Qwen/Qwen3.8-27B
Image-Text-to-Text • 28B • Updated • 2.95M • • 12.7k
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HauhauCS/Gemma4-12B-QAT-Uncensored-HauhauCS-Balanced new activity 2 months ago
HauhauCS/Gemma4-12B-QAT-Uncensored-HauhauCS-Balanced:MTP in LM Studio?Organizations
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MTP in LM Studio?
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#6 opened 2 months ago
by AlexPoto
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Gemma 4 12B can now run locally on just 8GB RAM via Dynamic GGUFs.
Google's new model, Gemma 4 12B Unified supports image, audio and 256K context.
You can run and train the model via Unsloth Studio.
GGUF: unsloth/gemma-4-12b-it-GGUF
Guide: https://unsloth.ai/docs/models/gemma-4
Google's new model, Gemma 4 12B Unified supports image, audio and 256K context.
You can run and train the model via Unsloth Studio.
GGUF: unsloth/gemma-4-12b-it-GGUF
Guide: https://unsloth.ai/docs/models/gemma-4
- 5 replies
upvoted a collection 3 months ago
problem with tokenizer?
1
#18 opened 5 months ago
by AlexPoto
liked 5
models 6 months ago
unsloth/LTX-2.3-GGUF
Image-to-Video • 21B • Updated • 206k • 554
Qwen/Qwen3.5-35B-A3B
Image-Text-to-Text • 36B • Updated • 2.49M • • 1.49k
AesSedai/Qwen3.5-35B-A3B-GGUF
36B • Updated • 477 • 84
unsloth/Qwen3.5-9B-GGUF
Image-Text-to-Text • 9B • Updated • 1.39M • 858
unsloth/GLM-4.7-Flash-REAP-23B-A3B-GGUF
Text Generation • 23B • Updated • 29.3k • 269
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reacted to danielhanchen's post with 🔥 8 months ago
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You can now run GLM-4.7, the new 355B parameter SOTA model on your local device (128GB RAM).✨
The model achieves SOTA performance on coding, agentic and chat benchmarks.
GGUF: unsloth/GLM-4.7-GGUF
Guide: https://docs.unsloth.ai/models/glm-4.7
The model achieves SOTA performance on coding, agentic and chat benchmarks.
GGUF: unsloth/GLM-4.7-GGUF
Guide: https://docs.unsloth.ai/models/glm-4.7
- 3 replies
Prompt Engineering and Context Engineering are complementary sides of the same LLM interaction process: the former crafts efficient input phrasing (like Zero-shot or Chain-of-Thought) that's cheaper and sufficient for most tasks due to minimal token usage, while the latter builds richer data environments for complex scenarios. Start with cost-effective Prompt Engineering for 80% of needs, escalating to Context Engineering only when extended knowledge or tools are required, as in your LM-Studio caching optimizations.
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