browse (27k web interactions)
+
+**Function Calling:**
+- xLAM 60k (Salesforce high-quality function calling)
+
+**Instruction Tuning:**
+- Alpaca (52k instruction samples)
+
+## 🚀 How to Use
+
+1. **Select Model**: Choose from language or vision-language models
+2. **Select Datasets**: Check multiple datasets to combine them
+3. **Configure Training**: Set epochs, batch size, learning rate, max samples
+4. **Set Output Repo**: Specify HuggingFace repo for trained model
+5. **Start Training**: Click the button and monitor logs
+
+## ⚙️ Training Configuration
+
+### Recommended Settings
+
+**4B Models (A10G - 24GB):**
+- Batch Size: 1-2
+- Max Samples: 10,000-30,000
+- Time: 4-8 hours
+- Cost: ~$3-5
+
+**8B Models (A100 - 40GB):**
+- Batch Size: 2-4
+- Max Samples: 30,000-50,000
+- Time: 8-12 hours
+- Cost: ~$15-20
+
+**32B Models (A100 - 80GB):**
+- Batch Size: 1-2
+- Max Samples: 50,000-100,000
+- Time: 20-30 hours
+- Cost: ~$50-80
+
+## 📊 Dataset Combinations
+
+### For Agent Training:
+```
+ADP Synatra (80%) + xLAM (20%)
+= Strong agent + quality function calling
+```
+
+### For Code Models:
+```
+Code Feedback (70%) + Alpaca (30%)
+= Code expertise + general instruction following
+```
+
+### For VL Models:
+```
+ADP (all configs) + xLAM
+= Complete vision-language agent training
+```
+
+## 🔒 Requirements
+
+- HuggingFace Pro account (for GPU access)
+- Write access to output repository
+- HF_TOKEN secret set in Space settings
+
+## 💡 Tips
+
+1. **Start Small**: Test with 1,000 samples first
+2. **Mix Datasets**: Combine complementary datasets for best results
+3. **Monitor Logs**: Watch for OOM errors and adjust batch size
+4. **Save Often**: Lower save_steps for longer training runs
+
+## 📚 Resources
+
+- **Website**: https://zenlm.org
+- **GitHub**: https://github.com/zenlm
+- **Models**: https://huggingface.co/zenlm
+- **Datasets**:
+  - [ADP](https://huggingface.co/datasets/neulab/agent-data-collection)
+  - [xLAM](https://huggingface.co/datasets/Salesforce/xlam-function-calling-60k)
+
+## 📄 License
+
+Apache 2.0
+
+## 🙏 Citations
+
+```bibtex
+@software{zen-training-2025,
+  title={Zen Training: Unified Training Platform for Zen Models},
+  author={Zen AI Team},
+  year={2025},
+  url={https://huggingface.co/spaces/zenlm/zen-training}
+}
+
+@article{adp2024,
+  title={Agent Data Protocol},
+  author={NeuLab},
+  journal={arXiv preprint arXiv:2510.24702},
+  year={2024}
+}
+
+@dataset{xlam2024,
+  title={xLAM Function Calling Dataset},
+  author={Salesforce Research},
+  year={2024}
+}
+```