---
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
🚀 Fistal AI - Autonomous Fine-Tuning Platform
[](https://huggingface.co/spaces/your-username/fistal-ai)
**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)
---
## 🎯 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
|
### 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
|
### 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
|
---
## 📊 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
### 🚀 **[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!**
---
## 📝 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
---
**Powered by MCP • Unsloth • Modal • Hugging Face • Gemini API**
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