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Phi-3-mini-4k-instruct-DPO-EPFL

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StefanKrsteski commited on May 31, 2024

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-# Model Card for Model ID
-Phi-3-mini-4k-instruct aligned using trl DPO on three datasets: EPFL-MNLP course (not yet publicly available), stackexchange (STEM only) and ultrafeedback.
 ## Model Details
-### Model Description
-<!-- Provide a longer summary of what this model is. -->
-- **Developed by:** [More Information Needed]
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-### Model Sources [optional]
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-## Uses
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-### Direct 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
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-### Training Procedure
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-#### Preprocessing [optional]
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-#### Training Hyperparameters
-- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
-#### Speeds, Sizes, Times [optional]
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-## Evaluation
-<!-- This section describes the evaluation protocols and provides the results. -->
-### 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]
-<!-- Relevant interpretability work for the model goes here -->
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-## Environmental Impact
-<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
-Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
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-## Technical Specifications [optional]
-### Model Architecture and Objective
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-## Model Card Contact
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-### Framework versions
 - PEFT 0.11.1

 - en
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+# Model Card for Phi-3-mini-4k-instruct DPO
 ## Model Details
+- **Model Name:** Phi-3-mini-4k-instruct DPO
+- **Publisher:** Team chatterbox, EPFL
+- **Model Type:** Language Model, Fine-tuned with Differential Privacy Optimization (DPO)
+- **Training Environment:** Trained on the EPFL SCITAS cluster using a 32GB GPU.
+## Intended Use
+- **Primary Applications:** This model is designed as part of an AI-Tutor system, aiming to accurately predict user preferences in educational scenarios.
+- **Intended Audience:** Educators, students, and developers creating educational AI applications.
+## Model/Data Description
 ### Training Data
+- **Datasets Used:**
+  - **Milestone 1 Dataset:** Includes [will fill] unique questions with preference pairs based on the 'overall' rating, totaling [will fill] usable entries after processing.
+  - **Stack Exchange Dataset:** Filters content from specific domains within the Stack Exchange network, using upvoted and downvoted answers to form preference pairs. Total entries: [will fill].
+  - **Ultra Feedback:** Utilizes responses rated on criteria like truthfulness and helpfulness to form preference pairs, with a total of [will fill] entries after preprocessing.
+- **Preprocessing Details:** Entries with identical chosen and rejected answers were removed. Datasets were formatted as JSONL where each line represents a JSON object with a "prompt", "chosen", and "rejected" response.
+## Training Procedure
+- **Configurations:** (Refer to the provided `training_args` and `trainer` configuration)
+- **Evaluation Metrics:** The primary metric for model performance is `eval_loss`, with the aim to minimize this value.
+## Evaluation Results
+- **Accuracies:** eval/rewards/accuracies - 0.83
+- **Loss:** eval/loss - 0.47
+- **Margins:** eval/margins - 4.31
+## References
+- **[Include references and citations for datasets, tools, and methodologies used.]**
 - PEFT 0.11.1