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wyt2000_-_InverseCoder-DS-6.7B-8bits
RichardErkhov/wyt2000_-_InverseCoder-DS-6.7B-8bits
wyt2000_-_InverseCoder-DS-6.7B-8bits is a machine learning model from RichardErkhov. Use it for the machine learning task on the model card, and read the license before you ship it in a product.
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Model card
From the Hugging Face model README
Quantization made by Richard Erkhov.
InverseCoder-DS-6.7B - bnb 8bits
- Model creator: https://huggingface.co/wyt2000/
- Original model: https://huggingface.co/wyt2000/InverseCoder-DS-6.7B/
Original model description:
license: other license_name: deepseek license_link: LICENSE datasets:
- wyt2000/InverseCoder-DS-6.7B-Evol-Instruct-90K
- ise-uiuc/Magicoder-Evol-Instruct-110K library_name: transformers pipeline_tag: text-generation tags:
- code model-index:
- name: InverseCoder-DS-6.7B
results:
- task:
type: text-generation
dataset:
type: openai_humaneval
name: HumanEval
metrics:
- name: pass@1 type: pass@1 value: 0.799 verified: false
- task:
type: text-generation
dataset:
type: openai_humaneval
name: HumanEval(+)
metrics:
- name: pass@1 type: pass@1 value: 0.768 verified: false
- task:
type: text-generation
dataset:
type: mbpp
name: MBPP
metrics:
- name: pass@1 type: pass@1 value: 0.786 verified: false
- task:
type: text-generation
dataset:
type: mbpp
name: MBPP(+)
metrics:
- name: pass@1 type: pass@1 value: 0.690 verified: false
- task:
type: text-generation
dataset:
type: ds1000
name: DS-1000 (Overall Completion)
metrics:
- name: pass@1 type: pass@1 value: 0.442 verified: false
- task:
type: text-generation
dataset:
type: nuprl/MultiPL-E
name: MultiPL-HumanEval (Java)
metrics:
- name: pass@1 type: pass@1 value: 0.607 verified: false
- task:
type: text-generation
dataset:
type: nuprl/MultiPL-E
name: MultiPL-HumanEval (JavaScript)
metrics:
- name: pass@1 type: pass@1 value: 0.701 verified: false
- task:
type: text-generation
dataset:
type: nuprl/MultiPL-E
name: MultiPL-HumanEval (C++)
metrics:
- name: pass@1 type: pass@1 value: 0.705 verified: false
- task:
type: text-generation
dataset:
type: nuprl/MultiPL-E
name: MultiPL-HumanEval (PHP)
metrics:
- name: pass@1 type: pass@1 value: 0.636 verified: false
- task:
type: text-generation
dataset:
type: nuprl/MultiPL-E
name: MultiPL-HumanEval (Swift)
metrics:
- name: pass@1 type: pass@1 value: 0.530 verified: false
- task:
type: text-generation
dataset:
type: nuprl/MultiPL-E
name: MultiPL-HumanEval (Rust)
metrics:
- name: pass@1 type: pass@1 value: 0.574 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: 0.626 verified: false
- task:
type: text-generation
dataset:
type: openai_humaneval
name: HumanEval
metrics:
<div align="center"> <img src="https://huggingface.co/wyt2000/InverseCoder-CL-7B/resolve/main/assets/logo.png" style="zoom:25%;" /> </div>
InverseCoder: Unleashing the Power of Instruction-Tuned Code LLMs with Inverse-Instruct
<img src="https://huggingface.co/wyt2000/InverseCoder-CL-7B/resolve/main/assets/overview.png" style="zoom:50%;" />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-DS-6.7B",
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},
}
Code
Official code repo for Inverse-Instruct (under development).
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
- Type
- llama
- Created
- Mar 16, 2025
- Updated
- Mar 16, 2025