You can deploy this model and start earning money today!

tensorblock/
mistral-orpo-capybara-7k-GGUF

Name: mistral-orpo-capybara-7k-GGUF
Availability: InStock

Views

No views yet

kaist-ai/mistral-orpo-capybara-7k - GGUF

This repo contains GGUF format model files for kaist-ai/mistral-orpo-capybara-7k.

The files were quantized using machines provided by TensorBlock, and they are compatible with llama.cpp as of commit b4242.

Forge
Forge Project
An OpenAI-compatible multi-provider routing layer.
🚀 Try it now! 🚀
Awesome MCP Servers	TensorBlock Studio
MCP Servers	Studio
A comprehensive collection of Model Context Protocol (MCP) servers.	A lightweight, open, and extensible multi-LLM interaction studio.
👀 See what we built 👀	👀 See what we built 👀

## Prompt template

<|system|>
{system_prompt}</s>
<|user|>
{prompt}</s>
<|assistant|>

Filename	Quant type	File Size	Description
mistral-orpo-capybara-7k-Q2_K.gguf	Q2_K	2.719 GB	smallest, significant quality loss - not recommended for most purposes
mistral-orpo-capybara-7k-Q3_K_S.gguf	Q3_K_S	3.165 GB	very small, high quality loss
mistral-orpo-capybara-7k-Q3_K_M.gguf	Q3_K_M	3.519 GB	very small, high quality loss
mistral-orpo-capybara-7k-Q3_K_L.gguf	Q3_K_L	3.822 GB	small, substantial quality loss
mistral-orpo-capybara-7k-Q4_0.gguf	Q4_0	4.109 GB	legacy; small, very high quality loss - prefer using Q3_K_M
mistral-orpo-capybara-7k-Q4_K_S.gguf	Q4_K_S	4.140 GB	small, greater quality loss
mistral-orpo-capybara-7k-Q4_K_M.gguf	Q4_K_M	4.368 GB	medium, balanced quality - recommended
mistral-orpo-capybara-7k-Q5_0.gguf	Q5_0	4.998 GB	legacy; medium, balanced quality - prefer using Q4_K_M
mistral-orpo-capybara-7k-Q5_K_S.gguf	Q5_K_S	4.998 GB	large, low quality loss - recommended
mistral-orpo-capybara-7k-Q5_K_M.gguf	Q5_K_M	5.131 GB	large, very low quality loss - recommended
mistral-orpo-capybara-7k-Q6_K.gguf	Q6_K	5.942 GB	very large, extremely low quality loss
mistral-orpo-capybara-7k-Q8_0.gguf	Q8_0	7.696 GB	very large, extremely low quality loss - not recommended

Firstly, install Huggingface Client

pip install -U "huggingface_hub[cli]"

Then, downoad the individual model file the a local directory

huggingface-cli download tensorblock/mistral-orpo-capybara-7k-GGUF --include "mistral-orpo-capybara-7k-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/mistral-orpo-capybara-7k-GGUF --local-dir MY_LOCAL_DIR --local-dir-use-symlinks False --include='*Q4_K*gguf'