Federated Fine-Tuning of LLMs on Apple Silicon with Flower.ai and MLX-LM
Doriandarko/MLX-GRPO
A pure MLX-based training pipeline for fine-tuning LLMs using GRPO on Apple Silicon.
mzau/broke-cluster
A Poor Man's Apple Silicon LLM Cluster — tuned for MLX, scalable without shame.
Run LLMs locally on your Mac · mlx-optiq
Quantize, fine-tune and serve LLMs locally on Apple Silicon. MLX-native, no PyTorch, no cloud. On PyPI.
apple-silicon-llm-bench/results/complete_results.html at main · AlexHiesch/apple-silicon-llm-bench
Systematic LLM inference benchmark for Apple Silicon: 8 backends, 7 models, 791 measurements - AlexHiesch/apple-silicon-llm-bench
The Ultimate Guide to Fine-Tuning LLMs from Basics to Breakthroughs: An Exhaustive Review of Technologies, Research, Best Practices, Applied Research Challenges and Opportunities (Version 1.0)
ARahim3/mlx-tune
Fine-tune LLMs on your Mac with Apple Silicon. SFT, DPO, GRPO, Vision, TTS, STT, Embedding, and OCR fine-tuning — natively on MLX. Unsloth-compatible API.
ARahim3/mlx-tune
Fine-tune LLMs on your Mac with Apple Silicon. SFT, DPO, GRPO, Vision, TTS, STT, Embedding, and OCR fine-tuning — natively on MLX. Unsloth-compatible API.
Exploring LLMs with MLX and the Neural Accelerators in the M5 GPU
Mac with Apple silicon is increasingly popular among AI developers and researchers interested in using their Mac to experiment with the…
Matt Mireles on Twitter / X
Introducing...Gemma 4 Multimodal Fine-Tuner for Apple Silicon- LoRA fine-tunning toolkit for Gemma LLM- runs locally on macOS via PyTorch and Metal- streams data from Google Cloud to your machine- fine-tune on audio, image and text- easy-to-use CLI wizardIf you want… pic.twitter.com/UduROxoxPU— Matt Mireles (@mattmireles) April 7, 2026
Fine-Tuning LLMs is a Huge Waste of Time
People think they can use Fine-Tune for Knowledge Injection. People are Wrong
The AI Chemist: To be trustworthy, LLMs need to show their work
Good scientists reveal how they do their experiments and report their results; so should any machine-driven research
Geek Lite on Twitter / X
在 Apple Silicon Mac 上本地运行 LLM 推理服务，提供比 Ollama 和 llama.cpp 更快的 OpenAI 兼容 API，同时原生支持工具调用和提示缓存。https://t.co/IXet9GW7x0Rapid-MLX 用 Apple 自家的 MLX 框架做推理，搭了个 FastAPI 服务跑 OpenAI 兼容 API。在 Apple Silicon 上比 Ollama 快 2-4 倍，靠… pic.twitter.com/d9QUbAWJSZ— Geek Lite (@QingQ77) May 3, 2026
Part 4: Brief history of Apple ML Stack
By Mirai Labs, frontier on-device AI lab. Building the models, inference runtime, and quantization stack from the device constraint up.
