@UnslothAI Unsloth AI

Unsloth AI posts on X about ai, vram, open ai, how to the most. They currently have [------] followers and [---] posts still getting attention that total [-------] engagements in the last [--] hours.

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Social Influence

Social category influence technology brands 24% stocks 16% finance 2% automotive brands 1% celebrities 1% vc firms 1% products 1%

Social topic influence ai #1545, vram #24, open ai 10%, how to 8%, agentic #7, llm #10, inference #408, performance #641, faster #198, collab 4%

Top accounts mentioned or mentioned by @alibabaqwen @grok @danielhanchen @huggingface @zaiorg @nvidiaaidev @amdindia @vipulgupta2048 @foley2k2 @scheminglunatic @deepseekai @nvidia @pytorch @ai_homelab @bygregorr @letechlead @emmanuel_mr18 @aifreak_ @agentcommunity_ @xai

Top assets mentioned Alphabet Inc Class A (GOOGL) IBM (IBM) Flex Ltd. Ordinary Shares (FLEX)

Top Social Posts

Top posts by engagements in the last [--] hours

"You can now train LLMs [--] faster with no accuracy loss via our new RoPE and MLP kernels. Our Triton kernels plus smart auto packing delivers [--] faster training & 30% less VRAM vs optimized FA3 setups. Train Qwen3-4B 3x faster on just 3.9GB VRAM. Blog: https://docs.unsloth.ai/new/3x-faster-training-packing https://docs.unsloth.ai/new/3x-faster-training-packing"
X Link 2025-12-10T14:41Z 43.6K followers, 628K engagements

"Note that VRAM is not required. You can run on a Mac with 256GB unified memory with similar speeds or [---] RAM without VRAM. You can even run with much less compute (e.g. 80GB RAM) as it'll offload but it'll be slower. https://twitter.com/i/web/status/2016532064955191619 https://twitter.com/i/web/status/2016532064955191619"
X Link 2026-01-28T15:21Z 43.6K followers, 16.5K engagements

"We successfully trained an LLM without human intervention using Claude Code. We made a guide on how to do this with local LLMs via Claude Code and OpenAI Codex. Connect GLM-4.7-Flash to your server and start agentic coding locally Guide: https://unsloth.ai/docs/basics/claude-codex https://unsloth.ai/docs/basics/claude-codex"
X Link 2026-01-29T15:50Z 43.6K followers, 138.1K engagements

"Qwen releases Qwen3-Coder-Next. 💜 The new 80B MoE model excels at agentic coding & local use. With 256K context it delivers similar performance to models with 10-20 more active parameters. Run on 46GB RAM or less. Guide: GGUF: https://huggingface.co/unsloth/Qwen3-Coder-Next-GGUF https://unsloth.ai/docs/models/qwen3-coder-next 🚀 IntroducingQwen3-Coder-Next an open-weight LM built for coding agents & local development. Whats new: 🤖 Scaling agentic training:800K verifiable tasks + executable envs 📈 EfficiencyPerformance Tradeoff: achieves strong results on SWE-Bench Pro with 80B total params"
X Link 2026-02-03T16:11Z 43.6K followers, 239.3K engagements

"You can now train MoE models [--] faster with 35% less VRAM via our new Triton kernels (no accuracy loss). Train gpt-oss locally on 12.8GB VRAM. In collab with @HuggingFace Unsloth trains DeepSeek Qwen3 GLM faster. Repo: Blog: https://unsloth.ai/docs/new/faster-moe https://github.com/unslothai/unsloth https://unsloth.ai/docs/new/faster-moe https://github.com/unslothai/unsloth"
X Link 2026-02-10T15:25Z 43.6K followers, 209.1K engagements

"OpenAI gpt-oss with ultra long context is here🚀 Introducing Unsloth Flex Attention which enables 61K context for gpt-oss bf16 training on a 80GB GPU. Unsloth achieves 8longer context 50% less VRAM & 1.5faster training vs. all implementations. https://docs.unsloth.ai/basics/long-context-gpt-oss-training https://docs.unsloth.ai/basics/long-context-gpt-oss-training"
X Link 2025-08-28T16:48Z 43.3K followers, 142.4K engagements

"NVIDIA made a beginner's guide to fine-tuning LLMs with Unsloth 💚 You'll learn about: - Training methods: LoRA FFT RL - When to fine-tune and why + use-cases - Amount of data and VRAM needed - How to train locally on DGX Spark RTX GPUs & more Guide: https://blogs.nvidia.com/blog/rtx-ai-garage-fine-tuning-unsloth-dgx-spark/ https://blogs.nvidia.com/blog/rtx-ai-garage-fine-tuning-unsloth-dgx-spark/"
X Link 2025-12-22T13:42Z 42.3K followers, 140K engagements

"Qwen releases Qwen-Image-2512 a new SOTA text-to-image model. 💜 It's the top performing open diffusion model on AI Arena and has more realistic + accurate images/text. Run locally with 14GB RAM via our Dynamic GGUF Guide: GGUF: https://huggingface.co/unsloth/Qwen-Image-2512-GGUF https://unsloth.ai/docs/models/qwen-image-2512 🎁ANewYeargiftfromQwenQwen-Image-2512ishere. 🚀OurDecemberupgradetoQwen-ImagejustintimefortheNewYear. ✨Whatsnew: MorerealistichumansdramaticallyreducedAIlookricherfacialdetails Finernaturaltexturessharperlandscapeswater https://t.co/8X6AVcJCIG"
X Link 2025-12-31T09:34Z 43.2K followers, 120.2K engagements

"@Kimi_Moonshot Congrats guys & thank you for this amazing open release 🌙 We're working on Dynamic GGUFs so you guys can run it locally: https://huggingface.co/unsloth/Kimi-K2.5-GGUF https://huggingface.co/unsloth/Kimi-K2.5-GGUF"
X Link 2026-01-27T07:20Z 43.3K followers, 101.9K engagements

"@Alibaba_Qwen Thank you so much for releasing an open-source LLM for fast and smart coding 🥰 We made GGUFs so you can run Qwen3-Coder-Next locally on 46GB RAM or less: https://huggingface.co/unsloth/Qwen3-Coder-Next-GGUF https://huggingface.co/unsloth/Qwen3-Coder-Next-GGUF"
X Link 2026-02-03T16:21Z 43.4K followers, 23.4K engagements

"@Alibaba_Qwen We're super excited for more Qwen models this year 👀🥰 Let's go open-source"
X Link 2026-02-03T17:16Z 43.3K followers, [----] engagements

"GLM-4.7-Flash GGUFs now produce significantly better outputs after recent llama.cpp bug fixes. We reconverted and updated the GGUFs. Run 4-bit locally on 18GB RAM. To get fixes re-download & use inference parameters by @Zai_org. Updated GGUFs: https://huggingface.co/unsloth/GLM-4.7-Flash-GGUF You can now run GLM-4.7-Flash locally on your device🔥 GLM-4.7-Flash is the best performing 30B model on SWE-Bench and GPQA. With 200K context it excels at coding agents chat & reasoning. Run local with 24GB RAM. Guide: https://t.co/SpJxl00VIa GGUF: https://t.co/aTuUxu32z3 https://t.co/3MwNRe3iva"
X Link 2026-01-21T13:28Z 43.5K followers, 153K engagements

"RT @Alibaba_Qwen: 💜💜💜💜💜 Thanks for the support from day 0"
X Link 2026-02-03T17:00Z 43.5K followers, [--] engagements

"You can now fine-tune embedding models in our free notebook Improve retrieval and RAG with better semantic search & similarity. Unsloth trains 2x faster 20% less VRAM 2x context & no accuracy loss Blog: EmbeddingGemma (300M): https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/EmbeddingGemma_(300M).ipynb https://unsloth.ai/docs/new/embedding-finetuning https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/EmbeddingGemma_(300M).ipynb https://unsloth.ai/docs/new/embedding-finetuning"
X Link 2026-01-22T16:08Z 43.5K followers, 81.5K engagements

"You can now run GLM-4.7-Flash locally on your device🔥 GLM-4.7-Flash is the best performing 30B model on SWE-Bench and GPQA. With 200K context it excels at coding agents chat & reasoning. Run local with 24GB RAM. Guide: GGUF: https://huggingface.co/unsloth/GLM-4.7-Flash-GGUF https://unsloth.ai/docs/models/glm-4.7-flash Introducing GLM-4.7-Flash: Your local coding and agentic assistant. Setting a new standard for the 30B class GLM-4.7-Flash balances high performance with efficiency making it the perfect lightweight deployment option. Beyond coding it is also recommended for creative writing"
X Link 2026-01-20T05:22Z 43.5K followers, 335.6K engagements

"You can now run Kimi K2.5 locally 🔥 We shrank the 1T model to 240GB (-60%) via Dynamic 1-bit. Run at [--] tok/s on 240GB VRAM/RAM. 2-bit is recommended as it passes our code tests. Run near full precision on 622GB. Guide: GGUF: https://huggingface.co/unsloth/Kimi-K2.5-GGUF https://unsloth.ai/docs/models/kimi-k2.5 🥝 Meet Kimi K2.5 Open-Source Visual Agentic Intelligence. 🔹 Global SOTA on Agentic Benchmarks: HLE full set (50.2%) BrowseComp (74.9%) 🔹 Open-source SOTA on Vision and Coding: MMMU Pro (78.5%) VideoMMMU (86.6%) SWE-bench Verified (76.8%) 🔹 Code with Taste: turn chats"
X Link 2026-01-28T13:59Z 43.5K followers, 464.5K engagements

"@Zai_org Congrats guys GLM-4.7-Flash is actually one of the most popular models we've ever seen 🔥👏 https://huggingface.co/unsloth/GLM-4.7-Flash-GGUF https://huggingface.co/unsloth/GLM-4.7-Flash-GGUF"
X Link 2026-02-10T13:09Z 43.5K followers, [----] engagements

"RT @NVIDIAAIDev: This is an incredible performance breakthrough from @UnslothAI. 12x faster fine-tuning 35% less VRAM all with no loss i"
X Link 2026-02-11T01:45Z 43.5K followers, [---] engagements

"@Zai_org Congrats guys on release & thank you for supporting open-source 👏 🥰 We uploaded GLM-5 GGUFs so people can run it locally: https://huggingface.co/unsloth/GLM-5-GGUF https://huggingface.co/unsloth/GLM-5-GGUF"
X Link 2026-02-11T19:20Z 43.6K followers, 16.6K engagements

"DeepSeek releases DeepSeek-OCR [--]. 🐋 The new 3B model achieves SOTA visual document and OCR understanding. DeepEncoder V2 is introduced which enables the model scan images in same logical order as humans boosting OCR accuracy. Instead of traditional vision LLMs which read an image in a fixed grid (top-left bottom-right) DeepEncoder V2 first builds a global understanding then learns a human-like reading order - what to attend to first next and so on. This improves OCR on complex layouts helping it follow columns link labels to values read tables coherently and handle mixed text + structure"
X Link 2026-01-27T06:09Z 43.6K followers, 222.9K engagements

"For tutorials on how to Run & Fine-tune DeepSeek-OCR [--] you can read our guide: Inference & training for the model is already supported in Unsloth. https://unsloth.ai/docs/models/deepseek-ocr-2 https://unsloth.ai/docs/models/deepseek-ocr-2"
X Link 2026-01-27T09:13Z 43.6K followers, [----] engagements

"We created a tool-calling guide for local LLMs Learn how to use any open model like Qwen3-Coder-Next and GLM-4.7-Flash for function calling. We provide hands-on examples for: story writing Python execution terminal tool calls maths and more. Guide: https://unsloth.ai/docs/basics/tool-calling-guide-for-local-llms https://unsloth.ai/docs/basics/tool-calling-guide-for-local-llms"
X Link 2026-02-05T16:04Z 43.6K followers, 46.8K engagements

"You can now run GLM-5 locally🔥 GLM-5 is a new open SOTA agentic coding & chat LLM with 200K context. We shrank the 744B model from 1.65TB to 241GB (-85%) via Dynamic 2-bit. Runs on a 256GB Mac or RAM/VRAM setups. Guide: GGUF: https://huggingface.co/unsloth/GLM-5-GGUF https://unsloth.ai/docs/models/glm-5 Introducing GLM-5: From Vibe Coding to Agentic Engineering GLM-5 is built for complex systems engineering and long-horizon agentic tasks. Compared to GLM-4.5 it scales from 355B params (32B active) to 744B (40B active) with pre-training data growing from 23T to 28.5T tokens."
X Link 2026-02-12T12:55Z 43.6K followers, 225.6K engagements

"You can now run MiniMax-2.5 locally 🚀 At 230B parameters MiniMax-2.5 is the strongest LLM under 700B params delivering SOTA agentic coding & chat. Run Dynamic 3/4-bit on a 128GB Mac for [--] tokens/s. Guide: GGUF: https://huggingface.co/unsloth/MiniMax-M2.5-GGUF https://unsloth.ai/docs/models/minimax-2.5 Introducing M2.5 an open-source frontier model designed for real-world productivity. - SOTA performance at coding (SWE-Bench Verified 80.2%) search (BrowseComp 76.3%) agentic tool-calling (BFCL 76.8%) & office work. - Optimized for efficient execution 37% faster at complex"
X Link 2026-02-15T13:41Z 43.6K followers, 137.4K engagements

"You can now run Qwen3.5 locally 💜 Qwen3.5-397B-A17B is an open MoE vision reasoning LLM for agentic coding & chat. It performs on par with Gemini [--] Pro Claude Opus [---] & GPT-5.2. Run 4-bit on 256GB Mac / RAM. Guide: GGUF: https://huggingface.co/unsloth/Qwen3.5-397B-A17B-GGUF https://unsloth.ai/docs/models/qwen3.5 🚀 Qwen3.5-397B-A17B is here: The first open-weight model in the Qwen3.5 series. 🖼Native multimodal. Trained for real-world agents. ✨Poweredbyhybridlinearattention+sparseMoEandlarge-scaleRLenvironmentscaling. ⚡8.6x19.0xdecodingthroughputvsQwen3-Max 🌍201 https://t.co/Pq0qIk54MB"
X Link 2026-02-16T09:43Z 43.6K followers, [----] engagements

"@Alibaba_Qwen Thank you for another epic open-source release 💜 We made some Qwen3.5 GGUFs so you can run it locally: https://huggingface.co/unsloth/Qwen3.5-397B-A17B-GGUF https://huggingface.co/unsloth/Qwen3.5-397B-A17B-GGUF"
X Link 2026-02-16T09:46Z 43.6K followers, [----] engagements

"Learn to fine-tune OpenAI gpt-oss with our new step-by-step guide Learn about: Local gpt-oss training + inference FAQ & tips Evaluation hyperparameters & overfitting Reasoning effort Data prep Run & saving your LLM to llama.cpp GGUF HF https://docs.unsloth.ai/basics/tutorial-how-to-fine-tune-gpt-oss https://docs.unsloth.ai/basics/tutorial-how-to-fine-tune-gpt-oss https://docs.unsloth.ai/basics/tutorial-how-to-fine-tune-gpt-oss https://docs.unsloth.ai/basics/tutorial-how-to-fine-tune-gpt-oss"
X Link 2025-08-18T14:02Z 31.2K followers, 41.9K engagements

"@QuixiAI @deepseek_ai Thanks Eric & everyone we really appreciate the support Huge thanks to @ggerganov and the llama.cpp team for making this possible as well and of course to the DeepSeek team 🥰"
X Link 2025-08-23T22:59Z 30.7K followers, [---] engagements

"@elonmusk @xai Thanks for supporting open-source We'll try to investigate how we can create Dynamic GGUFs so everyone can run it locally 👀"
X Link 2025-08-23T23:05Z 30.7K followers, 50.1K engagements

"RL used to be memory hungry but not anymore Introducing our new kernels & algos that allows faster RL with 50% less VRAM [--] more context & no accuracy loss. RL before required GPU splitting between training & inference. Now with Standby you don't http://docs.unsloth.ai/basics/memory-efficient-rl http://docs.unsloth.ai/basics/memory-efficient-rl"
X Link 2025-09-04T16:02Z 31.7K followers, 69.5K engagements

"You can now run @xAI Grok [---] locally on just 120GB RAM 🚀 The 270B parameter model runs [--] t/s on a 128GB Mac with our Dynamic 3-bit GGUF. We shrunk the 539GB model to 118GB (-80%) & left key layers in higher 8-bits Guide: GGUF: https://huggingface.co/unsloth/grok-2-GGUF https://docs.unsloth.ai/basics/grok-2 https://huggingface.co/unsloth/grok-2-GGUF https://docs.unsloth.ai/basics/grok-2 https://huggingface.co/unsloth/grok-2-GGUF https://docs.unsloth.ai/basics/grok-2 https://huggingface.co/unsloth/grok-2-GGUF https://docs.unsloth.ai/basics/grok-2"
X Link 2025-09-08T13:41Z 31.9K followers, 109K engagements

"Unsloth Dynamic GGUFs were introduced early this year where we selectively quantized some layers to as low as 1-bit and important layers to higher bits (6 8-bit). Blog post: Our Dynamic GGUFs consistently performs better on Aider Polyglot when compared to other community quants for the same model size and quant type. To ensure a fair comparison we do the following: We select similar sized files and bit types to each Unsloth quant. We use our fixed chat template if the community quant fails to execute the benchmark. We found some community quants having errors and this gets fixed by using our"
X Link 2025-09-10T15:21Z 31.6K followers, [----] engagements

"You can now train Vision LLMs with Reinforcement Learning in our free notebook Unsloth VLM RL via GRPO: [---] faster 90% less VRAM [--] longer context & no accuracy loss. Guide: GitHub: Qwen2.5-VL Colab: https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Qwen2_5_7B_VL_GRPO.ipynb https://github.com/unslothai/unsloth https://docs.unsloth.ai/new/vision-reinforcement-learning-vlm-rl https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Qwen2_5_7B_VL_GRPO.ipynb https://github.com/unslothai/unsloth"
X Link 2025-09-16T16:24Z 32.8K followers, 142.2K engagements

"@Alibaba_Qwen We're all super excited for Qwen3-VL 🥰👀"
X Link 2025-09-17T04:07Z 31.6K followers, [----] engagements

"Mistral releases Magistral [---] their new reasoning models 🔥 Magistral-Small-2509 excels at coding + math and is a major upgrade over Magistral [---]. Run the 24B model locally with 32GB RAM. Fine-tune with free notebook: GGUFs: https://huggingface.co/unsloth/Magistral-Small-2509-GGUF https://docs.unsloth.ai/models/magistral-how-to-run-and-fine-tune#fine-tuning-magistral-with-unsloth https://huggingface.co/unsloth/Magistral-Small-2509-GGUF https://docs.unsloth.ai/models/magistral-how-to-run-and-fine-tune#fine-tuning-magistral-with-unsloth https://huggingface.co/unsloth/Magistral-Small-2509-GGUF"
X Link 2025-09-17T15:55Z 32.4K followers, 51.2K engagements

"@deepseek_ai Thank you for another update We're excited to make Dynamic GGUFs so you all can run it locally 🐋 https://huggingface.co/unsloth/DeepSeek-V3.1-Terminus-GGUF https://huggingface.co/unsloth/DeepSeek-V3.1-Terminus-GGUF"
X Link 2025-09-22T13:42Z 32K followers, 20.8K engagements

"We're teaming up with @MistralAI and @NVIDIA for an Unsloth event on Tues Oct [--] at @YCombinator's office 🦥 Join us in San Francisco for a night of talks merch and more. Food & drinks provided. RSVP required http://lu.ma/unsloth-yc http://lu.ma/unsloth-yc http://lu.ma/unsloth-yc http://lu.ma/unsloth-yc"
X Link 2025-09-22T14:05Z 32.3K followers, 30.2K engagements

"You can now train OpenAI gpt-oss with Reinforcement Learning in our free notebook This notebook automatically creates faster kernels via RL. Unsloth RL achieves the fastest inference & lowest VRAM vs. any setup - [--] accuracy loss gpt-oss-20b GRPO Colab: https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/gpt-oss-(20B)-GRPO.ipynb https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/gpt-oss-(20B)-GRPO.ipynb https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/gpt-oss-(20B)-GRPO.ipynb"
X Link 2025-09-26T15:45Z 32.7K followers, 123.4K engagements

"The notebook shows how to counteract reward-hacking which is one of RL's biggest challenges. Blog + details: Since inference is crucial and vLLM is incompatible with gpt-oss RL we developed custom algorithms in Unsloth to deliver the fastest inference (3 faster) the lowest VRAM usage (50% less) and longest context lengths (8 more) - without any accuracy degradation. https://docs.unsloth.ai/new/gpt-oss-reinforcement-learning https://docs.unsloth.ai/new/gpt-oss-reinforcement-learning https://docs.unsloth.ai/new/gpt-oss-reinforcement-learning"
X Link 2025-09-26T15:45Z 32.6K followers, [----] engagements

"Join us @Pytorch and @AMD for a Virtual Hackathon on Oct 18-20. 🔥 Win $10K in prizes by training the best AI agent via Unsloth Sign up here: https://luma.com/4i64p3ec https://luma.com/4i64p3ec"
X Link 2025-09-27T17:11Z 32.4K followers, [---] engagements

"@deepseek_ai Thank you guys once again for supporting open-source and making AI more accessible Hopefully we'll be able to make GGUFs to allow everyone to run DeepSeek-V3.2-Exp locally 🐋"
X Link 2025-09-29T12:22Z 32.6K followers, 21.7K engagements

"LoRA in reinforcement learning (RL) can match full-finetuning performance when done right 💡 A new @thinkymachines post shows how using 10x larger learning rates applying LoRA on all layers & more LoRA at rank=1 even works. We're excited to have collaborated on this blog LoRA makes fine-tuning more accessible but it's unclear how it compares to full fine-tuning. We find that the performance often matches closely---more often than you might expect. In our latest Connectionism post we share our experimental results and recommendations for LoRA. https://t.co/DcVmUKeOyw LoRA makes fine-tuning"
X Link 2025-09-29T20:59Z 32.7K followers, 61.7K engagements

"IBM releases Granite-4.0 their new series of open models Run the 'Micro' 3B model on 4GB RAM or 'Small' 32B on 40GB RAM. Granite-4.0 excels at agentic tasks doc analysis RAG edge AI applications & more Dynamic GGUFs: Guide: https://docs.unsloth.ai/new/ibm-granite-4.0 https://huggingface.co/collections/unsloth/granite-40-68ddf64b4a8717dc22a9322d https://docs.unsloth.ai/new/ibm-granite-4.0 https://huggingface.co/collections/unsloth/granite-40-68ddf64b4a8717dc22a9322d https://docs.unsloth.ai/new/ibm-granite-4.0 https://huggingface.co/collections/unsloth/granite-40-68ddf64b4a8717dc22a9322d"
X Link 2025-10-02T14:14Z 32.8K followers, 42.7K engagements

"@Alibaba_Qwen Go Qwen team Thank you for releasing smaller models 😍"
X Link 2025-10-04T01:59Z 32.7K followers, [----] engagements

"@AMD @OpenAI Congrats We're also very excited to enable local efficient fine-tuning and reinforcement learning for AMD GPUs very soon 👀🦥"
X Link 2025-10-06T12:48Z 32.5K followers, [----] engagements

"Thank you @dkundel from OpenAI and Barath from NVIDIA for the collab. 🥰 Watch Dominik's full gpt-oss presentation: https://www.youtube.com/watchv=1HL2YHRj270 https://www.youtube.com/watchv=1HL2YHRj270"
X Link 2025-10-09T14:23Z 32.8K followers, [----] engagements

"DeepSeek-R1 GGUF's are now on @HuggingFace Includes all Llama & Qwen distilled models + [--] to 8-bit quantized versions. How to run R1: DeepSeek-R1 Collection: https://huggingface.co/collections/unsloth/deepseek-r1-all-versions-678e1c48f5d2fce87892ace5 https://unsloth.ai/blog/deepseek-r1 https://huggingface.co/collections/unsloth/deepseek-r1-all-versions-678e1c48f5d2fce87892ace5 https://unsloth.ai/blog/deepseek-r1 https://huggingface.co/collections/unsloth/deepseek-r1-all-versions-678e1c48f5d2fce87892ace5 https://unsloth.ai/blog/deepseek-r1"
X Link 2025-01-20T15:06Z 18.3K followers, 68.4K engagements

"You can now reproduce DeepSeek-R1's reasoning on your own local device Experience the "Aha" moment with just 7GB VRAM. Unsloth reduces GRPO training memory use by 80%. 15GB VRAM can transform Llama-3.1 (8B) & Phi-4 (14B) into reasoning models. Blog: http://unsloth.ai/blog/r1-reasoning http://unsloth.ai/blog/r1-reasoning http://unsloth.ai/blog/r1-reasoning http://unsloth.ai/blog/r1-reasoning"
X Link 2025-02-06T18:03Z 24K followers, [----] engagements

"You can now fine-tune TTS models with Unsloth Train run and save models like Sesame-CSM and OpenAI's Whisper locally with our free notebooks. Unsloth makes TTS training 1.5x faster with 50% less VRAM. GitHub: Docs & Notebooks: https://docs.unsloth.ai/basics/text-to-speech-tts-fine-tuning https://github.com/unslothai/unsloth https://docs.unsloth.ai/basics/text-to-speech-tts-fine-tuning https://github.com/unslothai/unsloth https://docs.unsloth.ai/basics/text-to-speech-tts-fine-tuning https://github.com/unslothai/unsloth https://docs.unsloth.ai/basics/text-to-speech-tts-fine-tuning"
X Link 2025-05-15T16:38Z 33.8K followers, 127.3K engagements

"We made a repo with 100+ Fine-tuning notebooks all in once place Has guides & examples for: Tool-calling Classification Synthetic data BERT TTS Vision LLMs GRPO DPO SFT CPT Dataprep eval saving Llama Qwen Gemma Phi DeepSeek https://github.com/unslothai/notebooks/ https://github.com/unslothai/notebooks/ https://github.com/unslothai/notebooks/ https://github.com/unslothai/notebooks/"
X Link 2025-06-04T13:29Z 36.7K followers, 84.7K engagements

"We made a complete Guide on Reinforcement Learning for LLMs Learn about: RL's goal & why it's key to building intelligent AI agents Why o3 Claude [--] & R1 use RL GRPO RLHF DPO reward functions Training your own local R1 model via Unsloth https://docs.unsloth.ai/basics/reinforcement-learning-guide https://docs.unsloth.ai/basics/reinforcement-learning-guide"
X Link 2025-06-17T14:36Z 39.7K followers, 70.5K engagements

"You can now run the worlds most powerful Western open models locally The hybrid reasoning 671B model matches o3 & Claude-4-Opus in performance. Trained on Llama [--] & DeepSeek-R1 Cogito-v2 has [--] variantseach setting new benchmarks. Guide + GGUFs: https://docs.unsloth.ai/basics/tutorials-how-to-fine-tune-and-run-llms/cogito-v2-how-to-run-locally Today we are releasing [--] hybrid reasoning models of sizes 70B 109B MoE 405B 671B MoE under open license. These are some of the strongest LLMs in the world and serve as a proof of concept for a novel AI paradigm - iterative self-improvement (AI systems"
X Link 2025-08-01T00:49Z 33.3K followers, 40.7K engagements

"@OpenAI Amazing guys Super excited to support them so y'all can run & fine-tune them locally 🤩"
X Link 2025-08-05T17:05Z 36.6K followers, 27.6K engagements

"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: GGUF: https://huggingface.co/unsloth/gpt-oss-20b-GGUF https://docs.unsloth.ai/basics/gpt-oss https://huggingface.co/unsloth/gpt-oss-20b-GGUF https://docs.unsloth.ai/basics/gpt-oss Our open models are here. Both of them. https://t.co/9tFxefOXcg https://huggingface.co/unsloth/gpt-oss-20b-GGUF https://docs.unsloth.ai/basics/gpt-oss https://huggingface.co/unsloth/gpt-oss-20b-GGUF"
X Link 2025-08-05T20:10Z 38.9K followers, 95.8K engagements

"Google releases Gemma [--] 270M a new model that runs locally on just [---] GB RAM.✨ Trained on 6T tokens it runs fast on phones & handles chat coding & math. Run at [--] t/s with our Dynamic GGUF or fine-tune via Unsloth & export to your phone. Details: https://docs.unsloth.ai/basics/gemma-3-how-to-run-and-fine-tune https://docs.unsloth.ai/basics/gemma-3-how-to-run-and-fine-tune Introducing Gemma [--] 270M 🔥 🤏A tiny model Just [---] million parameters 🧠 Very strong instruction following 🤖 Fine-tune in just a few minutes with a large vocabulary to serve as a high-quality foundation"
X Link 2025-08-14T16:18Z 33.8K followers, 156.6K engagements

"Can a 1-bit or 3-bit quantized model outperform GPT-4.1 or Claude-Opus-4 Yes Today we're excited to show how LLMs like DeepSeek-V3.1 can be quantized to just 1-bit or 3-bit and still beat SOTA models like Claude-Opus-4 (thinking) on Aider Polyglot. Details and blog below"
X Link 2025-09-10T15:21Z 38.1K followers, 165.4K engagements

"We made a free notebook that fine-tunes IBM Granite [---] into a powerful support agent This agent will enable real-time analysis & solving of customer interactions. You'll also learn how to train models using data from Google Sheets. Colab Notebook: https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Granite4.0.ipynb https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Granite4.0.ipynb https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Granite4.0.ipynb"
X Link 2025-10-02T15:37Z 33.4K followers, 50.6K engagements

"OpenAI shows how gpt-oss can autonomously beat [----] using reinforcement learning (RL). Training was done locally with Unsloth on NVIDIA DGX Spark. You can also do it free on Colab. 🦥 OpenAI DevDay notebook: https://github.com/openai/gpt-oss/blob/main/examples/reinforcement-fine-tuning.ipynb https://github.com/openai/gpt-oss/blob/main/examples/reinforcement-fine-tuning.ipynb https://github.com/openai/gpt-oss/blob/main/examples/reinforcement-fine-tuning.ipynb https://github.com/openai/gpt-oss/blob/main/examples/reinforcement-fine-tuning.ipynb"
X Link 2025-10-09T13:50Z 33.7K followers, 97.6K engagements

"You can now train models up to 200B parameters locally on NVIDIA DGX Spark with Unsloth 🦥 Fine-tune RL & deploy OpenAI gpt-oss-120b via our free notebook in 68GB unified memory: Read our step-by-step guide in collab with NVIDIA https://docs.unsloth.ai/new/fine-tuning-llms-with-nvidia-dgx-spark-and-unsloth https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/gpt-oss-(120B)_A100-Fine-tuning.ipynb https://docs.unsloth.ai/new/fine-tuning-llms-with-nvidia-dgx-spark-and-unsloth"
X Link 2025-10-15T13:43Z 33.8K followers, 46.4K engagements

"You can now fine-tune Qwen3-VL (8B) for free with our notebook Unsloth trains VLMs 1.7x faster with 60% less VRAM and 8x longer context - no accuracy loss. GitHub: Qwen3-VL GRPO Colab: Qwen3-VL Colab: https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Qwen3_VL_(8B)-Vision.ipynb https://docs.unsloth.ai/models/qwen3-vl-run-and-fine-tune#fine-tuning-qwen3-vl https://github.com/unslothai/unsloth https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Qwen3_VL_(8B)-Vision.ipynb https://docs.unsloth.ai/models/qwen3-vl-run-and-fine-tune#fine-tuning-qwen3-vl"
X Link 2025-10-16T13:51Z 34.9K followers, 107.9K engagements

"We just hit [---] million lifetime downloads on Hugging Face 🦥🤗 Huge thanks to all of you The amazing community model creators and HF team. 💖"
X Link 2025-10-21T13:45Z 33.4K followers, 31.7K engagements

"You can now quantize LLMs to 4-bit and recover 70% accuracy via Quantization-Aware Training. We teamed up with @PyTorch to show how QAT enables: 4x less VRAM with no inference overhead 1-3% increase in raw accuracy (GPQA MMLU Pro) Notebook & Blog: https://docs.unsloth.ai/new/quantization-aware-training-qat https://docs.unsloth.ai/new/quantization-aware-training-qat https://docs.unsloth.ai/new/quantization-aware-training-qat https://docs.unsloth.ai/new/quantization-aware-training-qat"
X Link 2025-10-22T15:36Z 34K followers, 44.9K engagements

"We showcased our one click fine-tuning UI for the first time at the NVIDIA x Mistral AI x Unsloth event at Y Combinator 🔥🦥 Huge thanks to everyone who came 🥰 🙌 Thank you to everyone who joined us at AI Dev Night with @UnslothAI and @MistralAI. We're looking forward to meeting more of you at #PyTorchCon #OpenSourceAIWeek. https://t.co/xCJrGMrbZ4 🙌 Thank you to everyone who joined us at AI Dev Night with @UnslothAI and @MistralAI. We're looking forward to meeting more of you at #PyTorchCon #OpenSourceAIWeek. https://t.co/xCJrGMrbZ4"
X Link 2025-10-23T19:54Z 38.5K followers, 14.4K engagements

"We teamed up with @NVIDIA to teach you how to fine-tune LLMs on Blackwell & RTX [--] GPUs. Unsloth makes training on Blackwell up to [--] faster with 70% less VRAM - no accuracy loss. Learn how to use our new Docker image & more in the official NVIDIA Blog: https://developer.nvidia.com/blog/train-an-llm-on-an-nvidia-blackwell-desktop-with-unsloth-and-scale-it/ https://developer.nvidia.com/blog/train-an-llm-on-an-nvidia-blackwell-desktop-with-unsloth-and-scale-it/ https://developer.nvidia.com/blog/train-an-llm-on-an-nvidia-blackwell-desktop-with-unsloth-and-scale-it/"
X Link 2025-10-27T14:02Z 34K followers, 35.2K engagements

"You can now run Qwen3-VL locally 💜 Run the 235B variant for SOTA vision/OCR on 128GB unified memory (dynamic 4-bit). Includes our chat template fixes. Qwen3-VL-2B runs at [--] t/s on 4GB RAM. Fine-tune & RL via Unsloth free notebooks & export to GGUF. https://docs.unsloth.ai/models/qwen3-vl https://docs.unsloth.ai/models/qwen3-vl https://docs.unsloth.ai/models/qwen3-vl https://docs.unsloth.ai/models/qwen3-vl"
X Link 2025-10-31T13:31Z 39.7K followers, 92.8K engagements

"To run Qwen3-VL you can read our step-by-step tutorial and download the GGUFs from our Hugging Face collection: https://huggingface.co/collections/unsloth/qwen3-vl https://huggingface.co/collections/unsloth/qwen3-vl"
X Link 2025-10-31T15:26Z 34.1K followers, [----] engagements

"@Alibaba_Qwen Thank you for the support 💜🦥 Here's our free Colab notebooks for fine-tuning and reinforcement learning (RL) of Qwen3-VL-8B: https://x.com/UnslothAI/status/1978821090135687182 You can now fine-tune Qwen3-VL (8B) for free with our notebook Unsloth trains VLMs 1.7x faster with 60% less VRAM and 8x longer context - no accuracy loss. GitHub: https://t.co/aZWYAt9MMh Qwen3-VL GRPO Colab: https://t.co/HkjYydXDnR Qwen3-VL Colab: https://t.co/r3p2wgIzVS https://x.com/UnslothAI/status/1978821090135687182 You can now fine-tune Qwen3-VL (8B) for free with our notebook Unsloth trains VLMs"
X Link 2025-11-02T03:18Z 34K followers, [----] engagements

"You can now fine-tune DeepSeek-OCR with our free notebook We fine-tuned DeepSeek-OCR improving its language understanding by 89% and reduced Character Error Rate from 149% to 60% Blog: GitHub: Colab: https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Deepseek_OCR_(3B)-Eval.ipynb https://github.com/unslothai/unsloth https://docs.unsloth.ai/new/deepseek-ocr https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Deepseek_OCR_(3B)-Eval.ipynb https://github.com/unslothai/unsloth https://docs.unsloth.ai/new/deepseek-ocr"
X Link 2025-11-04T15:20Z 34.4K followers, 80.6K engagements

"@donvito Most models with up to 32B parameters (e.g. Qwen3-32B) can fine-tune locally with Unsloth on a 24GB VRAM GPU. 🥰 LoRA or FFT will use much more VRAM though. You can find more details about this in our docs"
X Link 2025-11-04T16:42Z 34.1K followers, [----] engagements

"You can now run Kimi K2 Thinking locally with our Dynamic 1-bit GGUFs We shrank the 1T model to 245GB (-62%) & retained 85% of accuracy. Run on 247GB RAM. We also worked with the Kimi team on a system prompt fix. Guide: GGUF: https://huggingface.co/unsloth/Kimi-K2-Thinking-GGUF https://docs.unsloth.ai/models/kimi-k2-thinking-how-to-run-locally https://huggingface.co/unsloth/Kimi-K2-Thinking-GGUF https://docs.unsloth.ai/models/kimi-k2-thinking-how-to-run-locally https://huggingface.co/unsloth/Kimi-K2-Thinking-GGUF https://docs.unsloth.ai/models/kimi-k2-thinking-how-to-run-locally"
X Link 2025-11-08T15:43Z 36.7K followers, 178.2K engagements

"You can also run Kimi K2 Thinking in full precision by using our 4-bit or 5-bit GGUFs since the original model was released as INT4. 🙏 This will require 520GB - 730GB RAM/VRAM for fast inference"
X Link 2025-11-08T17:13Z 36.7K followers, [----] engagements

"You can now run Unsloth GGUFs locally via Docker Run LLMs on Mac or Windows with one line of code or no code at all We collabed with Docker to make Dynamic GGUFs available for everyone Just run: docker model run ai/gpt-oss:20B Guide: https://docs.unsloth.ai/models/how-to-run-llms-with-docker https://docs.unsloth.ai/models/how-to-run-llms-with-docker https://docs.unsloth.ai/models/how-to-run-llms-with-docker https://docs.unsloth.ai/models/how-to-run-llms-with-docker"
X Link 2025-11-17T14:33Z 35.3K followers, 93.2K engagements

"We made a guide on how to deploy LLMs locally with SGLang In collab with @lmsysorg you'll learn to: Deploy fine-tuned LLMs for large scale production Serve GGUFs locally Benchmark inference speed Use on the fly FP8 for 1.6x inference Guide: https://docs.unsloth.ai/basics/inference-and-deployment/sglang-guide https://docs.unsloth.ai/basics/inference-and-deployment/sglang-guide"
X Link 2025-11-21T14:40Z 35.2K followers, 29K engagements

"You can now run FP8 reinforcement learning on consumer GPUs Try DeepSeek-R1s FP8 GRPO at home using only a 5GB GPU. Qwen3-1.7B fits in 5GB VRAM. We collabed with PyTorch to make FP8 RL inference [---] faster. Unsloth: 60% less VRAM [--] longer context. https://docs.unsloth.ai/new/fp8-reinforcement-learning https://docs.unsloth.ai/new/fp8-reinforcement-learning https://docs.unsloth.ai/new/fp8-reinforcement-learning https://docs.unsloth.ai/new/fp8-reinforcement-learning"
X Link 2025-11-25T16:37Z 38K followers, 144.6K engagements

"You can now do 500K context length fine-tuning with Unsloth Train gpt-oss-20b to extend its context window to 530K on 80GB VRAM & 750K+ on 192GB - no accuracy loss. Unsloth's new algorithms + Tiled MLP = 72% less VRAM & 6x more context Blog + Notebook: https://docs.unsloth.ai/new/500k-context-length-fine-tuning https://docs.unsloth.ai/new/500k-context-length-fine-tuning https://docs.unsloth.ai/new/500k-context-length-fine-tuning https://docs.unsloth.ai/new/500k-context-length-fine-tuning"
X Link 2025-12-01T14:45Z 37.7K followers, 41K engagements

"To clarify yes this release supports any LLM or VLM not just gpt-oss - with limited RL support as well. :) More details in our blogpost"
X Link 2025-12-01T15:27Z 35.2K followers, [----] engagements

"Mistral releases Ministral [--] their new reasoning and instruct models 🔥 Ministral [--] comes in 3B 8B and 14B with vision support and best-in-class performance. Run the 14B models locally with 24GB RAM. Guide + Notebook: GGUFs: https://huggingface.co/collections/unsloth/ministral-3 https://docs.unsloth.ai/new/ministral-3 Introducing the Mistral [--] family of models: Frontier intelligence at all sizes. Apache [---]. Details in 🧵 https://t.co/lsrDmhW78u https://huggingface.co/collections/unsloth/ministral-3 https://docs.unsloth.ai/new/ministral-3 Introducing the Mistral [--] family of models: Frontier"
X Link 2025-12-02T15:17Z 40.3K followers, 81.6K engagements

"@Alibaba_Qwen Let's gooo Qwen & open-source 💜🦥"
X Link 2025-12-04T08:36Z 35.3K followers, [---] engagements

"You can now train Mistral Ministral [--] with reinforcement learning in our free notebook You'll GRPO the model to solve sudoku autonomously. Learn about our new reward functions RL environment & reward hacking. Blog: Notebook: https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Ministral_3_(3B)_Reinforcement_Learning_Sudoku_Game.ipynb https://docs.unsloth.ai/new/ministral-3 https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Ministral_3_(3B)_Reinforcement_Learning_Sudoku_Game.ipynb https://docs.unsloth.ai/new/ministral-3"
X Link 2025-12-04T15:01Z 37.7K followers, 41K engagements

"NVIDIA releases Nemotron [--] Nano a new 30B hybrid reasoning model 🔥 Nemotron [--] has a 1M context window and the best in class performance for SWE-Bench reasoning and chat. Run the MoE model locally with 24GB RAM. Guide: GGUF: https://huggingface.co/unsloth/Nemotron-3-Nano-30B-A3B-GGUF https://docs.unsloth.ai/models/nemotron-3 https://huggingface.co/unsloth/Nemotron-3-Nano-30B-A3B-GGUF https://docs.unsloth.ai/models/nemotron-3"
X Link 2025-12-15T14:07Z 38.5K followers, 138.3K engagements

"We teamed up with @NVIDIA and @MatthewBerman to teach you how to do Reinforcement Learning Learn about: - RL environments reward functions & reward hacking - Training OpenAI gpt-oss to automatically solve [----] - Local Windows training with @NVIDIA_AI_PC RTX GPUs - How RLVR (verifiable rewards) works - How to interpret RL metrics like KL Divergence Full video tutorial: https://www.youtube.com/watchv=9t-BAjzBWj8 https://www.youtube.com/watchv=9t-BAjzBWj8"
X Link 2025-12-16T14:31Z 38.5K followers, 51.9K engagements

"Google releases FunctionGemma a new 270M parameter model that runs on just [---] GB RAM.✨ Built for tool-calling run locally on your phone at 50+ tokens/s or fine-tune with Unsloth & deploy to your phone. Docs + Notebook: GGUF: https://huggingface.co/unsloth/functiongemma-270m-it-GGUF https://docs.unsloth.ai/models/functiongemma https://huggingface.co/unsloth/functiongemma-270m-it-GGUF https://docs.unsloth.ai/models/functiongemma Introducing FunctionGemma 🤏270m model for function calling 📱can run in your phone browser or other devices 🤖designed to be specialized for your own tasks"
X Link 2025-12-18T17:22Z 38.2K followers, 219.4K engagements

"@Alibaba_Qwen Congrats guys this is an amazing open-source effort 💜🥰 We made Qwen-Image-Edit-2511 GGUFs so everyone can run it locally 🙏 https://huggingface.co/unsloth/Qwen-Image-Edit-2511-GGUF https://huggingface.co/unsloth/Qwen-Image-Edit-2511-GGUF"
X Link 2025-12-23T16:17Z 38.5K followers, 32.5K engagements

"@NVIDIAAIDev Thanks guys for the constant support 🦥💚"
X Link 2025-12-23T23:29Z 38.1K followers, [---] engagements

"Merry Christmas from Unsloth 🎄🎁 Thank you for all the support this year Were excited to keep shipping open-source next year 🥰"
X Link 2025-12-24T14:53Z 38.5K followers, [----] engagements

"We just crossed [-----] stars on GitHub 🦥 Huge thanks to you every contributor and our amazing community for all your support. Our GitHub repo: https://github.com/unslothai/unsloth https://github.com/unslothai/unsloth"
X Link 2025-12-30T14:32Z 38.6K followers, 19.9K engagements

"@Alibaba_Qwen [----] was so amazing because of Qwen We're super excited for Qwen4 in [----] 💜🥰"
X Link 2025-12-31T04:47Z 38.6K followers, [----] engagements

"@Alibaba_Qwen Thanks guys for the support and day zero access We're excited for more Qwen in [----] 😍🌸"
X Link 2025-12-31T10:13Z 38.6K followers, [----] engagements

"We made a guide on how to run Qwen-Image diffusion models locally Learn to: Run Qwen-Image-2512 and Edit-2511 Use GGUF FP8 in ComfyUI stable-diffusion.cpp diffusers Create workflows & prompts Adjust hyperparams (sampling guidance) Guide: https://unsloth.ai/docs/models/qwen-image-2512 https://unsloth.ai/docs/models/qwen-image-2512 https://unsloth.ai/docs/models/qwen-image-2512 https://unsloth.ai/docs/models/qwen-image-2512"
X Link 2026-01-08T14:37Z 39.7K followers, 30.8K engagements

"@Zai_org Thank you guys for this amazing release You can now run & fine-tune the model locally: https://huggingface.co/unsloth/GLM-4.7-Flash-GGUF https://huggingface.co/unsloth/GLM-4.7-Flash-GGUF"
X Link 2026-01-20T05:13Z 39.8K followers, [----] engagements

"Update: For improved performance please use: --dry-multiplier [---] --temp [---] --top-k [--] --top-p [----] --min-p [----] which should reduce any looping or incorrect output issues. 🙏 --dry-multiplier [---] especially works well. For more information see: https://unsloth.ai/docs/models/glm-4.7-flash#reducing-repetition-and-looping https://unsloth.ai/docs/models/glm-4.7-flash#reducing-repetition-and-looping https://unsloth.ai/docs/models/glm-4.7-flash#reducing-repetition-and-looping https://unsloth.ai/docs/models/glm-4.7-flash#reducing-repetition-and-looping"
X Link 2026-01-20T07:26Z 39.3K followers, [----] engagements

"Run DeepSeek-V3.1 locally on 170GB RAM with Dynamic 1-bit GGUFs🐋 The 715GB model gets reduced to 170GB (-80% size) by smartly quantizing layers. The 1-bit GGUF passes all our code tests & we fixed the chat template Guide: GGUF: https://huggingface.co/unsloth/DeepSeek-V3.1-GGUF https://docs.unsloth.ai/basics/deepseek-v3.1 https://huggingface.co/unsloth/DeepSeek-V3.1-GGUF https://docs.unsloth.ai/basics/deepseek-v3.1"
X Link 2025-08-22T19:50Z 43.3K followers, 60.3K engagements

"Unsloth now has a Docker image 🐳 Train LLMs locally with no setup: just run the image and go. Includes every pre-made Unsloth notebook. Solves dependency or environment issues. Guide: https://docs.unsloth.ai/new/how-to-train-llms-with-unsloth-and-docker https://docs.unsloth.ai/new/how-to-train-llms-with-unsloth-and-docker"
X Link 2025-10-01T13:42Z 43.2K followers, 97.1K engagements

"You can now fine-tune LLMs and deploy them directly on your phone 🚀 We collabed with PyTorch so you can export and run your trained model 100% locally on your iOS or Android device. Deploy Qwen3 on Pixel [--] and iPhone [--] Pro at [--] tokens/sec. Guide: https://docs.unsloth.ai/new/deploy-llms-phone https://docs.unsloth.ai/new/deploy-llms-phone https://docs.unsloth.ai/new/deploy-llms-phone https://docs.unsloth.ai/new/deploy-llms-phone"
X Link 2025-12-17T14:55Z 41.6K followers, 136.6K engagements

"You can now fine-tune LLMs with Unsloth then deploy them in @LMStudio 🦥👾 We made a free notebook to fine-tune FunctionGemma (270M) so it thinks before calling tools then export the model to GGUF for deployment in LM Studio. Notebook: https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/FunctionGemma_(270M)-LMStudio.ipynb We worked with @UnslothAI on a new beginner's guide: How to fine-tune FunctionGemma and run it locally 🔧 Train FunctionGemma for custom tool calls ✨ Convert it to GGUF + import into LM Studio 👾 Serve it locally and use it in your code Step-by-step"
X Link 2025-12-23T15:51Z 41.7K followers, 60.7K engagements

"You can now do reinforcement learning training with [--] longer context and no accuracy loss via our new batching algorithms. Long reasoning chains in RL are costly but now we enable you to train gpt-oss with GRPO & reach 380K context on a 192GB GPU. https://unsloth.ai/docs/new/grpo-long-context https://unsloth.ai/docs/new/grpo-long-context https://unsloth.ai/docs/new/grpo-long-context https://unsloth.ai/docs/new/grpo-long-context"
X Link 2026-01-15T15:47Z 41.6K followers, 71.9K engagements

"@NVIDIAAIDev @huggingface Congrats guys thank you Nvidia team for releasing brilliant open-source models 😍💚"
X Link 2026-02-04T05:44Z 43.3K followers, [----] engagements

"We made a guide on how to do tool calling with local LLMs. Learn how to use open models like Qwen3-Coder-Next and GLM-4.7-Flash for function calling. Has hands-on examples for: story writing Python execution terminal tool calls maths and more. Guide: https://unsloth.ai/docs/basics/tool-calling-guide-for-local-llms https://unsloth.ai/docs/basics/tool-calling-guide-for-local-llms https://unsloth.ai/docs/basics/tool-calling-guide-for-local-llms https://unsloth.ai/docs/basics/tool-calling-guide-for-local-llms"
X Link 2026-02-05T15:57Z 42.2K followers, [---] engagements

"Unsloth is excited to support @HuggingFace Transformers v5 🤗🦥 Get all the latest performance improvements in inference training and more Transformers v5's FINAL stable release is out 🔥 Transformers' biggest release. The big Ws of this release: - Performance especially for MoE (6x-11x speedups) - No more slow/fast tokenizers - way simpler API explicit backends better performance - dynamic weight loading: way https://t.co/PV9lmE3KJx Transformers v5's FINAL stable release is out 🔥 Transformers' biggest release. The big Ws of this release: - Performance especially for MoE (6x-11x speedups) -"
X Link 2026-01-26T23:50Z 43.6K followers, 21.6K engagements

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