JamieAi33
/

Phi-2_PEFT

@@ -1,202 +1,98 @@
 ---
 base_model: microsoft/phi-2
 library_name: peft
 ---
-# Model Card for Model ID
-<!-- Provide a quick summary of what the model is/does. -->
 ## Model Details
 ### Model Description
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-- **Developed by:** [More Information Needed]
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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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-#### Preprocessing [optional]
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-#### Training Hyperparameters
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-## Environmental Impact
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-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]
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-### Framework versions
-- PEFT 0.13.2

 ---
 base_model: microsoft/phi-2
 library_name: peft
+license: apache-2.0
+datasets:
+- neil-code/dialogsum-test
+language:
+- en
+metrics:
+- bleu
+pipeline_tag: question-answering
 ---
+license: apache-2.0
+language:
+- en
+metrics:
+- rouge
+base_model:
+- microsoft/phi-2
+pipeline_tag: question-answering
+---
+# Model Card for PEFT-Fine-Tuned Model
+This model card documents a PEFT-fine-tuned version of `microsoft/phi-2` for question-answering tasks. The PEFT fine-tuning improved the model's performance, as detailed in the evaluation section.
 ## Model Details
 ### Model Description
+- **Developed by:** User (you can replace with your name or organization)
+- **Finetuned from model:** `microsoft/phi-2`
+- **Model type:** PEFT fine-tuned transformer
+- **Language(s) (NLP):** English
+- **License:** Apache 2.0
+The base model `microsoft/phi-2` was adapted using Parameter-Efficient Fine-Tuning (PEFT) for question-answering tasks. The training process focused on improving performance metrics while keeping computational costs low.
+---
+### Model Sources
+- **Repository:** https://huggingface.co/JamieAi33/Phi-2_PEFT
+---
 ## Uses
 ### Direct Use
+This model can be used out-of-the-box for question-answering tasks.
+### Downstream Use
+The model can be fine-tuned further on domain-specific datasets for improved performance.
 ### Out-of-Scope Use
+Avoid using this model for tasks outside question-answering or where fairness, bias, and ethical considerations are critical without further validation.
+---
 ## Bias, Risks, and Limitations
+Users should be aware that:
+- The model is trained on publicly available data and may inherit biases present in the training data.
+- It is optimized for English and may perform poorly in other languages.
+---
 ## How to Get Started with the Model
+Here’s an example of loading the model:
+```python
+from transformers import AutoModel
+from peft import PeftModel
+base_model = AutoModel.from_pretrained("microsoft/phi-2")
+adapter_model = PeftModel.from_pretrained(base_model, "Phi-2_PEFT")
+# Model Name: PEFT Fine-Tuned `microsoft/phi-2`
+This repository contains a PEFT fine-tuned version of the `microsoft/phi-2` model for question-answering tasks. The fine-tuning process leveraged Parameter-Efficient Fine-Tuning (PEFT) techniques to achieve improved performance.
+---
+## Metrics
+The model's performance was evaluated using the ROUGE metric. Below are the results:
+| **Metric**     | **Original Model** | **PEFT Model** | **Absolute Improvement** |
+|-----------------|--------------------|----------------|---------------------------|
+| **ROUGE-1**    | 29.76%             | 44.51%         | +14.75%                  |
+| **ROUGE-2**    | 10.76%             | 15.68%         | +4.92%                   |
+| **ROUGE-L**    | 21.69%             | 30.95%         | +9.25%                   |
+| **ROUGE-Lsum** | 22.75%             | 31.49%         | +8.74%                   |
+---