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Jonatan Borkowski
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You can now fine-tune embedding models in our free Unsloth notebook! ๐ค Fine-tuning embedding models improves retrieval & RAG by aligning vectors to your domain-specific notion of similarity, improving search, clustering, and recommendations on your data. โญ Blog + Notebooks: https://unsloth.ai/docs/new/embedding-finetuning Unsloth trains embedding models 1.8-3.3x faster with 20% less VRAM, 2x longer context & no accuracy loss vs. FA2 setups. We'd like to thank Hugging Face and Unsloth contributor: electroglyph for making this possible!
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My Favorite Open Source Models for Jan 2026 1. General Use - https://huggingface.co/deepseek-ai/DeepSeek-V3.2 2. Reasoning - https://huggingface.co/deepseek-ai/DeepSeek-V3.2-Speciale 3. Coding - https://huggingface.co/Qwen/Qwen3-Coder-30B-A3B-Instruct 4. OCR - https://huggingface.co/Qwen/Qwen3-VL-8B-Instruct 5. Image Generation - https://huggingface.co/black-forest-labs/FLUX.2-dev 6. Image Editing - https://huggingface.co/Qwen/Qwen-Image-Edit-2509 What model do you use regularly?
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FunctionGemma Tuning Lab is a new no-code tool by @google that lets you fine-tune a model directly from the browser, with no coding knowledge required, using TRL behind the scenes. blog: https://developers.googleblog.com/a-guide-to-fine-tuning-functiongemma/ try it out: https://huggingface.co/spaces/google/functiongemma-tuning-lab This example builds on a more advanced one for learning fine-tuning with SFT using TRL: https://ai.google.dev/gemma/docs/functiongemma/finetuning-with-functiongemma
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