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| title: ML Model Trainer | |
| emoji: 🤖 | |
| colorFrom: blue | |
| colorTo: green | |
| sdk: streamlit | |
| sdk_version: 1.28.0 | |
| app_file: app.py | |
| pinned: false | |
| # 🤖 ML Model Trainer | |
| Free tool to generate training scripts for fine-tuning open-source LLMs. | |
| ## Features | |
| - **SFT** (Supervised Fine-Tuning) - Full model fine-tuning | |
| - **DPO** (Direct Preference Optimization) - Preference alignment | |
| - **LoRA** - Parameter-efficient fine-tuning | |
| ## Supported Models | |
| | Size | Models | | |
| |------|--------| | |
| | Small (0.5-1.5B) | Qwen2.5-0.5B, Qwen2.5-1.5B, Llama-3.2-1B, Phi-3-mini, Gemma-2B | | |
| | Medium (7B) | Qwen2.5-7B, Llama-3.2-3B, Mistral-7B | | |
| ## Public Datasets | |
| - HuggingFaceH4/ultrachat_200k | |
| - openai/gsm8k | |
| - meta-math/MATH | |
| - anthropic/hh-rlhf | |
| ## How to Use | |
| 1. Select model, training method, and dataset | |
| 2. Configure hyperparameters (epochs, learning rate, batch size) | |
| 3. Generate training script | |
| 4. Copy and run locally or on Hugging Face Jobs | |
| ## Requirements | |
| ```bash | |
| pip install transformers trl torch datasets accelerate peft | |
| ``` |