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mistral-1L-mini-GGUF

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metadata
tags:
  - generated_from_trainer
  - TensorBlock
  - GGUF
datasets:
  - roneneldan/TinyStories
metrics:
  - accuracy
base_model: nilq/mistral-1L-mini
model-index:
  - name: mistral-1L-mini
    results:
      - task:
          type: text-generation
          name: Causal Language Modeling
        dataset:
          name: roneneldan/TinyStories
          type: roneneldan/TinyStories
        metrics:
          - type: accuracy
            value: 0.4719044781443692
            name: Accuracy
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nilq/mistral-1L-mini - GGUF

This repo contains GGUF format model files for nilq/mistral-1L-mini.

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

Prompt template

Model file specification

Filename Quant type File Size Description
mistral-1L-mini-Q2_K.gguf Q2_K 0.004 GB smallest, significant quality loss - not recommended for most purposes
mistral-1L-mini-Q3_K_S.gguf Q3_K_S 0.004 GB very small, high quality loss
mistral-1L-mini-Q3_K_M.gguf Q3_K_M 0.004 GB very small, high quality loss
mistral-1L-mini-Q3_K_L.gguf Q3_K_L 0.004 GB small, substantial quality loss
mistral-1L-mini-Q4_0.gguf Q4_0 0.004 GB legacy; small, very high quality loss - prefer using Q3_K_M
mistral-1L-mini-Q4_K_S.gguf Q4_K_S 0.004 GB small, greater quality loss
mistral-1L-mini-Q4_K_M.gguf Q4_K_M 0.004 GB medium, balanced quality - recommended
mistral-1L-mini-Q5_0.gguf Q5_0 0.004 GB legacy; medium, balanced quality - prefer using Q4_K_M
mistral-1L-mini-Q5_K_S.gguf Q5_K_S 0.004 GB large, low quality loss - recommended
mistral-1L-mini-Q5_K_M.gguf Q5_K_M 0.004 GB large, very low quality loss - recommended
mistral-1L-mini-Q6_K.gguf Q6_K 0.005 GB very large, extremely low quality loss
mistral-1L-mini-Q8_0.gguf Q8_0 0.005 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-1L-mini-GGUF --include "mistral-1L-mini-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-1L-mini-GGUF --local-dir MY_LOCAL_DIR --local-dir-use-symlinks False --include='*Q4_K*gguf'