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TText generation▧Image generation▷Video generation⌘Code generation◖Speech synthesis♫Music generation⬡3D modeling▤Document analysis
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**Model details:** - Parameter count: 28B - 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: 5B (source checkpoint) - Input support: text - Speculative decoding: no - imatrix: yes - details **Don't know which to choose?** Grab Q4_K_M (3.25GB) - usually a good mix of size and performance. Download instructions available here These quants were made with llama.cpp release b11462 - if this model's architecture is newly supported, you'll need that release or newer to run them.

Text generation
Input Text→ Output Text
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**Model details:** - Parameter count: 35B (source checkpoint) - Input support: text - Speculative decoding: no - imatrix: yes - details **Don't know which to choose?** Grab Q4_K_M (22.04GB) - 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:

Text generation
Input Text→ Output Text
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**Model details:** - Parameter count: 9B (source checkpoint) - Input support: text - Speculative decoding: no - imatrix: yes - details **Don't know which to choose?** Grab Q4_K_M (5.78GB) - usually a good mix of size and performance. Download instructions available here These quants were made with llama.cpp release b11462 - if this model's architecture is newly supported, you'll need that release or newer to run them.

Text generation
Input Text→ Output Text
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**Model details:** - Parameter count: 37B (source checkpoint) - Input support: text - Speculative decoding: no - imatrix: yes - details **Don't know which to choose?** Grab Q4_K_M (22.70GB) - 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:

Text generation
Input Text→ Output Text
Price not verifiedCheck multimodal and channel pricing separately
View details ↗
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**Model details:** - Parameter count: 28B - 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 (17.77GB) - 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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All quants made using imatrix option with dataset from here - llama.cpp - ramalama - LM Studio - koboldcpp - Jan AI - Text Generation Web UI - LoLLMs - Atomic Chat Note: if it's a newly supported model, you may need to wait for an update from the developers.

Category pending
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**Model details:** - Parameter count: 78B (source checkpoint) - Input support: text - Speculative decoding: no - imatrix: yes - details **Don't know which to choose?** Grab Q4_K_M (49.76GB) - 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:

Text generation
Input Text→ Output Text
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Kudzu-8B-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
Input Text→ Output Text
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1 M = 1 million tokens · USD
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Separate input, output and cache rates, then estimate the same workload. Image, audio and video rates may use items, duration or resolution rather than tokens.

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Rankings depend on tasks, versions and methods. Compare accuracy, latency, reliability and costs on your own workload.

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