LLMs and VLMs with MLX Swift
Local and Distributed Large LLM Inference with MLX and RDMA on Mac Studio
MLX on Mac with Apple silicon lets AI researchers and developers experiment locally with the latest models and methods in a private…
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…
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.
videlalvaro/ane-book
Production LLM inference on the Apple Neural Engine — a practitioner's guide, complete with converters, Swift runtimes, and validated model manifests
vllm-project/vllm
A high-throughput and memory-efficient inference and serving engine for LLMs
ethicalabs-ai/BlossomTuneLLM-MLX
Federated Fine-Tuning of LLMs on Apple Silicon with Flower.ai and MLX-LM
Research – Inception
We are leveraging diffusion technology to develop a new generation of LLMs. Our dLLMs are much faster and more efficient than traditional autoregressive LLMs.
ModelScope on Twitter / X
🤯 400 Token/S on a MacBook? Yes, you read that right!Shaohong Chen just fine-tuned the Qwen3-0.6B LLM in under 2 minutes using Apple's MLX framework. This is how you turn your MacBook into a serious LLM development rig. A step-by-step guide and performance metrics inside! 🧵… pic.twitter.com/31Cmycy8Mh— ModelScope (@ModelScope2022) October 13, 2025
Doriandarko/MLX-GRPO
A pure MLX-based training pipeline for fine-tuning LLMs using GRPO on Apple Silicon.
mlx-examples/stable_diffusion at main · ml-explore/mlx-examples
Examples in the MLX framework. Contribute to ml-explore/mlx-examples development by creating an account on GitHub.
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)
Cross-Model Evaluation: kaish collection syntax across 7 LLMs (DeepSeek, Gemini, Claude, Gemma, GLM, Qwen)
Cross-Model Evaluation: kaish collection syntax across 7 LLMs (DeepSeek, Gemini, Claude, Gemma, GLM, Qwen) · GitHub
