Model Card for Model ID
Coder-2b is a phi-2 fine tuned model trained on jondurbin/py-dpo-v0.1 using Reinforcement Learning from Human Feedback with DPO.
it is an instruct model capable of generating code starting from an instruction given by the user.
It is intended for those people who have few hardware resources available and want to speed up the process of writing Python code.
Model Details
Model Description
with the idea of creating a model that works on limited hardware, starting from a phi-2 model, coder-2b was fine-tuned with the Vezora/Tested-22k-Python-Alpaca dataset to make it capable of creating python code starting from from a user-written prompt. With further fine tuning, using the jondurbin/py-dpo-v0.1 dataset and leveraging the RLHF DPO technique, the model was further improved to produce more accurate outputs.
- Developed by: Lodo97
- Language(s) (NLP): English
- Finetuned from model Lodo97/Test1:
Model Sources [optional]
- Repository: Lodo97/coder-2b-v0.1-hfrl
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- Demo [optional]: [More Information Needed]
Uses
- Generate python code from an instruction provided by the user
- Find errors and bugs
- Rewrite code
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
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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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