Instructions to use Siddharth-Yadav/llama-3.2-3b-BugFixer with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Siddharth-Yadav/llama-3.2-3b-BugFixer with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("Siddharth-Yadav/llama-3.2-3b-BugFixer", dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Model Card for Model ID
- Model Details
- Uses
- Bias, Risks, and Limitations
- How to Get Started with the Model
- Training Details
- Evaluation
- Model Examination [optional]
- Environmental Impact
- Technical Specifications [optional]
- Citation [optional]
- Glossary [optional]
- More Information [optional]
- Model Card Authors [optional]
- Model Card Contact
Model Card for Model ID
Model Details
Model Description
This model is used to generated correct verison of a buggy program along with the bug location as input
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- Developed by: Siddharth Yadav
- Funded by [optional]: [More Information Needed]
- Shared by [optional]: [More Information Needed]
- Model type: Code- Instruct
- Language(s) (NLP): [More Information Needed]
- License: [More Information Needed]
- Finetuned from model [optional]: Llama-Instruct 3B
Model Sources [optional]
- Repository: [More Information Needed]
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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
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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
It is trained on 325 code snippets database built from quixBugs python progrmas dataset. [More Information Needed]
Training Procedure
It is trained using QLora technique
Preprocessing [optional]
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Training Hyperparameters
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Speeds, Sizes, Times [optional]
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Evaluation
Testing Data, Factors & Metrics
Testing Data
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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
Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).
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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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Glossary [optional]
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Model Card Authors [optional]
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Model Card Contact
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