unsloth/NVIDIA-Nemotron-3-Super-120B-A12B-GGUF
Text Generation • 121B • Updated • 69.1k • 93
André Leo Bjørningstad
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liked a model 9 days ago
unsloth/NVIDIA-Nemotron-3-Super-120B-A12B-GGUF liked a model 9 days ago
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Tesslate/OmniCoder-9BOrganizations
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LocoreMind/LocoTrainer-4B
Text Generation • 4B • Updated • 2.02k • 55
Tesslate/OmniCoder-9B
Text Generation • Updated • 21.2k • 382
janhq/Jan-code-4b-gguf
Text Generation • 4B • Updated • 22.2k • 55
janhq/Qwen3.5-35B-A3B-GGUF
Image-Text-to-Text • 35B • Updated • 34.2k • 5
janhq/Jan-v2-VL-max-gguf
Image-Text-to-Text • 31B • Updated • 4.21k • 4
liked 4
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upvoted an article 29 days ago
Article
GGML and llama.cpp join HF to ensure the long-term progress of Local AI
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model about 1 month ago
reactedto danielhanchen's post with 🔥 about 1 month ago
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We collaborated with Hugging Face to enable you to train MoE models 12× faster with 35% less VRAM via our new Triton kernels (no accuracy loss). 🤗
Train gpt-oss locally on 12.8GB VRAM with our free notebooks: https://unsloth.ai/docs/new/faster-moe
Train gpt-oss locally on 12.8GB VRAM with our free notebooks: https://unsloth.ai/docs/new/faster-moe
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liked 2
models about 2 months ago
repliedto danielhanchen's post about 2 months ago
I got qwen3-coder-next up 24tps when i removed -nkvo and kept -kvu. Think i could increase it further if i took the time to compile llama.cpp and not use docker image.
upvoted a collection about 2 months ago
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repliedto danielhanchen's post about 2 months ago
I run it on threadripper 3970x with 256gb system ram and offloading computation layers to a gtx 1660 6gb vram. Using llama.cpp with -nkvo -kvu and all MoE on CPU. With an amazing speed on 14/TpS generation speed using q8_0. I’m amazed
reactedto danielhanchen's post with 🤯 about 2 months ago
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Qwen releases Qwen3-Coder-Next! 💜 Run the locally on 46GB RAM or less.
Thhe model excels at agentic coding & local use. With 256K context, it delivers similar performance to models with 10-20× more active parameters.
GGUF: unsloth/Qwen3-Coder-Next-GGUF
Guide: https://unsloth.ai/docs/models/qwen3-coder-next
Thhe model excels at agentic coding & local use. With 256K context, it delivers similar performance to models with 10-20× more active parameters.
GGUF: unsloth/Qwen3-Coder-Next-GGUF
Guide: https://unsloth.ai/docs/models/qwen3-coder-next
- 10 replies