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metadata
library_name: transformers
license: apache-2.0
datasets:
  - DeepMount00/gquad_it
language:
  - it
tags:
  - TensorBlock
  - GGUF
base_model: DeepMount00/Mistral-RAG
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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.

Prompt template

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'