metadata
pipeline_tag: text-generation
base_model: bigcode/starcoder2-15b-instruct-v0.1
datasets:
- bigcode/self-oss-instruct-sc2-exec-filter-50k
license: bigcode-openrail-m
library_name: transformers
tags:
- code
- TensorBlock
- GGUF
model-index:
- name: starcoder2-15b-instruct-v0.1
results:
- task:
type: text-generation
dataset:
name: LiveCodeBench (code generation)
type: livecodebench-codegeneration
metrics:
- type: pass@1
value: 20.4
- task:
type: text-generation
dataset:
name: LiveCodeBench (self repair)
type: livecodebench-selfrepair
metrics:
- type: pass@1
value: 20.9
- task:
type: text-generation
dataset:
name: LiveCodeBench (test output prediction)
type: livecodebench-testoutputprediction
metrics:
- type: pass@1
value: 29.8
- task:
type: text-generation
dataset:
name: LiveCodeBench (code execution)
type: livecodebench-codeexecution
metrics:
- type: pass@1
value: 28.1
- task:
type: text-generation
dataset:
name: HumanEval
type: humaneval
metrics:
- type: pass@1
value: 72.6
- task:
type: text-generation
dataset:
name: HumanEval+
type: humanevalplus
metrics:
- type: pass@1
value: 63.4
- task:
type: text-generation
dataset:
name: MBPP
type: mbpp
metrics:
- type: pass@1
value: 75.2
- task:
type: text-generation
dataset:
name: MBPP+
type: mbppplus
metrics:
- type: pass@1
value: 61.2
- task:
type: text-generation
dataset:
name: DS-1000
type: ds-1000
metrics:
- type: pass@1
value: 40.6

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bigcode/starcoder2-15b-instruct-v0.1 - GGUF
This repo contains GGUF format model files for bigcode/starcoder2-15b-instruct-v0.1.
The files were quantized using machines provided by TensorBlock, and they are compatible with llama.cpp as of commit b4011.
Prompt template
<|endoftext|>You are an exceptionally intelligent coding assistant that consistently delivers accurate and reliable responses to user instructions.
### Instruction
{prompt}
### Response
Model file specification
Filename | Quant type | File Size | Description |
---|---|---|---|
starcoder2-15b-instruct-v0.1-Q2_K.gguf | Q2_K | 5.768 GB | smallest, significant quality loss - not recommended for most purposes |
starcoder2-15b-instruct-v0.1-Q3_K_S.gguf | Q3_K_S | 6.507 GB | very small, high quality loss |
starcoder2-15b-instruct-v0.1-Q3_K_M.gguf | Q3_K_M | 7.492 GB | very small, high quality loss |
starcoder2-15b-instruct-v0.1-Q3_K_L.gguf | Q3_K_L | 8.350 GB | small, substantial quality loss |
starcoder2-15b-instruct-v0.1-Q4_0.gguf | Q4_0 | 8.443 GB | legacy; small, very high quality loss - prefer using Q3_K_M |
starcoder2-15b-instruct-v0.1-Q4_K_S.gguf | Q4_K_S | 8.532 GB | small, greater quality loss |
starcoder2-15b-instruct-v0.1-Q4_K_M.gguf | Q4_K_M | 9.183 GB | medium, balanced quality - recommended |
starcoder2-15b-instruct-v0.1-Q5_0.gguf | Q5_0 | 10.265 GB | legacy; medium, balanced quality - prefer using Q4_K_M |
starcoder2-15b-instruct-v0.1-Q5_K_S.gguf | Q5_K_S | 10.265 GB | large, low quality loss - recommended |
starcoder2-15b-instruct-v0.1-Q5_K_M.gguf | Q5_K_M | 10.646 GB | large, very low quality loss - recommended |
starcoder2-15b-instruct-v0.1-Q6_K.gguf | Q6_K | 12.201 GB | very large, extremely low quality loss |
starcoder2-15b-instruct-v0.1-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-instruct-v0.1-GGUF --include "starcoder2-15b-instruct-v0.1-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-instruct-v0.1-GGUF --local-dir MY_LOCAL_DIR --local-dir-use-symlinks False --include='*Q4_K*gguf'