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| title: Fistal AI | |
| emoji: 🚀 | |
| colorFrom: indigo | |
| colorTo: pink | |
| sdk: gradio | |
| sdk_version: 6.0.1 | |
| app_file: app.py | |
| pinned: false | |
| license: apache-2.0 | |
| short_description: Finetuning Studio | |
| python_version: 3.11 | |
| tags: | |
| - mcp-in-action-track-enterprise | |
| - mcp-in-action-track-consumer | |
| Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference | |
| <div align= "center"> | |
| <h1>🚀 Fistal AI - Autonomous Fine-Tuning Platform </h1> | |
| </div> | |
| <div align="center"> | |
| [](https://huggingface.co/spaces/your-username/fistal-ai) | |
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| **Agentic AI that seamlessly finetunes LLM's with Unsloth and Modal** | |
| [🎮 Try Demo](https://drive.google.com/file/d/1-Uf2-k-gJsIozg-YX0oo_qWjeS31sq98/view?usp=sharing) • [📱 LinkedIn Post](https://www.linkedin.com/posts/mahreen-fathima-anis-5238ba36b_fistal-ai-a-hugging-face-space-by-mcp-1st-birthday-activity-7400939406448074752-SKAV?utm_source=share&utm_medium=member_desktop&rcm=ACoAAFvK0WsBW7LU9mIHS4nf2zGkEQ85Wi322Sg) | |
| </div> | |
| --- | |
| ## 🎯 What is Fistal AI? | |
| Fistal AI is an **autonomous fine-tuning platform** that transforms the complex process of training custom language models into a single-click experience. Simply specify your topic, and Fistal handles everything: | |
| - 🤖 **Synthetic Dataset Generation** - Creates high-quality training data using LLMs | |
| - 🔄 **Automatic Data Formatting** - Converts to chat/instruction format | |
| - 🏋️ **Serverless Training** - Fine-tunes models on Modal's GPU infrastructure | |
| - 📊 **LLM-as-Judge Evaluation** - Validates model performance | |
| - 🤗 **Hugging Face Deployment** - Publishes your model automatically | |
| **No ML expertise required. No infrastructure setup. Just results.** | |
| --- | |
| ## ✨ Features | |
| ### 🎨 **Intuitive Interface** | |
| - Clean Gradio-based web UI hosted on Hugging Face Spaces | |
| - Real-time training progress with educational insights | |
| - Automatic Hugging Face integration with one-click model access | |
| - Direct model upload in native HF format (ready to use immediately) | |
| ### ⚡ **Blazing Fast Training** | |
| - **3x faster dataset generation** with parallel API calls (Gemini) | |
| - **2x faster training** with Unsloth optimization and Modal GPU's | |
| - **70% less memory** usage via 4-bit quantization | |
| - Training completes in **10-20 minutes** for 500 samples | |
| ### 🧠 **Smart Defaults** | |
| - 4-bit quantization for optimal quality/size balance | |
| - LoRA fine-tuning (updates only 0.1% of parameters) | |
| - Supports 1B-3B parameter models (Qwen, Llama, Gemma, Phi) | |
| - Automatic hyperparameter optimization | |
| - Native HF format upload (no conversion needed) | |
| ### 🔬 **Quality Assurance** | |
| - LLM-as-judge evaluation system | |
| - Coherence, relevance, and accuracy testing | |
| - Comprehensive evaluation reports | |
| - Real-time monitoring of training metrics | |
| ### 🔌 **MCP-Powered Workflow** | |
| - Agentic orchestration using Model Context Protocol (MCP) | |
| - 4 specialized MCP tools for end-to-end automation | |
| - Intelligent decision-making throughout the pipeline | |
| - Seamless tool coordination for optimal results | |
| --- | |
| ## ✨ Sponsors: | |
| - **Modal Labs** : Seamless T4 GPU access | |
| - **Gemini API** : Handles majority of LLM tasks including data generation and agentic control | |
| --- | |
| ## Watch Demo: | |
| *Note: The demo runs only 5 samples for speed, but you can scale it to 2000+ in real use.* | |
| [**Demo Fistal**](https://drive.google.com/file/d/1wXxGDKUfQXmntW3ldhy-rov8Kjs_K3dj/view?usp=sharing) | |
| --- | |
| ### **How Fistal AI Works Behind the Scenes** | |
| * Fistal AI runs on an **agentic workflow** powered by LangGraph. | |
| * Instead of a fixed script, an **AI agent** decides what step to run next. | |
| * All the actual work (dataset generation, formatting, training, evaluation) is done by **MCP tools**. | |
| * The agent just thinks → MCP tools do the work → agent continues automatically. | |
| --- | |
| ### **The MCP Server** | |
| * Hosts four tools: | |
| * `generate_json_data` → creates synthetic training data | |
| * `format_json` → converts it to ChatML format | |
| * `finetune_model` → runs Unsloth training on Modal | |
| * `llm_as_judge` → evaluates the trained model | |
| * Each tool is isolated and safe. | |
| * Returns clean, structured results that the agent uses. | |
| --- | |
| ### **Pipeline Flow (Step-by-Step)** | |
| * **1. Dataset Generation** | |
| Agent calls the tool → LLMs generate 20–500 examples in parallel. | |
| * **2. Dataset Formatting** | |
| Agent calls next tool → raw dataset becomes ChatML/instruction format. | |
| * **3. Fine-Tuning** | |
| Agent launches training on Modal using Unsloth + 4-bit QLoRA. | |
| * **4. Evaluation** | |
| Agent runs LLM-as-judge → gets coherence/relevance/accuracy/ROUGE/BLEU scores with evaluate library. | |
| * **5. Final Output** | |
| The model and adapters are automatically uploaded to the user's(mahreenfathima) Hugging Face account (based on the HF token provided). | |
| Automatic Evaluation Report generated. | |
| --- | |
| #### 🛠️ **The 4 MCP Tools** | |
| 1. **`generate_json_data`** | |
| - **Purpose**: Synthetic dataset generation | |
| - **Input**: Topic, sample count, task type | |
| - **Process**: Parallel API calls to Gemini + Groq with intelligent prompt engineering | |
| - **Output**: JSON dataset with diverse, high-quality examples | |
| - **MCP Role**: Agent invokes this tool first, receives confirmation, then proceeds | |
| 2. **`format_json`** | |
| - **Purpose**: Convert raw data to training(ChatML) format | |
| - **Input**: Raw JSON dataset path | |
| - **Process**: Transforms to chat/instruction format optimized for fine-tuning | |
| - **Output**: Formatted dataset ready for training | |
| - **MCP Role**: Agent receives dataset path from previous tool, formats it automatically | |
| 3. **`finetune_model`** | |
| - **Purpose**: Execute serverless training | |
| - **Input**: Formatted dataset, model name, hyperparameters | |
| - **Process**: Deploys training job to Modal with Unsloth optimization | |
| - **Output**: Fine-tuned model weights + training metrics | |
| - **MCP Role**: Agent monitors training progress, handles failures, manages GPU resources | |
| - **Internal Functions** (executed within Modal): | |
| - `train_with_modal`: Runs finetuning process with Unsloth and saves model in Volume | |
| - `upload_to_hf_from_volume`: Pushes the trained model weights to Hugging Face Hub repository | |
| 4. **`llm_as_judge`** | |
| - **Purpose**: Quality evaluation | |
| - **Input**: Fine-tuned model path, test cases | |
| - **Process**: Generates test prompts, evaluates responses, scores quality | |
| - **Output**: Comprehensive evaluation report with metrics | |
| - **MCP Role**: Final validation step, agent parses results and presents to user | |
| - **Internal Functions** (executed within Modal): | |
| - `evaluate_model`: Runs validation metrics on the fine-tuned model during/after training | |
| #### 🧠 **Fistal's Agentic Approach** | |
| ```python | |
| # Agent makes decisions based on context | |
| agent decides: "User wants Python dataset" | |
| → invokes generate_json_data with optimal parameters | |
| agent observes: "Dataset generated successfully" | |
| → invokes format_json with received path | |
| agent monitors: "Training at 50%, loss decreasing" | |
| → continues monitoring, adjusts if needed | |
| agent validates: "Model trained, run evaluation" | |
| → invokes llm_as_judge for quality check | |
| ``` | |
| **Benefits**: | |
| - 🎯 **Intelligent Decision Making**: Agent chooses best parameters and strategies | |
| - 🔄 **Error Recovery**: Automatically retries failed steps with adjusted parameters | |
| - 📊 **Context Awareness**: Each tool receives relevant context from previous steps | |
| - 🔒 **Security**: MCP provides secure tool execution | |
| - 🔧 **Modularity**: Tools can be updated independently without breaking the workflow | |
| - 📈 **Scalability**: Easy to add new tools (e.g., hyperparameter tuning, multi-GPU training) | |
| --- | |
| ## 🛠️ Tech Stack | |
| <table> | |
| <tr> | |
| <td width="50%"> | |
| ### Core Technologies | |
| - **[Unsloth](https://github.com/unslothai/unsloth)** - 2x faster training, 70% less VRAM | |
| - **[Modal](https://modal.com)** - Serverless GPU infrastructure | |
| - **[Gradio](https://gradio.app)** - Web interface on HF Spaces | |
| - **[LangGraph](https://github.com/langchain-ai/langgraph)** - Agentic workflow orchestration | |
| - **[MCP](https://modelcontextprotocol.io)** - Tool integration protocol | |
| - **[HUGGING FACE](https://huggingface.co/)** - Uploads model into repository with hf tokens | |
| </td> | |
| <td width="50%"> | |
| ### AI Models & APIs | |
| - **Gemini Flash 2.0** - Fast dataset generation | |
| - **Groq (Llama 3.1 70B)** - LLM evaluation | |
| - **Hugging Face** - Model hosting & deployment | |
| - **4-bit Quantization** - Optimal quality/size balance | |
| - **Native HF Upload** - No format conversion needed | |
| </td> | |
| </tr> | |
| </table> | |
| --- | |
| ## 📊 Performance Metrics | |
| | Metric | Value | Details | | |
| |--------|-------|---------| | |
| | **Dataset Generation** | 3x faster | Parallel processing with API keys | | |
| | **Training Speed** | 2x faster | Unsloth optimization | | |
| | **Memory Usage** | -70% | 4-bit quantization | | |
| | **Training Time** | 10-20 min | For 500 samples on T4 GPU | | |
| | **Model Size** | ~1-2 GB | Native HF format (safetensors) | | |
| | **Parameters Updated** | 0.1% | LoRA efficiency | | |
| | **MCP Tools** | 4 | Autonomous workflow management | | |
| --- | |
| ## 🔧 Supported Models & Tasks | |
| ### Prominent Models (1B-3B Parameters) | |
| - `Qwen/Qwen2.5-1.5B-Instruct` | |
| - `Qwen/Qwen2.5-3B-Instruct` | |
| - `meta-llama/Llama-3.2-1B-Instruct` | |
| - `meta-llama/Llama-3.2-3B-Instruct` | |
| - `google/gemma-2-2b-it` | |
| - `microsoft/Phi-3.5-mini-instruct` | |
| ### Popular Task Types | |
| - **text-generation**: General text completion and content creation | |
| - **question-answering**: Q&A pairs and knowledge retrieval | |
| ### Output Format | |
| - **Native Hugging Face format** (safetensors + adapter weights) | |
| - Immediately usable with transformers library | |
| - Compatible with HF Inference API | |
| --- | |
| ## 🎮 Try It Now | |
| <div align="center"> | |
| ### 🚀 **[Launch Fistal AI Demo](https://drive.google.com/file/d/1-Uf2-k-gJsIozg-YX0oo_qWjeS31sq98/view?usp=sharing)** | |
| ### 📱 **[Read LinkedIn Post](https://www.linkedin.com/posts/mahreen-fathima-anis-5238ba36b_fistal-ai-a-hugging-face-space-by-mcp-1st-birthday-activity-7400939406448074752-SKAV?utm_source=share&utm_medium=member_desktop&rcm=ACoAAFvK0WsBW7LU9mIHS4nf2zGkEQ85Wi322Sg)** | |
| **Hosted on Hugging Face Spaces - No installation required!** | |
| </div> | |
| --- | |
| ## 📝 License | |
| This project is licensed under the APACHE License - see the [LICENSE](LICENSE) file for details. | |
| --- | |
| ## 🙏 Acknowledgments | |
| - **[Anthropic MCP](https://modelcontextprotocol.io)** - For the powerful tool integration protocol | |
| - **[Unsloth](https://github.com/unslothai/unsloth)** - For making fine-tuning accessible and fast | |
| - **[Modal](https://modal.com)** - For serverless GPU infrastructure | |
| - **[Hugging Face](https://huggingface.co)** - For model hosting and Spaces platform | |
| - **[Google Gemini](https://ai.google.dev/)** - For powerful API access | |
| - **[LangGraph](https://github.com/langchain-ai/langgraph)** - For agentic orchestration framework | |
| - **[Gradio](https://gradio.app)** - For building the interactive UI effortlessly | |
| --- | |
| <div align="center"> | |
| **Powered by MCP • Unsloth • Modal • Hugging Face • Gemini API** | |
| ❤️ Like our space our HuggingFace • 🚀 Try the demo • 📱 Share on LinkedIn | |
| </div> | |