Downloads · 30 days
77
8% of all-time downloads
coldint-sn29-GGUF
tensorblock/coldint-sn29-GGUF
coldint-sn29-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.
<div style="width: auto; margin-left: auto; margin-right: auto" <img src="https://i.imgur.com/jC7kdl8.jpeg" alt="TensorBlock" style="width: 100%; min-width: 400px; display: block; margin: auto;" </div
All-time downloads
1K
Public
Repo size
54.6 GB
Likes
0
Public
Repo makeup
Click a slice to open those files.
.gguf2.9 GB · 100%
At a glance
- Library
- transformers
- Access
- Public
- Created
- Nov 21, 2024
- Updated
- Jan 27, 2026
- SHA
- db12a087
Base models
Model card
From the Hugging Face model README
<div style="width: auto; margin-left: auto; margin-right: auto">
<img src="https://i.imgur.com/jC7kdl8.jpeg" alt="TensorBlock" style="width: 100%; min-width: 400px; display: block; margin: auto;">
</div>
rak-r05/coldint-sn29 - GGUF
This repo contains GGUF format model files for rak-r05/coldint-sn29.
The files were quantized using machines provided by TensorBlock, and they are compatible with llama.cpp as of commit b4011.
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<|im_start|>system
{system_prompt}<|im_end|>
<|im_start|>user
{prompt}<|im_end|>
<|im_start|>assistant
Model file specification
| Filename | Quant type | File Size | Description |
|---|---|---|---|
| coldint-sn29-Q2_K.gguf | Q2_K | 2.923 GB | smallest, significant quality loss - not recommended for most purposes |
| coldint-sn29-Q3_K_S.gguf | Q3_K_S | 3.340 GB | very small, high quality loss |
| coldint-sn29-Q3_K_M.gguf | Q3_K_M | 3.626 GB | very small, high quality loss |
| coldint-sn29-Q3_K_L.gguf | Q3_K_L | 3.796 GB | small, substantial quality loss |
| coldint-sn29-Q4_0.gguf | Q4_0 | 3.983 GB | legacy; small, very high quality loss - prefer using Q3_K_M |
| coldint-sn29-Q4_K_S.gguf | Q4_K_S | 4.200 GB | small, greater quality loss |
| coldint-sn29-Q4_K_M.gguf | Q4_K_M | 4.507 GB | medium, balanced quality - recommended |
| coldint-sn29-Q5_0.gguf | Q5_0 | 4.792 GB | legacy; medium, balanced quality - prefer using Q4_K_M |
| coldint-sn29-Q5_K_S.gguf | Q5_K_S | 4.894 GB | large, low quality loss - recommended |
| coldint-sn29-Q5_K_M.gguf | Q5_K_M | 5.156 GB | large, very low quality loss - recommended |
| coldint-sn29-Q6_K.gguf | Q6_K | 6.047 GB | very large, extremely low quality loss |
| coldint-sn29-Q8_0.gguf | Q8_0 | 7.319 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/coldint-sn29-GGUF --include "coldint-sn29-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/coldint-sn29-GGUF --local-dir MY_LOCAL_DIR --local-dir-use-symlinks False --include='*Q4_K*gguf'
- Library
- transformers
- Created
- Nov 21, 2024
- Updated
- Jan 27, 2026