JamieAi33
/

Phi-2-QLora

@@ -1,205 +1,99 @@
----
 license: apache-2.0
 language:
 - en
 metrics:
-- bleu
 base_model:
 - microsoft/phi-2
 pipeline_tag: question-answering
 library_name: fastai
 ---
-# Model Card for Model ID
-<!-- Provide a quick summary of what the model is/does. -->
-This modelcard aims to be a base template for new models. It has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/modelcard_template.md?plain=1).
 ## Model Details
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 license: apache-2.0
 language:
 - en
 metrics:
+- rouge
 base_model:
 - microsoft/phi-2
 pipeline_tag: question-answering
 library_name: fastai
 ---
+# 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:** JamieAi33
+- **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-QLora
+- **Paper:** [Optional: Add a reference to PEFT or any relevant paper]
+- **Demo:** [Optional: Link to your Hugging Face Space or demo]
+---
 ## 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, "JamieAi33/Phi-2-QLora")
+# 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%                   |
+---
+## Training Configuration
+| Hyperparameter        | Value                   |
+|-----------------------|-------------------------|
+| **Batch Size**        | 1                       |
+| **Learning Rate**     | 2e-4                   |
+| **Max Steps**         | 1000                   |
+| **Optimizer**         | Paged AdamW (8-bit)    |
+| **Logging Steps**     | 25                     |
+| **Evaluation Steps**  | 25                     |
+| **Gradient Checkpointing** | Enabled          |