metadata
pipeline_tag: text-generation
base_model: ibm-granite/granite-3b-code-instruct-2k
inference: false
license: apache-2.0
datasets:
- bigcode/commitpackft
- TIGER-Lab/MathInstruct
- meta-math/MetaMathQA
- glaiveai/glaive-code-assistant-v3
- glaive-function-calling-v2
- bugdaryan/sql-create-context-instruction
- garage-bAInd/Open-Platypus
- nvidia/HelpSteer
metrics:
- code_eval
library_name: transformers
tags:
- code
- granite
- TensorBlock
- GGUF
model-index:
- name: granite-3b-code-instruct
results:
- task:
type: text-generation
dataset:
name: HumanEvalSynthesis(Python)
type: bigcode/humanevalpack
metrics:
- type: pass@1
value: 51.2
name: pass@1
- type: pass@1
value: 43.9
name: pass@1
- type: pass@1
value: 41.5
name: pass@1
- type: pass@1
value: 31.7
name: pass@1
- type: pass@1
value: 40.2
name: pass@1
- type: pass@1
value: 29.3
name: pass@1
- type: pass@1
value: 39.6
name: pass@1
- type: pass@1
value: 26.8
name: pass@1
- type: pass@1
value: 39
name: pass@1
- type: pass@1
value: 14
name: pass@1
- type: pass@1
value: 23.8
name: pass@1
- type: pass@1
value: 12.8
name: pass@1
- type: pass@1
value: 26.8
name: pass@1
- type: pass@1
value: 28
name: pass@1
- type: pass@1
value: 33.5
name: pass@1
- type: pass@1
value: 27.4
name: pass@1
- type: pass@1
value: 31.7
name: pass@1
- type: pass@1
value: 16.5
name: pass@1
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ibm-granite/granite-3b-code-instruct-2k - GGUF
This repo contains GGUF format model files for ibm-granite/granite-3b-code-instruct-2k.
The files were quantized using machines provided by TensorBlock, and they are compatible with llama.cpp as of commit b4011.
Prompt template
System:
{system_prompt}
Question:
{prompt}
Answer:
Model file specification
| Filename | Quant type | File Size | Description |
|---|---|---|---|
| granite-3b-code-instruct-2k-Q2_K.gguf | Q2_K | 1.247 GB | smallest, significant quality loss - not recommended for most purposes |
| granite-3b-code-instruct-2k-Q3_K_S.gguf | Q3_K_S | 1.445 GB | very small, high quality loss |
| granite-3b-code-instruct-2k-Q3_K_M.gguf | Q3_K_M | 1.608 GB | very small, high quality loss |
| granite-3b-code-instruct-2k-Q3_K_L.gguf | Q3_K_L | 1.747 GB | small, substantial quality loss |
| granite-3b-code-instruct-2k-Q4_0.gguf | Q4_0 | 1.860 GB | legacy; small, very high quality loss - prefer using Q3_K_M |
| granite-3b-code-instruct-2k-Q4_K_S.gguf | Q4_K_S | 1.875 GB | small, greater quality loss |
| granite-3b-code-instruct-2k-Q4_K_M.gguf | Q4_K_M | 1.986 GB | medium, balanced quality - recommended |
| granite-3b-code-instruct-2k-Q5_0.gguf | Q5_0 | 2.251 GB | legacy; medium, balanced quality - prefer using Q4_K_M |
| granite-3b-code-instruct-2k-Q5_K_S.gguf | Q5_K_S | 2.251 GB | large, low quality loss - recommended |
| granite-3b-code-instruct-2k-Q5_K_M.gguf | Q5_K_M | 2.316 GB | large, very low quality loss - recommended |
| granite-3b-code-instruct-2k-Q6_K.gguf | Q6_K | 2.666 GB | very large, extremely low quality loss |
| granite-3b-code-instruct-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-instruct-2k-GGUF --include "granite-3b-code-instruct-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-instruct-2k-GGUF --local-dir MY_LOCAL_DIR --local-dir-use-symlinks False --include='*Q4_K*gguf'