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metadata
library_name: peft
base_model: codellama/CodeLlama-7b-hf
license: llama2
dataset:
  type: codeparrot/xlcost-text-to-code
  name: xlcost
tags:
  - code

Model Card for Model ID

Model Details

Model Description

This model is fine-tuned base CodeLlama with C++ code from the 'codeparrot/xlcost-text-to-code' dataset. It can generate C++ code with specific task descriptions. If you get the error "ValueError: Tokenizer class CodeLlamaTokenizer does not exist or is not currently imported." make sure your Transformer version is 4.33.0 and accelerate>=0.20.3.

  • Developed by: [Rudan XIAO]
  • Model type: [code generation]
  • License: [llama2]
  • Finetuned from model [optional]: [codellama/CodeLlama-7b-hf]

Model Sources [optional]

Uses

Direct Use

[More Information Needed]

Downstream Use [optional]

[More Information Needed]

Out-of-Scope Use

[More Information Needed]

Bias, Risks, and Limitations

[More Information Needed]

Recommendations

Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.

How to Get Started with the Model

Use the code below to get started with the model.

[More Information Needed]

Training Details

Training Data

https://huggingface.co/datasets/codeparrot/xlcost-text-to-code

[More Information Needed]

Training Procedure

The detailed training report is here.

Preprocessing [optional]

[More Information Needed]

Training Hyperparameters

  • Training regime: [bf16]

Speeds, Sizes, Times [optional]

[More Information Needed]

Evaluation

I have use the Catch2 unit test framework for generated C++ code snippets correctness verification.

Todo: Use the pass@k metric with the HumanEval-X dataset to verify the performance of the model.

Testing Data, Factors & Metrics

Testing Data

https://huggingface.co/datasets/THUDM/humaneval-x

[More Information Needed]

Factors

[More Information Needed]

Metrics

[More Information Needed]

Results

[More Information Needed]

Summary

Model Examination [optional]

[More Information Needed]

Environmental Impact

I used 4 NVIDIA A40-48Q GPU server configured with Python 3.10 and Cuda 12.2 to run the code in this article. It ran for about eight hours.

Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).

  • Hardware Type: [NVIDIA A40-48Q GPU]
  • Hours used: [8]
  • Cloud Provider: [More Information Needed]
  • Compute Region: [More Information Needed]
  • Carbon Emitted: [More Information Needed]

Technical Specifications [optional]

Model Architecture and Objective

[More Information Needed]

Compute Infrastructure

[More Information Needed]

Hardware

[More Information Needed]

Software

[More Information Needed]

Citation [optional]

BibTeX:

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APA:

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Glossary [optional]

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More Information [optional]

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Model Card Authors [optional]

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Model Card Contact

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Framework versions

  • PEFT 0.7.1