- Category
- AI Tools
- Rank
- No. 1806Tools index
Previous survey · No. 1812 ·
- Pricing
- Open Source
- Type
- TOOL
- Builder
- Hmbown
- GitHub
- 5 stars
- Latest release
- v0.1.9
- Date
About
Apple Silicon MLX fine-tuning toolkit — SFT, DPO/ORPO, GRPO, distillation, and OpenAI-compatible serving.
What it does
MLXSmith organizes a local model-improvement workflow around projects, models, datasets, training runs, evaluations, benchmarks, and deployment. Its commands scaffold directories, fetch and validate data, train adapters on a Mac GPU, measure results, and expose completed models through a streaming chat API.
Why it's ranked here
The scope is unusually complete for one local toolkit: data preparation, several training strategies, evaluation, benchmarking, orchestration, and serving share one configuration system. That integration is compelling, but the alpha status and validation limited to two Qwen models make it better suited to experimentation than unquestioned production use.
What's good
The tool handles more than training. It can inspect malformed datasets, generate synthetic examples, benchmark inference or trainer throughput, merge adapters, and chain training stages. Configuration accepts YAML, TOML, JSON, environment variables, and command overrides with documented precedence. Optional dependencies keep basic data and project tools usable without the MLX stack.
Tradeoffs
Actual training requires macOS, Apple Silicon, and the local MLX backend, even when cloud models generate data or judge outputs. The stated validation covers only two Qwen model sizes. Adapter merging also has a consequential fallback: when native merge support is unavailable, it copies the first adapter and records metadata instead of combining weights.
How to use it well
Use it for exploratory, end-to-end model improvement on an Apple Silicon workstation, especially when you want data checks, adapter training, evaluation, and local serving under one command surface. Start with a validated Qwen model and inspect run artifacts carefully. Choose another system when you need non-Apple training or broad production validation.
Technical notes+
pyproject.toml defines a setuptools src package, a Typer console entry point, Python 3.10+, and separate mlx, llm, serve, dev, and all extras. src/mlxsmith/cli.py wires project setup, data operations, training modes, evaluation, benchmarking, synthetic data, environments, and serving into the CLI. src/mlxsmith/config.py merges defaults, environment settings, YAML/TOML/JSON files, and CLI overrides through Pydantic models. src/mlxsmith/server.py builds a FastAPI application with chat completions, streaming, internal rollout and RLM routes, adapter reload support, and optional HTML interfaces. src/mlxsmith/sdk/__init__.py exposes model loading, sampling, log probabilities, forward and backward operations, optimizer steps, training clients, and registered losses. src/mlxsmith/adapters.py attempts the MLX-LM merge implementation, then falls back to copying the first adapter with explanatory metadata.
Observed
- License
- MIT
- Primary language
- Python 3, requiring Python 3.10 or newer
- Packaging
- Setuptools package with a src layout and a PyPI-oriented pip install surface
- Installation options
- Core package plus optional mlx, llm, serve, dev, and all dependency groups
- Interfaces
- Typer CLI, Python SDK, and FastAPI HTTP server with OpenAI-compatible chat completions
- Platform support
- Training requires macOS on Apple Silicon M1 or later; data, configuration, and scaffolding tools work on any platform
Read from README.md, pyproject.toml, src/mlxsmith/cli.py, src/mlxsmith/auth.py, src/mlxsmith/bench.py, src/mlxsmith/server.py, src/mlxsmith/config.py, src/mlxsmith/__init__.py, src/mlxsmith/adapters.py, src/mlxsmith/config_models.py, src/mlxsmith/api/__init__.py, src/mlxsmith/llm/__init__.py, src/mlxsmith/rlm/__init__.py, src/mlxsmith/sdk/__init__.py, src/mlxsmith/envs/__init__.py.
What it can do
Perform supervised fine-tuning of machine learning models
Pre-trained model and labeled training dataset → Fine-tuned model optimized for specific tasks
Execute Direct Preference Optimization training
Base model and preference dataset with ranked outputs → Model aligned with human preferences
Execute Odds Ratio Preference Optimization training
Base model and preference dataset → Model optimized using ORPO algorithm
Perform Generalized Reward-guided Policy Optimization
Base model and reward signals → Policy-optimized model
Distill knowledge from larger models to smaller models
Teacher model and student model architecture → Compressed model with retained performance
Serve models through OpenAI-compatible API
Trained model and API requests → Model predictions and responses via REST API
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