metadata
language:
- en
- fr
- de
- es
- it
- pt
- zh
- ja
- ru
- ko
license: apache-2.0
library_name: vllm
inference: false
base_model: mistralai/Mistral-Small-24B-Instruct-2501
extra_gated_description: >-
If you want to learn more about how we process your personal data, please read
our <a href="https://mistral.ai/terms/">Privacy Policy</a>.
tags:
- TensorBlock
- GGUF
Feedback and support: TensorBlock's Twitter/X, Telegram Group and Discord server
mistralai/Mistral-Small-24B-Instruct-2501 - GGUF
This repo contains GGUF format model files for mistralai/Mistral-Small-24B-Instruct-2501.
The files were quantized using machines provided by TensorBlock, and they are compatible with llama.cpp as of commit ec7f3ac.
Prompt template
<s>[SYSTEM_PROMPT]{system_prompt}[/SYSTEM_PROMPT][INST]{prompt}[/INST]
Model file specification
| Filename | Quant type | File Size | Description |
|---|---|---|---|
| Mistral-Small-24B-Instruct-2501-Q2_K.gguf | Q2_K | 8.890 GB | smallest, significant quality loss - not recommended for most purposes |
| Mistral-Small-24B-Instruct-2501-Q3_K_S.gguf | Q3_K_S | 10.400 GB | very small, high quality loss |
| Mistral-Small-24B-Instruct-2501-Q3_K_M.gguf | Q3_K_M | 11.474 GB | very small, high quality loss |
| Mistral-Small-24B-Instruct-2501-Q3_K_L.gguf | Q3_K_L | 12.401 GB | small, substantial quality loss |
| Mistral-Small-24B-Instruct-2501-Q4_0.gguf | Q4_0 | 13.442 GB | legacy; small, very high quality loss - prefer using Q3_K_M |
| Mistral-Small-24B-Instruct-2501-Q4_K_S.gguf | Q4_K_S | 13.549 GB | small, greater quality loss |
| Mistral-Small-24B-Instruct-2501-Q4_K_M.gguf | Q4_K_M | 14.334 GB | medium, balanced quality - recommended |
| Mistral-Small-24B-Instruct-2501-Q5_0.gguf | Q5_0 | 16.304 GB | legacy; medium, balanced quality - prefer using Q4_K_M |
| Mistral-Small-24B-Instruct-2501-Q5_K_S.gguf | Q5_K_S | 16.304 GB | large, low quality loss - recommended |
| Mistral-Small-24B-Instruct-2501-Q5_K_M.gguf | Q5_K_M | 16.764 GB | large, very low quality loss - recommended |
| Mistral-Small-24B-Instruct-2501-Q6_K.gguf | Q6_K | 19.346 GB | very large, extremely low quality loss |
| Mistral-Small-24B-Instruct-2501-Q8_0.gguf | Q8_0 | 25.055 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-Small-24B-Instruct-2501-GGUF --include "Mistral-Small-24B-Instruct-2501-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-Small-24B-Instruct-2501-GGUF --local-dir MY_LOCAL_DIR --local-dir-use-symlinks False --include='*Q4_K*gguf'