Mistral-Nemo-Japanese-Instruct-2408-GGUF

tensorblock/Mistral-Nemo-Japanese-Instruct-2408-GGUF

Mistral-Nemo-Japanese-Instruct-2408-GGUF is a text generation model from tensorblock. Use it when you need the model to write or continue text. The card lists the license as apache-2.0.

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Downloads · 30 days

1% of all-time downloads

All-time downloads

3.6K

Public

Repo size

93.5 GB

Likes

Public

Hugging Face

Repo makeup

Click a slice to open those files.

.gguf4.8 GB · 100%

At a glance

Task: Text Generation
License: apache-2.0
Access: Public
Created: Nov 28, 2024
Updated: Jan 27, 2026
SHA: 29bb7436

Base models

cyberagent/Mistral-Nemo-Japanese-Instruct-2408

Model card

From the Hugging Face model README

cyberagent/Mistral-Nemo-Japanese-Instruct-2408 - GGUF

This repo contains GGUF format model files for cyberagent/Mistral-Nemo-Japanese-Instruct-2408.

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

<s><|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
Mistral-Nemo-Japanese-Instruct-2408-Q2_K.gguf	Q2_K	4.791 GB	smallest, significant quality loss - not recommended for most purposes
Mistral-Nemo-Japanese-Instruct-2408-Q3_K_S.gguf	Q3_K_S	5.534 GB	very small, high quality loss
Mistral-Nemo-Japanese-Instruct-2408-Q3_K_M.gguf	Q3_K_M	6.083 GB	very small, high quality loss
Mistral-Nemo-Japanese-Instruct-2408-Q3_K_L.gguf	Q3_K_L	6.562 GB	small, substantial quality loss
Mistral-Nemo-Japanese-Instruct-2408-Q4_0.gguf	Q4_0	7.072 GB	legacy; small, very high quality loss - prefer using Q3_K_M
Mistral-Nemo-Japanese-Instruct-2408-Q4_K_S.gguf	Q4_K_S	7.120 GB	small, greater quality loss
Mistral-Nemo-Japanese-Instruct-2408-Q4_K_M.gguf	Q4_K_M	7.477 GB	medium, balanced quality - recommended
Mistral-Nemo-Japanese-Instruct-2408-Q5_0.gguf	Q5_0	8.519 GB	legacy; medium, balanced quality - prefer using Q4_K_M
Mistral-Nemo-Japanese-Instruct-2408-Q5_K_S.gguf	Q5_K_S	8.519 GB	large, low quality loss - recommended
Mistral-Nemo-Japanese-Instruct-2408-Q5_K_M.gguf	Q5_K_M	8.728 GB	large, very low quality loss - recommended
Mistral-Nemo-Japanese-Instruct-2408-Q6_K.gguf	Q6_K	10.056 GB	very large, extremely low quality loss
Mistral-Nemo-Japanese-Instruct-2408-Q8_0.gguf	Q8_0	13.022 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/Mistral-Nemo-Japanese-Instruct-2408-GGUF --include "Mistral-Nemo-Japanese-Instruct-2408-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-Nemo-Japanese-Instruct-2408-GGUF --local-dir MY_LOCAL_DIR --local-dir-use-symlinks False --include='*Q4_K*gguf'

Task: Text Generation
License: apache-2.0
Languages: ja, en
Created: Nov 28, 2024
Updated: Jan 27, 2026