metadata
license: apache-2.0
library_name: transformers
tags:
- code
- granite
- 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: true
model-index:
- name: granite-34b-code-base
results:
- task:
type: text-generation
dataset:
name: MBPP
type: mbpp
metrics:
- type: pass@1
value: 47.2
name: pass@1
- task:
type: text-generation
dataset:
name: MBPP+
type: evalplus/mbppplus
metrics:
- type: pass@1
value: 53.1
name: pass@1
- task:
type: text-generation
dataset:
name: HumanEvalSynthesis(Python)
type: bigcode/humanevalpack
metrics:
- type: pass@1
value: 48.2
name: pass@1
- type: pass@1
value: 54.9
name: pass@1
- type: pass@1
value: 61.6
name: pass@1
- type: pass@1
value: 40.2
name: pass@1
- type: pass@1
value: 50
name: pass@1
- type: pass@1
value: 39.6
name: pass@1
- type: pass@1
value: 42.7
name: pass@1
- type: pass@1
value: 26.2
name: pass@1
- type: pass@1
value: 47
name: pass@1
- type: pass@1
value: 26.8
name: pass@1
- type: pass@1
value: 36.6
name: pass@1
- type: pass@1
value: 25
name: pass@1
- type: pass@1
value: 20.1
name: pass@1
- type: pass@1
value: 30.5
name: pass@1
- type: pass@1
value: 40.9
name: pass@1
- type: pass@1
value: 34.1
name: pass@1
- type: pass@1
value: 39
name: pass@1
- type: pass@1
value: 12.2
name: pass@1
mlx-community/granite-34b-code-base-4bit
The Model mlx-community/granite-34b-code-base-4bit was converted to MLX format from ibm-granite/granite-34b-code-base using mlx-lm version 0.13.0.
Use with mlx
pip install mlx-lm
from mlx_lm import load, generate
model, tokenizer = load("mlx-community/granite-34b-code-base-4bit")
response = generate(model, tokenizer, prompt="hello", verbose=True)