README.md

metadata

pipeline_tag: text-generation
inference:
  parameters:
    temperature: 0.2
    top_p: 0.95
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/starcoder2-15b
model-index:
  - name: starcoder2-15b
    results:
      - task:
          type: text-generation
        dataset:
          name: CruxEval-I
          type: cruxeval-i
        metrics:
          - type: pass@1
            value: 48.1
      - task:
          type: text-generation
        dataset:
          name: DS-1000
          type: ds-1000
        metrics:
          - type: pass@1
            value: 33.8
      - task:
          type: text-generation
        dataset:
          name: GSM8K (PAL)
          type: gsm8k-pal
        metrics:
          - type: accuracy
            value: 65.1
      - task:
          type: text-generation
        dataset:
          name: HumanEval+
          type: humanevalplus
        metrics:
          - type: pass@1
            value: 37.8
      - task:
          type: text-generation
        dataset:
          name: HumanEval
          type: humaneval
        metrics:
          - type: pass@1
            value: 46.3
      - task:
          type: text-generation
        dataset:
          name: RepoBench-v1.1
          type: repobench-v1.1
        metrics:
          - type: edit-smiliarity
            value: 74.08

bigcode/starcoder2-15b - GGUF

This repo contains GGUF format model files for bigcode/starcoder2-15b.

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
starcoder2-15b-Q2_K.gguf	Q2_K	5.768 GB	smallest, significant quality loss - not recommended for most purposes
starcoder2-15b-Q3_K_S.gguf	Q3_K_S	6.507 GB	very small, high quality loss
starcoder2-15b-Q3_K_M.gguf	Q3_K_M	7.492 GB	very small, high quality loss
starcoder2-15b-Q3_K_L.gguf	Q3_K_L	8.350 GB	small, substantial quality loss
starcoder2-15b-Q4_0.gguf	Q4_0	8.443 GB	legacy; small, very high quality loss - prefer using Q3_K_M
starcoder2-15b-Q4_K_S.gguf	Q4_K_S	8.532 GB	small, greater quality loss
starcoder2-15b-Q4_K_M.gguf	Q4_K_M	9.183 GB	medium, balanced quality - recommended
starcoder2-15b-Q5_0.gguf	Q5_0	10.265 GB	legacy; medium, balanced quality - prefer using Q4_K_M
starcoder2-15b-Q5_K_S.gguf	Q5_K_S	10.265 GB	large, low quality loss - recommended
starcoder2-15b-Q5_K_M.gguf	Q5_K_M	10.646 GB	large, very low quality loss - recommended
starcoder2-15b-Q6_K.gguf	Q6_K	12.201 GB	very large, extremely low quality loss
starcoder2-15b-Q8_0.gguf	Q8_0	15.800 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/starcoder2-15b-GGUF --include "starcoder2-15b-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/starcoder2-15b-GGUF --local-dir MY_LOCAL_DIR --local-dir-use-symlinks False --include='*Q4_K*gguf'