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starcoder2-3b-GGUF

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metadata
pipeline_tag: text-generation
inference: true
widget:
  - text: 'def print_hello_world():'
    example_title: Hello world
    group: Python
datasets:
  - bigcode/the-stack-v2-train
license: bigcode-openrail-m
library_name: transformers
tags:
  - code
  - TensorBlock
  - GGUF
base_model: bigcode/starcoder2-3b
model-index:
  - name: starcoder2-3b
    results:
      - task:
          type: text-generation
        dataset:
          name: CruxEval-I
          type: cruxeval-i
        metrics:
          - type: pass@1
            value: 32.7
      - task:
          type: text-generation
        dataset:
          name: DS-1000
          type: ds-1000
        metrics:
          - type: pass@1
            value: 25
      - task:
          type: text-generation
        dataset:
          name: GSM8K (PAL)
          type: gsm8k-pal
        metrics:
          - type: accuracy
            value: 27.7
      - task:
          type: text-generation
        dataset:
          name: HumanEval+
          type: humanevalplus
        metrics:
          - type: pass@1
            value: 27.4
      - task:
          type: text-generation
        dataset:
          name: HumanEval
          type: humaneval
        metrics:
          - type: pass@1
            value: 31.7
      - task:
          type: text-generation
        dataset:
          name: RepoBench-v1.1
          type: repobench-v1.1
        metrics:
          - type: edit-smiliarity
            value: 71.19
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bigcode/starcoder2-3b - GGUF

This repo contains GGUF format model files for bigcode/starcoder2-3b.

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
starcoder2-3b-Q2_K.gguf Q2_K 1.139 GB smallest, significant quality loss - not recommended for most purposes
starcoder2-3b-Q3_K_S.gguf Q3_K_S 1.273 GB very small, high quality loss
starcoder2-3b-Q3_K_M.gguf Q3_K_M 1.455 GB very small, high quality loss
starcoder2-3b-Q3_K_L.gguf Q3_K_L 1.618 GB small, substantial quality loss
starcoder2-3b-Q4_0.gguf Q4_0 1.629 GB legacy; small, very high quality loss - prefer using Q3_K_M
starcoder2-3b-Q4_K_S.gguf Q4_K_S 1.642 GB small, greater quality loss
starcoder2-3b-Q4_K_M.gguf Q4_K_M 1.758 GB medium, balanced quality - recommended
starcoder2-3b-Q5_0.gguf Q5_0 1.964 GB legacy; medium, balanced quality - prefer using Q4_K_M
starcoder2-3b-Q5_K_S.gguf Q5_K_S 1.964 GB large, low quality loss - recommended
starcoder2-3b-Q5_K_M.gguf Q5_K_M 2.031 GB large, very low quality loss - recommended
starcoder2-3b-Q6_K.gguf Q6_K 2.320 GB very large, extremely low quality loss
starcoder2-3b-Q8_0.gguf Q8_0 3.003 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/starcoder2-3b-GGUF --include "starcoder2-3b-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/starcoder2-3b-GGUF --local-dir MY_LOCAL_DIR --local-dir-use-symlinks False --include='*Q4_K*gguf'