metadata
license: bigcode-openrail-m
library_name: transformers
tags:
- code
- mlx
base_model: bigcode/starcoder2-15b
datasets:
- bigcode/self-oss-instruct-sc2-exec-filter-50k
pipeline_tag: text-generation
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
mlx-community/starcoder2-15b-instruct-v0.1
This model was converted to MLX format from bigcode/starcoder2-15b-instruct-v0.1
using mlx-lm version 0.12.1.
Refer to the original model card for more details on the model.
Use with mlx
pip install mlx-lm
from mlx_lm import load, generate
model, tokenizer = load("mlx-community/starcoder2-15b-instruct-v0.1")
response = generate(model, tokenizer, prompt="hello", verbose=True)