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unsloth_KernelLLM-GGUF
tensorblock/unsloth_KernelLLM-GGUF
unsloth_KernelLLM-GGUF is a machine learning model from tensorblock. Use it for the machine learning task on the model card, and read the license before you ship it in a product. It is set up for transformers. The card lists the license as other.
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Repo size
61.5 GB
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Repo makeup
Click a slice to open those files.
.gguf7.2 GB · 100%
At a glance
- Library
- transformers
- License
- other
- Access
- Public
- Created
- Aug 3, 2025
- Updated
- Jan 27, 2026
- SHA
- 8be1eec5
Base models
Datasets
Model card
From the Hugging Face model README
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<img src="https://i.imgur.com/jC7kdl8.jpeg" alt="TensorBlock" style="width: 100%; min-width: 400px; display: block; margin: auto;">
</div>
unsloth/KernelLLM - GGUF
<div style="text-align: left; margin: 20px 0;"> <a href="https://discord.com/invite/Ej5NmeHFf2" style="display: inline-block; padding: 10px 20px; background-color: #5865F2; color: white; text-decoration: none; border-radius: 5px; font-weight: bold;"> Join our Discord to learn more about what we're building ↗ </a> </div>This repo contains GGUF format model files for unsloth/KernelLLM.
The files were quantized using machines provided by TensorBlock, and they are compatible with llama.cpp as of commit b5753.
Our projects
<table border="1" cellspacing="0" cellpadding="10"> <tr> <th colspan="2" style="font-size: 25px;">Forge</th> </tr> <tr> <th colspan="2"> <img src="https://imgur.com/faI5UKh.jpeg" alt="Forge Project" width="900"/> </th> </tr> <tr> <th colspan="2">An OpenAI-compatible multi-provider routing layer.</th> </tr> <tr> <th colspan="2"> <a href="https://github.com/TensorBlock/forge" target="_blank" style=" display: inline-block; padding: 8px 16px; background-color: #FF7F50; color: white; text-decoration: none; border-radius: 6px; font-weight: bold; font-family: sans-serif; ">🚀 Try it now! 🚀</a> </th> </tr> <tr> <th style="font-size: 25px;">Awesome MCP Servers</th> <th style="font-size: 25px;">TensorBlock Studio</th> </tr> <tr> <th><img src="https://imgur.com/2Xov7B7.jpeg" alt="MCP Servers" width="450"/></th> <th><img src="https://imgur.com/pJcmF5u.jpeg" alt="Studio" width="450"/></th> </tr> <tr> <th>A comprehensive collection of Model Context Protocol (MCP) servers.</th> <th>A lightweight, open, and extensible multi-LLM interaction studio.</th> </tr> <tr> <th> <a href="https://github.com/TensorBlock/awesome-mcp-servers" target="_blank" style=" display: inline-block; padding: 8px 16px; background-color: #FF7F50; color: white; text-decoration: none; border-radius: 6px; font-weight: bold; font-family: sans-serif; ">👀 See what we built 👀</a> </th> <th> <a href="https://github.com/TensorBlock/TensorBlock-Studio" target="_blank" style=" display: inline-block; padding: 8px 16px; background-color: #FF7F50; color: white; text-decoration: none; border-radius: 6px; font-weight: bold; font-family: sans-serif; ">👀 See what we built 👀</a> </th> </tr> </table>Prompt template
<|begin_of_text|><|begin_of_text|><|start_header_id|>system<|end_header_id|>
{system_prompt}<|eot_id|><|start_header_id|>user<|end_header_id|>
{prompt}<|eot_id|><|start_header_id|>assistant<|end_header_id|>
Model file specification
| Filename | Quant type | File Size | Description |
|---|---|---|---|
| KernelLLM-Q2_K.gguf | Q2_K | 3.179 GB | smallest, significant quality loss - not recommended for most purposes |
| KernelLLM-Q3_K_S.gguf | Q3_K_S | 3.665 GB | very small, high quality loss |
| KernelLLM-Q3_K_M.gguf | Q3_K_M | 4.019 GB | very small, high quality loss |
| KernelLLM-Q3_K_L.gguf | Q3_K_L | 4.322 GB | small, substantial quality loss |
| KernelLLM-Q4_0.gguf | Q4_0 | 4.661 GB | legacy; small, very high quality loss - prefer using Q3_K_M |
| KernelLLM-Q4_K_S.gguf | Q4_K_S | 4.693 GB | small, greater quality loss |
| KernelLLM-Q4_K_M.gguf | Q4_K_M | 4.921 GB | medium, balanced quality - recommended |
| KernelLLM-Q5_0.gguf | Q5_0 | 5.599 GB | legacy; medium, balanced quality - prefer using Q4_K_M |
| KernelLLM-Q5_K_S.gguf | Q5_K_S | 5.599 GB | large, low quality loss - recommended |
| KernelLLM-Q5_K_M.gguf | Q5_K_M | 5.733 GB | large, very low quality loss - recommended |
| KernelLLM-Q6_K.gguf | Q6_K | 6.596 GB | very large, extremely low quality loss |
| KernelLLM-Q8_0.gguf | Q8_0 | 8.541 GB | very large, extremely low quality loss - not recommended |
Downloading instruction
Command line
Firstly, install Huggingface Client
pip install -U "huggingface_hub[cli]"
Then, downoad the individual model file the a local directory
huggingface-cli download tensorblock/unsloth_KernelLLM-GGUF --include "KernelLLM-Q2_K.gguf" --local-dir MY_LOCAL_DIR
If you wanna download multiple model files with a pattern (e.g., *Q4_K*gguf), you can try:
huggingface-cli download tensorblock/unsloth_KernelLLM-GGUF --local-dir MY_LOCAL_DIR --local-dir-use-symlinks False --include='*Q4_K*gguf'
- Library
- transformers
- License
- other
- Created
- Aug 3, 2025
- Updated
- Jan 27, 2026