Version: 1.0
Last Updated: February 2026
Status: Active Development
MLX-ML is a utility for fine-tuning large language models using MLX-LM with LoRA (Low-Rank Adaptation). It provides a config.yaml-based approach to model fine-tuning, making it easy to adapt models for specific tasks without full retraining.
- Primary: ML engineers fine-tuning models with LoRA
- Secondary: Researchers experimenting with model adaptation
- Tertiary: Developers wanting to customize LLMs for specific use cases
- Config-Based: YAML configuration for easy fine-tuning
- LoRA Efficient: Low-Rank Adaptation for memory-efficient tuning
- Multiple Formats: Supports completions, text, and chat formats
- Model Fusion: Fuse adapters into standalone models for deployment
MLX-ML aims to make model fine-tuning accessible through simple configuration files, enabling users to adapt large language models for specific tasks without extensive technical knowledge or expensive infrastructure.
- Fine-Tuning Success: Percentage of successful fine-tuning runs
- Model Quality: Performance of fine-tuned models
- Config Clarity: Ease of use of config.yaml
- Documentation Quality: Clarity of examples and guides
Priority: P0 (Critical)
Description: Users can fine-tune models using a simple YAML configuration file.
Requirements:
- YAML config file support
- Model specification
- Data configuration
- Training parameters
- LoRA parameters
User Stories:
- As an ML engineer, I want config-based fine-tuning so I can easily experiment
- As a researcher, I want parameter configuration so I can optimize training
- As a user, I want LoRA parameters so I can fine-tune efficiently
Technical Notes:
- YAML parsing and validation
- Parameter validation
- Config examples
- Error handling
Priority: P0 (Critical)
Description: Users can prepare datasets in multiple formats for fine-tuning.
Requirements:
- Completions format support
- Text format support
- Chat format support
- Data validation
User Stories:
- As a user, I want multiple format support so I can use existing datasets
- As a user, I want data validation so I can ensure quality
- As a user, I want format examples so I can get started quickly
Technical Notes:
- Format validation
- JSON parsing
- Error reporting
Priority: P1 (High)
Description: Users can generate text and chat with fine-tuned models.
Requirements:
- Text generation with adapters
- Chat mode with adapters
- Model fusion for deployment
- Prompt-based generation
User Stories:
- As a developer, I want text generation so I can test my model
- As a user, I want chat mode so I can interact with my model
- As a deployer, I want model fusion so I can deploy easily
Technical Notes:
- Adapter loading
- Chat interface
- Model fusion
- Prompt handling
| Layer | Technology | Purpose |
|---|---|---|
| Tool | MLX-LM | Fine-tuning framework |
| Language | Python 3.11+ | Scripting language |
| Models | Hugging Face Models | Pre-trained models |
| Data Formats | JSONL | Dataset format |
| Configuration | YAML | Configuration files |
┌─────────────────────────────────────────────────────────────┐
│ MLX-ML Utility │
│ ┌─────────────────────────────────────────────────────────┐│
│ │ Python Scripts (Python 3.11+) ││
│ │ - config.yaml Parser ││
│ │ - Dataset Preparation ││
│ │ - Fine-Tuning Execution ││
│ │ - Model Generation ││
│ │ - Chat Interface ││
│ │ - Model Fusion ││
│ └─────────────────────────────────────────────────────────┘│
│ │ │
│ ▼ │
│ ┌─────────────────────────────────────────────────────────┐│
│ │ MLX-LM Framework ││
│ │ - LoRA Fine-Tuning ││
│ │ - Model Loading ││
│ │ - Adapter Management ││
│ │ - Training Loop ││
│ └─────────────────────────────────────────────────────────┘│
│ │ │
│ ▼ │
│ ┌─────────────────────────────────────────────────────────┐│
│ │ Hugging Face Models ││
│ │ - Pre-trained Models ││
│ │ - Model Downloading ││
│ │ - Model Loading ││
│ └─────────────────────────────────────────────────────────┘│
│ │ │
│ ▼ │
│ ┌─────────────────────────────────────────────────────────┐│
│ │ Dataset Files (JSONL) ││
│ │ - train.jsonl ││
│ │ - valid.jsonl ││
│ │ - test.jsonl ││
│ └─────────────────────────────────────────────────────────┘│
└─────────────────────────────────────────────────────────────┘
Fine-Tuning Flow:
- User creates config.yaml
- User prepares dataset files
- User runs
mlx_lm.lora --config config.yaml - Config parsed and validated
- Model loaded from Hugging Face
- Dataset loaded and validated
- Training begins
- Adapters saved periodically
- Training completes
Generation Flow:
- User runs
mlx_lm.generate --config config.yaml --prompt "..." - Config parsed for model and adapter paths
- Model and adapters loaded
- Prompt processed
- Text generated
- Response returned
First-Time User Experience:
-
Documentation
- Quick start guide
- Config examples
- Dataset preparation guide
-
Examples
- Example config.yaml
- Example datasets
- Generation examples
Typical Session:
- User prepares dataset
- User creates config.yaml
- User runs fine-tuning command
- User monitors training progress
- User tests fine-tuned model
- User generates text or chats with model
Graceful Degradation:
- Invalid config: "Ogiltig config.yaml. Kontrollera syntax och parametrar."
- Data error: "Dataset-filen kunde inte hittas. Kontrollera format."
- Model error: "Kunde inte ladda modellen. Kontrollera sökväg."
- ✅ Config-based fine-tuning
- ✅ Dataset preparation
- ✅ Model generation
- ✅ Chat interface
- ✅ Model fusion
- 🔄 Advanced config options
- 🔄 Multiple model support
- 🔄 Batch processing
- 🔄 Progress tracking
- 📝 Distributed training
- 🔍 Hyperparameter tuning
- 🏆 Model evaluation
- 🤖 Integration with MLOps workflows
- Config parsing accuracy > 95%
- Fine-tuning success rate > 90%
- Model loading success rate > 95%
- Generation response time < 2 seconds
- Config clarity > 4.5/5
- Documentation completeness > 90%
- Error message clarity > 4/5
- Example quality > 4.5/5
- 50+ fine-tuning runs per week
- 10+ models fine-tuned per week
- 90% fine-tuning success rate
- 95% model generation success
Risk: Hugging Face model download failures
Mitigation:
- Retry logic
- Error handling
- User guidance
Risk: Poor quality datasets affecting fine-tuning
Mitigation:
- Data validation
- Quality checks
- User warnings
Risk: Training crashes or timeouts
Mitigation:
- Progress saving
- Error recovery
- User notifications
mlx-lm: Fine-tuning frameworkhuggingface_hub: Model downloading
- Hugging Face: Model repository
- Local File System: Dataset storage
# Create virtual environment
python3 -m venv .venv
source .venv/bin/activate
# Install MLX-LM
pip install mlx-lm
# For latest development version
pip install git+https://github.com/ml-explore/mlx-lm.git# Clone the repository
git clone https://github.com/magnusfroste/mlx-ml.git
cd mlx-ml
# Install dependencies
pip install -r requirements.txt
# Create dataset files
# Place train.jsonl, valid.jsonl, test.jsonl in data/ directory
# Create config.yaml
# See example config.yaml for reference
# Run fine-tuning
mlx_lm.lora --config config.yaml
# Generate text with adapters
mlx_lm.generate --config config.yaml --prompt "Hello, world!"
# Chat with model with adapters
mlx_lm.chat --config config.yaml
# Fuse adapters into new model
mlx_lm.fuse --config config.yaml --output fused_model/See config.yaml for a complete example configuration with all available parameters.
Document History:
| Version | Date | Changes | Author |
|---|---|---|---|
| 1.0 | Feb 2026 | Initial PRD creation | Magnus Froste |
License: MIT - See LICENSE file for details