metadata
license: llama2
datasets:
- wyt2000/InverseCoder-CL-7B-Evol-Instruct-90K
- ise-uiuc/Magicoder-Evol-Instruct-110K
library_name: transformers
pipeline_tag: text-generation
tags:
- code
model-index:
- name: InverseCoder-CL-7B
results:
- task:
type: text-generation
dataset:
type: openai_humaneval
name: HumanEval
metrics:
- name: pass@1
type: pass@1
value: 0.762
verified: false
- task:
type: text-generation
dataset:
type: openai_humaneval
name: HumanEval(+)
metrics:
- name: pass@1
type: pass@1
value: 0.72
verified: false
- task:
type: text-generation
dataset:
type: mbpp
name: MBPP
metrics:
- name: pass@1
type: pass@1
value: 0.706
verified: false
- task:
type: text-generation
dataset:
type: mbpp
name: MBPP(+)
metrics:
- name: pass@1
type: pass@1
value: 0.601
verified: false
- task:
type: text-generation
dataset:
type: ds1000
name: DS-1000 (Overall Completion)
metrics:
- name: pass@1
type: pass@1
value: 0.399
verified: false
- task:
type: text-generation
dataset:
type: nuprl/MultiPL-E
name: MultiPL-HumanEval (Java)
metrics:
- name: pass@1
type: pass@1
value: 0.487
verified: false
- task:
type: text-generation
dataset:
type: nuprl/MultiPL-E
name: MultiPL-HumanEval (JavaScript)
metrics:
- name: pass@1
type: pass@1
value: 0.619
verified: false
- task:
type: text-generation
dataset:
type: nuprl/MultiPL-E
name: MultiPL-HumanEval (C++)
metrics:
- name: pass@1
type: pass@1
value: 0.526
verified: false
- task:
type: text-generation
dataset:
type: nuprl/MultiPL-E
name: MultiPL-HumanEval (PHP)
metrics:
- name: pass@1
type: pass@1
value: 0.552
verified: false
- task:
type: text-generation
dataset:
type: nuprl/MultiPL-E
name: MultiPL-HumanEval (Swift)
metrics:
- name: pass@1
type: pass@1
value: 0.53
verified: false
- task:
type: text-generation
dataset:
type: nuprl/MultiPL-E
name: MultiPL-HumanEval (Rust)
metrics:
- name: pass@1
type: pass@1
value: 0.461
verified: false
- task:
type: text-generation
dataset:
type: nuprl/MultiPL-E
name: MultiPL-HumanEval (Average for non-python languages)
metrics:
- name: pass@1
type: pass@1
value: 0.529
verified: false
InverseCoder: Unleashing the Power of Instruction-Tuned Code LLMs with Inverse-Instruct
InverseCoder is a series of code LLMs instruction-tuned by generating data from itself through Inverse-Instruct.
Models and Datasets
Usage
Similar to Magicoder-S-DS-6.7B, use the code below to get started with the model. Make sure you installed the transformers library.
from transformers import pipeline
import torch
INVERSECODER_PROMPT = """You are an exceptionally intelligent coding assistant that consistently delivers accurate and reliable responses to user instructions.
@@ Instruction
{instruction}
@@ Response
"""
instruction = <Your code instruction here>
prompt = INVERSECODER_PROMPT.format(instruction=instruction)
generator = pipeline(
model="wyt2000/InverseCoder-CL-7B",
task="text-generation",
torch_dtype=torch.bfloat16,
device_map="auto",
)
result = generator(prompt, max_length=1024, num_return_sequences=1, temperature=0.0)
print(result[0]["generated_text"])
Paper
Arxiv: https://arxiv.org/abs/2407.05700
Please cite the paper if you use the models or datasets from InverseCoder.
@misc{wu2024inversecoderunleashingpowerinstructiontuned,
title={InverseCoder: Unleashing the Power of Instruction-Tuned Code LLMs with Inverse-Instruct},
author={Yutong Wu and Di Huang and Wenxuan Shi and Wei Wang and Lingzhe Gao and Shihao Liu and Ziyuan Nan and Kaizhao Yuan and Rui Zhang and Xishan Zhang and Zidong Du and Qi Guo and Yewen Pu and Dawei Yin and Xing Hu and Yunji Chen},
year={2024},
eprint={2407.05700},
archivePrefix={arXiv},
primaryClass={cs.CL},
url={https://arxiv.org/abs/2407.05700},
}
Acknowledgements
- Magicoder: Training code, original datasets and data decontamination
- DeepSeek-Coder: Base model for InverseCoder-DS
- CodeLlama: Base model for InverseCoder-CL
- AutoMathText: Self-evaluation and data selection method