GLM-5.3-Flash, also known as ox-alpha, is out now! Zai’s GLM-5.3-Flash is a new 320B parameter multimodal open model with 18B active parameters. GLM-5.3-Flash outperforms GLM-5.2 across benchmarks and real-world workloads at one-tenth the price, while approaching Claude 4.8 Opus on coding and agentic benchmarks. Docs we're working on: https://lnkd.in/e96jZkMH Model: https://lnkd.in/e3qT4xt5 GGUF coming soon!
Unsloth AI
Technology, Information and Internet
San Francisco, California 47,195 followers
Making AI accessible for everyone! 🦥
About us
Making open-source AI more accessible.
- Website
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https://unsloth.ai
External link for Unsloth AI
- Industry
- Technology, Information and Internet
- Company size
- 11-50 employees
- Headquarters
- San Francisco, California
- Type
- Privately Held
- Founded
- 2023
- Specialties
- artificial intelligence, ai, llms, language models, finetuning, and open-source
Locations
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Primary
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San Francisco, California 94114, US
Employees at Unsloth AI
Updates
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Unsloth AI reposted this
You can now fine-tune Qwen3.8-27B for free with our notebook! 🔥 Local training works on 24GB VRAM. Unsloth AI trains Qwen3.8 1.5x faster with 50% less VRAM than other setups with FA2. We utilize many kernels including our own and others like Flash Linear Attention kernels for maximum performance. GitHub: https://lnkd.in/gyaDBTxK Qwen3.8-27B Notebooks + Guide: https://lnkd.in/ggPFQgtr
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Qwen announces Qwen3.8-Flash-Next, a new open-weight multimodal MoE model. 💜 The model will be released tomorrow and we are working on hopefully Unsloth day zero support. Qwen3.8-Flash-Next is built on the next-generation of Qwen4 architecture. Thank you Qwen for another amazing open model.
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Qwen3.8-27B Unsloth GGUF is now the #2 trending model on Hugging Face with 2.7M downloads! 💗 Unsloth also reached #3 trending on GitHub! Thanks so much for the love! Model: https://lnkd.in/dWfJJf_Z GitHub: https://lnkd.in/dcqhW9Vv
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Qwen3.8-27B GGUF has reached 1,000 likes in less than 24 hours! ❤️ It's now the #3 trending model on Hugging Face with 1M overall downloads. Run on 17GB RAM/VRAM setups via Unsloth! Qwen Model: https://lnkd.in/dWfJJf_Z GitHub: https://lnkd.in/dcqhW9Vv
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Qwen3.8-27B 4-bit GGUF generates an interactive volcano simulation with geology, thermodynamics, fluid flow, buoyancy/drag, projectiles, cooling, groundwater interaction, and environmental effects in Unsloth Desktop. Share your results from Qwen3.8-27B! 👇 What’s your favorite prompt to test a new model with? Unsloth Desktop GitHub: https://lnkd.in/dcqhW9Vv Qwen3.8-27B quants: https://lnkd.in/ge-ZYqx3
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2-bit NVIDIA Nemotron 3.5 Lightning GGUF ran tool calls nonstop for 10 minutes on just 22GB of VRAM. 🤯 It cited 80+ websites, executed code & searched for 10 real-world locations. Run and train via Unsloth Desktop. GGUF: https://lnkd.in/gYfxQhsz Guide: https://lnkd.in/ge5QzjgH
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Unsloth AI reposted this
Introducing Unsloth Desktop 🦥 The first desktop app to run and train models locally. • Open-source. Runs on Mac, Windows and Linux • Supports MLX, diffusion image/video, audio, GGUF • Connect Claude Code and Codex to local LLMs • 50% more accurate, self-healing tool calls + sandboxed code exec • Works for CPU + multiGPU setups - NVIDIA, AMD, Intel, Mac • Train models 2× faster with 70% less VRAM • Private web search, deep research, RAG, MCP and exports (NVFP4, GGUF) • Use Unsloth’s OpenAI-compatible API and cloud models • Securely deploy LLMs remotely and access them anywhere Unsloth AI Desktop is now available on unsloth.ai and GitHub. GitHub: https://lnkd.in/gyaDBTxK Blog and Guide: https://lnkd.in/gFbK-2pe
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2-bit Muse Glimmer GGUF made 100+ toolcalls on just 14GB RAM. 🔥 Meta Muse Glimmer did a complete repo bug hunt for 5 mins nonstop with: evidence, repro, fix, tests and a PR writeup. Run and train it in Unsloth. GitHub repo: https://lnkd.in/dcqhW9Vv
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We compared 1-bit Kimi K3 to Claude Opus 5 and GPT 5.6. 🔥 We gave 4 models the same prompt: Create a glass aquarium whose side panel develops a visible crack and then bursts. 1-bit Kimi K3 GGUF ran locally on 4x B200s at 36 tok/s. GitHub repo: https://lnkd.in/dcqhW9Vv