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]
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
[More Information Needed]
Downstream Use [optional]
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Out-of-Scope Use
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Bias, Risks, and Limitations
[More Information Needed]
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.
[More Information Needed]
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
[More Information Needed]
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
Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).
- 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
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Compute Infrastructure
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Hardware
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Software
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Citation [optional]
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