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All quants made using imatrix option with dataset from here Run them directly with llama.cpp, or any other llama.cpp based project Some of these quants (Q3_K_XL, Q4_K_L etc) are the standard quantization method with the embeddings and output weights quantized to Q8_0 instead of what they would normally default to.

Text generation
Input Text→ Output Text
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**Model details:** - Parameter count: 28B (source checkpoint) - Input support: text, image (with mmproj file) - details - Speculative decoding: yes (MTP) - details - imatrix: yes - details **Don't know which to choose?** Grab Q4_K_M (17.44GB) - usually a good mix of size and performance. Download instructions available here The files marked `true` in the Split column above are stored as multiple parts in a folder. To download all the parts to a local folder, run:

Visual QA
Input Text · Image→ Output Text
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**Model details:** - Parameter count: 9B - Input support: text, image (with mmproj file) - details - Speculative decoding: no - imatrix: yes - details **Don't know which to choose?** Grab Q4_K_M (5.84GB) - usually a good mix of size and performance. Download instructions available here These quants run with llama.cpp - installable in one line via llama.app:

Code generationOpen weights
Input Text→ Output Text
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**Model details:** - Parameter count: 35B (source checkpoint) - Input support: text, image (with mmproj file) - details - Speculative decoding: no - imatrix: yes - details **Don't know which to choose?** Grab Q4_K_M (22.32GB) - usually a good mix of size and performance. Download instructions available here The files marked `true` in the Split column above are stored as multiple parts in a folder. To download all the parts to a local folder, run:

Visual QA
Input Text · Image→ Output Text
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**Model details:** - Parameter count: 3B - Input support: text - Speculative decoding: no - imatrix: yes - details **Don't know which to choose?** Grab Q4_K_M (1.62GB) - usually a good mix of size and performance. Download instructions available here These quants were made with llama.cpp release b10883 - if this model's architecture is newly supported, you'll need that release or newer to run them.

Text generation
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miniG-GGUF

bartowski

基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。

Basic profile indexed · More details to follow

Text generation
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If running with CUDA on a model that has `Q4_K` or `Q5_K` for `61.blk.ffn_down_exps` you should compile with the following flag: to avoid `NaN` activations. This is most likely an issue in `llama.cpp` itself and will hopefully be resolved, but in the meantime this fixes the issue. You can check in the info on huggingface which models are affected, but it seems to be `Q5_K_S`, `Q4_K_S`, `Q3_K_XL`, `Q3_K_L`, `Q3_K_M`, `IQ3_M`, `IQ3_XS`, and `IQ3_XXS`

Text generation
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1 M = 1 million tokens · USD
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