---
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]

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[More Information Needed]

## Evaluation

<!-- This section describes the evaluation protocols and provides the results. -->

### Testing Data, Factors & Metrics

#### Testing Data

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[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

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#### 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

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#### Software

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## 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:**

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## 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]

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## Model Card Authors [optional]

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## Model Card Contact

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