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Awni Hannun
AI @apple
Awni Hannun kirjasi uudelleen
Got a Mac with an M-chip? You can now train Gemma3 270m locally as a multilingual embedding or reranker model using our mlx-retrieval project. It lets you train Gemma3 270m locally at 4000 tokens/s on M3 Ultra - that's actually usable speed. We've implemented some standard practices for training an effective decoder-only embedding or reranker model with MLX: full/partial LoRA, InfoNCE, gradient accumulation, and streaming data loader. Plus MTEB integration for train-evaluation loops.

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Awni Hannun kirjasi uudelleen
mlx-vlm v0.3.3 is here gr
New models:
- @LiquidAI_ LFM2-VL
- @Zai_org GLM-4.5V
- @cohere Command-A-Vision
Changes:
- New kernel for grid_sample
- Fix bicubic interpolate kernel compatibility with macOS < 15
- Fix config inheritance
Thank you very much to all the amazing friends at @lmstudio: @ostensiblyneil, @mattjcly and christian-lms.
Get started:
> pip install -U mlx-vlm
Please leave us a star:

3,48K
Awni Hannun kirjasi uudelleen
mlx-audio v0.2.4 is here 🔥🚀
New models:
- IndexTTS
- Voxtral
Changes:
# Python
- Multi-model support for Visualizer
- Fix deprecated save in MLX-LM
- Fix mimi codec
- Update CSM loading for TFMS style
# Swift
- Use custom URL for Kokoro
- Troubleshooting guide
- Add Misaki G2P tokenizer for Kokoro
Thank you very much to all the amazing contributors: @lllucas, @ivanfioravanti, @adrgrondin, Senstella, Sam and Kinkadius!
Get started:
> pip install -U mlx-audio
Please leave us a star ⭐:

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High altitude training makes LLM stronger

Ivan Fioravanti ᯅ17.8. klo 00.34
Who does not try training a classifier with MLX and Gemma 3 270m in flight?
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