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You can now run gpt-oss-120b & 20b locally with our GGUFs! 🦥 Run OpenAI's 120b model on 66GB RAM & 20b model on 14GB RAM. Both in original precision. Uploads includes our chat template fixes. Guide: docs.unsloth.ai/basics/gpt-oss GGUF: huggingface.co/unsloth/gpt-os…
You can now run gpt-oss-120b & 20b locally with our GGUFs! 🦥 Run OpenAI's 120b model on 66GB RAM & 20b model on 14GB RAM. Both in original precision. Uploads includes our chat template fixes. Guide: docs.unsloth.ai/basics/gpt-oss GGUF: huggingface.co/unsloth/gpt-os… https://t.co/VUmkjjs5ZX
I'm thrilled to announce the definitive course on Claude Code, created with @AnthropicAI and taught by Elie Schoppik @eschoppik. If you want to use highly agentic coding - where AI works autonomously for many minutes or longer, not just completing code snippets - this is it.…
Free for @huggingface pro users: hf.co/nanotron
Fuck ML tutorials. This is a collection of 300 ML system design case studies in real world, from Stripe, Spotify, Netflix, Meta, etc. Perfect for interviews and to learn how it’s done in the battlefield. Wish there was a similar thing for agents!
LeetCode was HARD until I Learned these 15 Patterns: 1. Prefix Sum 2. Two Pointers 3. Sliding Window 4. Fast & Slow Pointers 5. LinkedList In-place Reversal 6. Monotonic Stack 7. Top ‘K’ Elements 8. Overlapping Intervals 9. Modified Binary Search 10. Binary Tree Traversal 11.…
PyTorch in One Hour🤖 A gem of a blog by @rasbt for you to learn all the essential concept of tensors and their usage in PyTorch🚀 Link: sebastianraschka.com/teaching/pytor…
@@DrSJaishankar Sir, my son Anurag Tiwari (Passport: W4768296), hired by Synergy Ship Arabia, boarded vessel JANA 505 at Sharjah, UAE on 29.06.2025. He died the same day. No clear cause of death, no help from company. Kindly help with urgent repatriation of his body. 🙏
‼️Sentence Transformers v5.0 is out! The biggest update yet introduces Sparse Embedding models, encode methods improvements, Router module for asymmetric models & much more. Sparse + Dense = 🔥 hybrid search performance! Details in 🧵
You can now fine-tune Gemma 3n for free with our notebook! Unsloth makes Google Gemma training 1.5x faster with 50% less VRAM and 5x longer context lengths - with no accuracy loss. Guide: docs.unsloth.ai/basics/gemma-3… GitHub: github.com/unslothai/unsl… Colab: colab.research.google.com/github/unsloth…
Since it's summer, and more or less internship and tech interview season, I made all 30 chapters of my Machine Learning Q and AI book freely available for the summer: sebastianraschka.com/books/ml-q-and… Hope it’s helpful! Happy reading, and good luck if you are interviewing!
We made a Guide on mastering LoRA Hyperparameters, so you can learn to fine-tune LLMs correctly! Learn to: • Train smarter models with fewer hallucinations • Choose optimal: learning rates, epochs, LoRA rank, alpha • Avoid overfitting & underfitting 🔗docs.unsloth.ai/get-started/fi…
Let's fine-tune DeepSeek-R1 (distilled Llama) 100% locally:
PPO and GRPO — a workflow breakdown of the most popular reinforcement learning algorithms ➡️ Proximal Policy Optimization (PPO): The Stable Learner It’s used everywhere from dialogue agents to instruction tuning as it balances between learning fast and staying safe. ▪️ How PPO…
AI Engineering Hub just crossed 10k GitHub stars! It’s 100% open-source and hosts 70+ free hands-on demos. Here are 10 MCP, RAG, and AI Agents projects for AI engineers:
Learn to build and deploy GenAI pipelines in "Orchestrating Workflows for GenAI Applications", built in partnership with @astronomerio and taught by Kenten Danas, the company's DevRel Senior Manager, and Tamara Fingerlin, developer advocate. Many GenAI applications require…
Finetune DeepSeek-R1-0528-Qwen3 with GRPO using our free notebook! Our new reward function increases multilingual (or custom domain) response rates by 40%+. Unsloth makes R1 finetuning 2× faster with 70% less VRAM. GitHub: github.com/unslothai/unsl… Colab: colab.research.google.com/github/unsloth…
LLM Compressor just got way easier to use. You can now compress most LLMs directly from their @huggingface model definition. No need to write custom wrappers. This new autowrapper supports 95% of multimodal and decoder models out of the box. Let’s break it down 🧵:
We all believe that base LLMs already know how to reason, thanks to math & code data in pre-training. So why fine-tune all parameters to learn reasoning capacity? 💡In our LIFT paper (accepted by ICML), we identify Principal Weights responsible for reasoning, and show that…

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