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metadata
pipeline_tag: text-generation
inference: true
widget:
  - text: 'def print_hello_world():'
    example_title: Hello world
    group: Python
license: bigcode-openrail-m
datasets:
  - bigcode/the-stack-dedup
metrics:
  - code_eval
library_name: transformers
tags:
  - code
  - TensorBlock
  - GGUF
base_model: bigcode/tiny_starcoder_py
model-index:
  - name: Tiny-StarCoder-Py
    results:
      - task:
          type: text-generation
        dataset:
          name: HumanEval
          type: openai_humaneval
        metrics:
          - type: pass@1
            value: 7.84%
            name: pass@1
            verified: false
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bigcode/tiny_starcoder_py - GGUF

This repo contains GGUF format model files for bigcode/tiny_starcoder_py.

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

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## Prompt template

Model file specification

Filename Quant type File Size Description
tiny_starcoder_py-Q2_K.gguf Q2_K 0.097 GB smallest, significant quality loss - not recommended for most purposes
tiny_starcoder_py-Q3_K_S.gguf Q3_K_S 0.103 GB very small, high quality loss
tiny_starcoder_py-Q3_K_M.gguf Q3_K_M 0.112 GB very small, high quality loss
tiny_starcoder_py-Q3_K_L.gguf Q3_K_L 0.118 GB small, substantial quality loss
tiny_starcoder_py-Q4_0.gguf Q4_0 0.117 GB legacy; small, very high quality loss - prefer using Q3_K_M
tiny_starcoder_py-Q4_K_S.gguf Q4_K_S 0.118 GB small, greater quality loss
tiny_starcoder_py-Q4_K_M.gguf Q4_K_M 0.125 GB medium, balanced quality - recommended
tiny_starcoder_py-Q5_0.gguf Q5_0 0.131 GB legacy; medium, balanced quality - prefer using Q4_K_M
tiny_starcoder_py-Q5_K_S.gguf Q5_K_S 0.131 GB large, low quality loss - recommended
tiny_starcoder_py-Q5_K_M.gguf Q5_K_M 0.136 GB large, very low quality loss - recommended
tiny_starcoder_py-Q6_K.gguf Q6_K 0.146 GB very large, extremely low quality loss
tiny_starcoder_py-Q8_0.gguf Q8_0 0.182 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/tiny_starcoder_py-GGUF --include "tiny_starcoder_py-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/tiny_starcoder_py-GGUF --local-dir MY_LOCAL_DIR --local-dir-use-symlinks False --include='*Q4_K*gguf'