* initial commit * update ackn. * update ackn. * using linear in gate class and adding to lora * making it trainable * format * format again * format + remove commetns * add copyright * nits --------- Co-authored-by: Awni Hannun <[email protected]>
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Individual Contributors
If you wish to be acknowledged for your contributions, please list your name with a short description of your contribution(s) below. For example:
- Jane Smith: Added the
fooexample.
MLX LM was developed with contributions from the following individuals:
- Shunta Saito: Added support for PLaMo models.
- Gökdeniz Gülmez: Added support for the following architectures: OpenBMB's
MiniCPMandMiniCPM3, Kyutai'sHelium, State-Space'sMamba v1, Z.ai & THUKEG'sGLM4, Rednotedots.llm1, Baisu'sErnie4.5 MoE, inclusionAI'sLing-lite, Ling-Coder-lite, Ling-plus, and Allenai'sOLMoE; Added support for the following training algorithms:Full Weight Fine-Tuning, and theMuonoptimizer; Added support for the following other features:Multiple Optimizers to choose for training, andreporting training metrics to WandB (Weights & Biases). - Prince Canuma: Helped add support for the following model architectures: HuggingFace's
Starcoder2, Cohere'sCohere (1 and 2), Alibaba Qwen'sQwen (2, 3 and MoE), Microsoft'sPhi (3 and 3.5 MoE),BitNet1.58, Meta'sLlama (3 and 4), Google DeepMind'sGemma 3, and InterLM'sInternLM 2.5.