metadata
pipeline_tag: text-generation
inference: false
license: apache-2.0
datasets:
- codeparrot/github-code-clean
- bigcode/starcoderdata
- open-web-math/open-web-math
- math-ai/StackMathQA
metrics:
- code_eval
library_name: transformers
tags:
- code
- granite
- TensorBlock
- GGUF
base_model: ibm-granite/granite-3b-code-base-2k
model-index:
- name: granite-3b-code-base-2k
results:
- task:
type: text-generation
dataset:
name: MBPP
type: mbpp
metrics:
- type: pass@1
value: 36
name: pass@1
- task:
type: text-generation
dataset:
name: MBPP+
type: evalplus/mbppplus
metrics:
- type: pass@1
value: 45.1
name: pass@1
- task:
type: text-generation
dataset:
name: HumanEvalSynthesis(Python)
type: bigcode/humanevalpack
metrics:
- type: pass@1
value: 36.6
name: pass@1
- type: pass@1
value: 37.2
name: pass@1
- type: pass@1
value: 40.9
name: pass@1
- type: pass@1
value: 26.2
name: pass@1
- type: pass@1
value: 35.4
name: pass@1
- type: pass@1
value: 22
name: pass@1
- type: pass@1
value: 25
name: pass@1
- type: pass@1
value: 18.9
name: pass@1
- type: pass@1
value: 29.9
name: pass@1
- type: pass@1
value: 17.1
name: pass@1
- type: pass@1
value: 26.8
name: pass@1
- type: pass@1
value: 14
name: pass@1
- type: pass@1
value: 18.3
name: pass@1
- type: pass@1
value: 23.2
name: pass@1
- type: pass@1
value: 29.9
name: pass@1
- type: pass@1
value: 24.4
name: pass@1
- type: pass@1
value: 16.5
name: pass@1
- type: pass@1
value: 3.7
name: pass@1
ibm-granite/granite-3b-code-base-2k - GGUF
This repo contains GGUF format model files for ibm-granite/granite-3b-code-base-2k.
The files were quantized using machines provided by TensorBlock, and they are compatible with llama.cpp as of commit b4011.
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Model file specification
| Filename | Quant type | File Size | Description |
|---|---|---|---|
| granite-3b-code-base-2k-Q2_K.gguf | Q2_K | 1.247 GB | smallest, significant quality loss - not recommended for most purposes |
| granite-3b-code-base-2k-Q3_K_S.gguf | Q3_K_S | 1.445 GB | very small, high quality loss |
| granite-3b-code-base-2k-Q3_K_M.gguf | Q3_K_M | 1.608 GB | very small, high quality loss |
| granite-3b-code-base-2k-Q3_K_L.gguf | Q3_K_L | 1.747 GB | small, substantial quality loss |
| granite-3b-code-base-2k-Q4_0.gguf | Q4_0 | 1.860 GB | legacy; small, very high quality loss - prefer using Q3_K_M |
| granite-3b-code-base-2k-Q4_K_S.gguf | Q4_K_S | 1.875 GB | small, greater quality loss |
| granite-3b-code-base-2k-Q4_K_M.gguf | Q4_K_M | 1.986 GB | medium, balanced quality - recommended |
| granite-3b-code-base-2k-Q5_0.gguf | Q5_0 | 2.251 GB | legacy; medium, balanced quality - prefer using Q4_K_M |
| granite-3b-code-base-2k-Q5_K_S.gguf | Q5_K_S | 2.251 GB | large, low quality loss - recommended |
| granite-3b-code-base-2k-Q5_K_M.gguf | Q5_K_M | 2.316 GB | large, very low quality loss - recommended |
| granite-3b-code-base-2k-Q6_K.gguf | Q6_K | 2.666 GB | very large, extremely low quality loss |
| granite-3b-code-base-2k-Q8_0.gguf | Q8_0 | 3.451 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/granite-3b-code-base-2k-GGUF --include "granite-3b-code-base-2k-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/granite-3b-code-base-2k-GGUF --local-dir MY_LOCAL_DIR --local-dir-use-symlinks False --include='*Q4_K*gguf'