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]
- Repository: [https://github.com/medxiaorudan/CodeGeneration]
- Paper [optional]: [More Information Needed]
- Demo [optional]: [More Information Needed]
Uses
Direct Use
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Downstream Use [optional]
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Out-of-Scope Use
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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.
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Training Details
Training Data
https://huggingface.co/datasets/codeparrot/xlcost-text-to-code
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Training Procedure
The detailed training report is here.
Preprocessing [optional]
[More Information Needed]
Training Hyperparameters
- Training regime: [bf16]
Speeds, Sizes, Times [optional]
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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
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Factors
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Metrics
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Results
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Summary
Model Examination [optional]
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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
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Compute Infrastructure
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Hardware
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Software
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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