FerdinandC/llama-autocomplete-code-finetuned

Model Card for Model ID

This model is a fine-tuned version of LLaMA 3.1_8B, optimized specifically for Python code generation. Trained on a dataset of Python code examples, it is designed to generate accurate Python code snippets based on textual prompts. It understands Python syntax, structures, and common coding patterns, making it suitable for tasks such as code completion, function generation, and problem-solving in Python.

This model is particularly useful for developers looking for automated assistance in Python coding tasks, providing suggestions or full code blocks to accelerate the development process. Its specialized training allows it to generate well-formed Python code with a higher degree of accuracy compared to a general-purpose language model.

While the model performs well in generating Python code, it may still require validation to ensure the output adheres to the expected behavior in specific contexts. Integration into IDEs or use cases like code autocompletion tools can enhance developer productivity by reducing manual effort and improving coding efficiency.

This model can be a valuable resource for anyone working with Python, from beginners to experienced programmers seeking code automation.

Model Details

Model Description

• Developed by: [FerdinandC]
• Funded by [optional]: [More Information Needed]
• Shared by [optional]: [More Information Needed]
• Model type: [text generation]
• Language(s) (NLP): [python, transformers, peft]
• License: [More Information Needed]
• Finetuned from model [optional]: [meta-llama/Llama-3.1-8B-Instruct]

Model Sources [optional]

• Repository: [https://huggingface.co/FerdinandC/llama-autocomplete-code-finetuned/tree/main]
• Paper [optional]: [More Information Needed]
• Demo [optional]: [More Information Needed]

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

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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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Framework versions

• PEFT 0.12.0