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OpenLLM-Ro/RoMistral-7b-Instruct-2024-05-17 - GGUF

This repo contains GGUF format model files for OpenLLM-Ro/RoMistral-7b-Instruct-2024-05-17.

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

Prompt template

<s>{system_prompt} [INST] {prompt} [/INST]

Model file specification

Filename Quant type File Size Description
RoMistral-7b-Instruct-2024-05-17-Q2_K.gguf Q2_K 2.719 GB smallest, significant quality loss - not recommended for most purposes
RoMistral-7b-Instruct-2024-05-17-Q3_K_S.gguf Q3_K_S 3.165 GB very small, high quality loss
RoMistral-7b-Instruct-2024-05-17-Q3_K_M.gguf Q3_K_M 3.519 GB very small, high quality loss
RoMistral-7b-Instruct-2024-05-17-Q3_K_L.gguf Q3_K_L 3.822 GB small, substantial quality loss
RoMistral-7b-Instruct-2024-05-17-Q4_0.gguf Q4_0 4.109 GB legacy; small, very high quality loss - prefer using Q3_K_M
RoMistral-7b-Instruct-2024-05-17-Q4_K_S.gguf Q4_K_S 4.140 GB small, greater quality loss
RoMistral-7b-Instruct-2024-05-17-Q4_K_M.gguf Q4_K_M 4.368 GB medium, balanced quality - recommended
RoMistral-7b-Instruct-2024-05-17-Q5_0.gguf Q5_0 4.998 GB legacy; medium, balanced quality - prefer using Q4_K_M
RoMistral-7b-Instruct-2024-05-17-Q5_K_S.gguf Q5_K_S 4.998 GB large, low quality loss - recommended
RoMistral-7b-Instruct-2024-05-17-Q5_K_M.gguf Q5_K_M 5.131 GB large, very low quality loss - recommended
RoMistral-7b-Instruct-2024-05-17-Q6_K.gguf Q6_K 5.942 GB very large, extremely low quality loss
RoMistral-7b-Instruct-2024-05-17-Q8_0.gguf Q8_0 7.696 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/RoMistral-7b-Instruct-2024-05-17-GGUF --include "RoMistral-7b-Instruct-2024-05-17-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/RoMistral-7b-Instruct-2024-05-17-GGUF --local-dir MY_LOCAL_DIR --local-dir-use-symlinks False --include='*Q4_K*gguf'
Downloads last month
186
GGUF
Model size
7.24B params
Architecture
llama

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Inference API
Unable to determine this model's library. Check the docs .

Model tree for tensorblock/RoMistral-7b-Instruct-2024-05-17-GGUF

Quantized
(3)
this model

Datasets used to train tensorblock/RoMistral-7b-Instruct-2024-05-17-GGUF

Evaluation results