Mistral-RAG-GGUF

tensorblock/Mistral-RAG-GGUF

Mistral-RAG-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 apache-2.0.

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Downloads · 30 days

2% of all-time downloads

All-time downloads

2.6K

Public

Repo size

54.6 GB

Likes

Public

Hugging Face

Repo makeup

Click a slice to open those files.

.gguf6.2 GB · 100%

At a glance

Library: transformers
License: apache-2.0
Access: Public
Created: Nov 11, 2024
Updated: Jan 27, 2026
SHA: 80c490a7

Base models

DeepMount00/Mistral-RAG

Datasets

DeepMount00/gquad_it

Model card

From the Hugging Face model README

DeepMount00/Mistral-RAG - GGUF

This repo contains GGUF format model files for DeepMount00/Mistral-RAG.

The files were quantized using machines provided by TensorBlock, and they are compatible with llama.cpp as of commit b4011.

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Model file specification

Filename	Quant type	File Size	Description
Mistral-RAG-Q2_K.gguf	Q2_K	2.532 GB	smallest, significant quality loss - not recommended for most purposes
Mistral-RAG-Q3_K_S.gguf	Q3_K_S	2.947 GB	very small, high quality loss
Mistral-RAG-Q3_K_M.gguf	Q3_K_M	3.277 GB	very small, high quality loss
Mistral-RAG-Q3_K_L.gguf	Q3_K_L	3.560 GB	small, substantial quality loss
Mistral-RAG-Q4_0.gguf	Q4_0	3.827 GB	legacy; small, very high quality loss - prefer using Q3_K_M
Mistral-RAG-Q4_K_S.gguf	Q4_K_S	3.856 GB	small, greater quality loss
Mistral-RAG-Q4_K_M.gguf	Q4_K_M	4.068 GB	medium, balanced quality - recommended
Mistral-RAG-Q5_0.gguf	Q5_0	4.654 GB	legacy; medium, balanced quality - prefer using Q4_K_M
Mistral-RAG-Q5_K_S.gguf	Q5_K_S	4.654 GB	large, low quality loss - recommended
Mistral-RAG-Q5_K_M.gguf	Q5_K_M	4.779 GB	large, very low quality loss - recommended
Mistral-RAG-Q6_K.gguf	Q6_K	5.534 GB	very large, extremely low quality loss
Mistral-RAG-Q8_0.gguf	Q8_0	7.167 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-RAG-GGUF --include "Mistral-RAG-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-RAG-GGUF --local-dir MY_LOCAL_DIR --local-dir-use-symlinks False --include='*Q4_K*gguf'

Library: transformers
License: apache-2.0
Languages: it
Created: Nov 11, 2024
Updated: Jan 27, 2026