Transformers
Safetensors
code
bug-fix
code-generation
code-repair
codet5p
ai
machine-learning
deep-learning
huggingface
finetuned-model
Instructions to use Girinath11/aiml_code_debug_model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Girinath11/aiml_code_debug_model with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("Girinath11/aiml_code_debug_model", device_map="auto") - Notebooks
- Google Colab
- Kaggle
| 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. | |
| ### 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. | |
| ### Out-of-Scope Use | |
| - General-purpose natural language tasks. | |
| - Code generation unrelated to AI/ML domains. | |
| - Use on production code without human review. | |
| ## 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. | |
| ### 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] |