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TokDrift: When LLM Speaks in Subwords but Code Speaks in Grammar
Paper • 2510.14972 • Published • 35 -
LightMem: Lightweight and Efficient Memory-Augmented Generation
Paper • 2510.18866 • Published • 113 -
Every Attention Matters: An Efficient Hybrid Architecture for Long-Context Reasoning
Paper • 2510.19338 • Published • 115 -
The Smol Training Playbook
📚2.91kThe secrets to building world-class LLMs
Jonatan Borkowski PRO
j14i
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Recent Activity
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danielhanchen's
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1 day ago
You can now fine-tune embedding models in our free Unsloth notebook! 🤗
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.
⭐ 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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mahimairaja's
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1 day ago
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?
reacted
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sergiopaniego's
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1 day ago
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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