phi3-mini-4k-qlora-python-code-20k-mypo-4k-rfc

Model Card for Model ID

• Base Model: https://huggingface.co/edumunozsala/phi3-mini-4k-qlora-python-code-20k
• Preference Dataset: https://huggingface.co/datasets/joshuasundance/mypo-4k-rfc
• Training Code: https://gist.github.com/joshuasundance-swca/a94672960733782865932a645587ccdc
• Training Metrics: trainer_state.json

This is an experimental model made by using joshuasundance/mypo-4k-rfc for DPO training of edumunozsala/phi3-mini-4k-qlora-python-code-20k.

The goal is to learn about model training and potentially get the base model to reliably produce Python with type hints. I chose edumunozsala/phi3-mini-4k-qlora-python-code-20k because I was able to train this model in one hour on my laptop.

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: Joshua Sundance Bailey
• Model type: phi 3 qlora DPO
• Language(s) (NLP): English
• License: MIT
• Finetuned from model [optional]: edumunozsala/phi3-mini-4k-qlora-python-code-20k

Model Sources [optional]

• Training Code: https://gist.github.com/joshuasundance-swca/a94672960733782865932a645587ccdc

Uses

For evaluation and testing only. Do not expect great results, and do not use this model for anything important. It has not been evaluated in any way after training.

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

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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

• Original qlora: iamtarun/python_code_instructions_18k_alpaca
• DPO: joshuasundance/mypo-4k-rfc

Training Procedure

See training code using peft, transformers, and trl

Preprocessing [optional]

See training code using peft, transformers, and trl

Training Hyperparameters

See training code using peft, transformers, and trl

Speeds, Sizes, Times [optional]

See trainer_state.json in this repo

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Evaluation

See trainer_state.json in this repo

Testing Data, Factors & Metrics

Testing Data

20% of DPO dataset (see training code)

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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

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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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BibTeX:

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APA:

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Glossary [optional]

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More Information [optional]

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Model Card Authors [optional]

Joshua Sundance Bailey

Model Card Contact

Joshua Sundance Bailey