metadata
license: apache-2.0
library_name: transformers
tags:
- code
- mlx
datasets:
- codeparrot/github-code-clean
- bigcode/starcoderdata
- open-web-math/open-web-math
- math-ai/StackMathQA
metrics:
- code_eval
pipeline_tag: text-generation
inference: false
model-index:
- name: granite-3b-code-base
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
mlx-community/granite-3b-code-base-8bit
The Model mlx-community/granite-3b-code-base-8bit was converted to MLX format from ibm-granite/granite-3b-code-base using mlx-lm version 0.12.0.
Use with mlx
pip install mlx-lm
from mlx_lm import load, generate
model, tokenizer = load("mlx-community/granite-3b-code-base-8bit")
response = generate(model, tokenizer, prompt="hello", verbose=True)