| | --- |
| | library_name: transformers |
| | tags: |
| | - code |
| | - bug-fix |
| | - code-generation |
| | - code-repair |
| | - codet5p |
| | - ai |
| | - machine-learning |
| | - deep-learning |
| | - huggingface |
| | - finetuned-model |
| | license: apache-2.0 |
| | datasets: |
| | - Girinath11/aiml_code_debug_dataset |
| | metrics: |
| | - bleu |
| | base_model: |
| | - Salesforce/codet5p-220m |
| | --- |
| | |
| | # Model Card for Model ID |
| |
|
| | This is a fine-tuned version of the [Salesforce/codet5p-220m](https://huggingface.co/Salesforce/codet5p-220m) model, specialized for real-world AI, ML, and Deep Learning code bug-fix tasks. |
| | The model was trained on 150,000 code pairs (buggy → fixed) extracted from GitHub projects relevant to the AI/ML/GenAI ecosystem. |
| | It is optimized for suggesting correct code fixes from faulty code snippets and is highly effective for debugging and auto-correction in AI coding environments. |
| |
|
| | ## Model Details |
| |
|
| | ### Model Description |
| |
|
| | 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:** [Girinath V] |
| | - **Funded by [optional]:** [More Information Needed] |
| | - **Shared by [optional]:** [More Information Needed] |
| | - **Model type:** [Text-to-text Transformer (Encoder-Decoder)] |
| | - **Language(s) (NLP):** [Programming (Python, some support for other AI/ML languages] |
| | - **License:** [Apache 2.0] |
| | - **Finetuned from model:** [[Salesforce/codet5p-220m](https://huggingface.co/Salesforce/codet5p-220m)] |
| |
|
| | ### Model Sources: |
| |
|
| | - **Repository:** [More Information Needed] |
| | - **Paper [optional]:** [More Information Needed] |
| | - **Demo [optional]:** [More Information Needed] |
| |
|
| | ## Uses |
| |
|
| | ### Direct Use |
| |
|
| | -Fix real-world AI/ML/GenAI Python code bugs. |
| | - Debug model training scripts, data pipelines, and inference code. |
| | - Educational use for learning from code correction. |
| |
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| |
|
| | ### Downstream Use [optional] |
| |
|
| | - Integrated into code review pipelines. |
| | - LLM-enhanced IDE plugins for auto-fixing AI-related bugs. |
| | - Assistant agents in AI-powered coding copilots. |
| |
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| |
|
| | ### Out-of-Scope Use |
| |
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| | - General-purpose natural language tasks. |
| | - Code generation unrelated to AI/ML domains. |
| | - Use on production code without human review. |
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| |
|
| | ## Bias, Risks, and Limitations |
| |
|
| | ## Biases |
| |
|
| | - Model favors AI/ML/GenAI-related Python patterns. |
| | - Not trained for full-stack or UI/frontend code debugging. |
| |
|
| | ### Limitations |
| |
|
| | - May not generalize well outside its fine-tuned domain. |
| | - Struggles with ambiguous or undocumented buggy code. |
| |
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| |
|
| | ### Recommendations |
| |
|
| | - Use alongside human review. |
| | - Combine with static analysis for best results. |
| |
|
| |
|
| | ## How to Get Started with the Model |
| |
|
| | from transformers import AutoTokenizer, AutoModelForSeq2SeqLM |
| | tokenizer = AutoTokenizer.from_pretrained("Girinath11/aiml_code_debug_model") |
| | model = AutoModelForSeq2SeqLM.from_pretrained("Girinath11/aiml_code_debug_model") |
| | inputs = tokenizer("buggy: def add(a,b) return a+b", return_tensors="pt") |
| | outputs = model.generate(**inputs) |
| | print(tokenizer.decode(outputs[0])) |
| | |
| | |
| | ## Training Details |
| | |
| | ### Training Data |
| | |
| | -150,000 real-world buggy–fixed Python code pairs. |
| | |
| | -Data collected from GitHub AI/ML repositories. |
| | |
| | -Includes data cleaning, formatting, deduplication. |
| | |
| | |
| | |
| | ### Training Procedure |
| | |
| | <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> |
| | |
| | #### Preprocessing [optional] |
| | |
| | [More Information Needed] |
| | |
| | |
| | #### Training Hyperparameters |
| | |
| | - **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> |
| | |
| | #### Speeds, Sizes, Times [optional] |
| | |
| | <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> |
| | |
| | [More Information Needed] |
| | |
| | ## Evaluation |
| | |
| | <!-- This section describes the evaluation protocols and provides the results. --> |
| | |
| | ### Testing Data, Factors & Metrics |
| | |
| | #### Testing Data |
| | |
| | <!-- This should link to a Dataset Card if possible. --> |
| | |
| | [More Information Needed] |
| | |
| | #### Factors |
| | |
| | <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> |
| | |
| | [More Information Needed] |
| | |
| | #### Metrics |
| | |
| | <!-- These are the evaluation metrics being used, ideally with a description of why. --> |
| | |
| | [More Information Needed] |
| | |
| | ### Results |
| | |
| | [More Information Needed] |
| | |
| | #### Summary |
| | |
| | |
| | |
| | ## Model Examination [optional] |
| | |
| | <!-- Relevant interpretability work for the model goes here --> |
| | |
| | [More Information Needed] |
| | |
| | ## Environmental Impact |
| | |
| | <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> |
| | |
| | Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). |
| | |
| | - **Hardware Type:** [More Information Needed] |
| | - **Hours used:** [More Information Needed] |
| | - **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] |
| | |
| | <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> |
| | |
| | **BibTeX:** |
| | |
| | [More Information Needed] |
| | |
| | **APA:** |
| | |
| | [More Information Needed] |
| | |
| | ## Glossary [optional] |
| | |
| | <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> |
| | |
| | [More Information Needed] |
| | |
| | ## More Information [optional] |
| | |
| | [More Information Needed] |
| | |
| | ## Model Card Authors [optional] |
| | |
| | [More Information Needed] |
| | |
| | ## Model Card Contact |
| | |
| | [More Information Needed] |
