Girinath11/aiml_code_debug_model

Model Card for Model ID

This is a fine-tuned version of the 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]

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

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]

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