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- ---
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  license: apache-2.0
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  language:
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  - en
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  metrics:
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- - bleu
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  base_model:
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  - microsoft/phi-2
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  pipeline_tag: question-answering
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  library_name: fastai
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  ---
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- # Model Card for Model ID
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-
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- <!-- Provide a quick summary of what the model is/does. -->
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- 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).
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  ## Model Details
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  ### Model Description
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- <!-- Provide a longer summary of what this model is. -->
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- - **Developed by:** [More Information Needed]
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- - **Funded by [optional]:** [More Information Needed]
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- - **Shared by [optional]:** [More Information Needed]
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- - **Model type:** [More Information Needed]
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- - **Language(s) (NLP):** [More Information Needed]
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- - **License:** [More Information Needed]
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- - **Finetuned from model [optional]:** [More Information Needed]
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- ### Model Sources [optional]
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- <!-- Provide the basic links for the model. -->
 
 
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- - **Repository:** [More Information Needed]
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- - **Paper [optional]:** [More Information Needed]
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- - **Demo [optional]:** [More Information Needed]
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  ## Uses
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- <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
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-
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  ### Direct Use
 
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- <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
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- [More Information Needed]
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-
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- ### Downstream Use [optional]
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-
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- <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
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-
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- [More Information Needed]
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  ### Out-of-Scope Use
 
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- <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
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- [More Information Needed]
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  ## Bias, Risks, and Limitations
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- <!-- This section is meant to convey both technical and sociotechnical limitations. -->
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- [More Information Needed]
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-
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- ### Recommendations
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- <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
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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.
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  ## How to Get Started with the Model
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- Use the code below to get started with the model.
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- [More Information Needed]
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-
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- ## Training Details
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-
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- ### Training Data
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-
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- <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
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- [More Information Needed]
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-
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- ### Training Procedure
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-
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- <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
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- #### Preprocessing [optional]
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-
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- [More Information Needed]
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- #### Training Hyperparameters
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- - **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
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-
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- #### Speeds, Sizes, Times [optional]
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-
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- <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
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- [More Information Needed]
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-
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- ## Evaluation
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-
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- <!-- This section describes the evaluation protocols and provides the results. -->
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-
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- ### Testing Data, Factors & Metrics
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- #### Testing Data
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- <!-- This should link to a Dataset Card if possible. -->
 
 
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- [More Information Needed]
 
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- #### Factors
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- <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
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- [More Information Needed]
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- #### Metrics
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- <!-- These are the evaluation metrics being used, ideally with a description of why. -->
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- [More Information Needed]
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- ### Results
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- [More Information Needed]
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- #### Summary
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- ## Model Examination [optional]
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- <!-- Relevant interpretability work for the model goes here -->
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- [More Information Needed]
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- ## Environmental Impact
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- <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
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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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- - **Hardware Type:** [More Information Needed]
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- - **Hours used:** [More Information Needed]
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- - **Cloud Provider:** [More Information Needed]
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- - **Compute Region:** [More Information Needed]
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- - **Carbon Emitted:** [More Information Needed]
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- ## Technical Specifications [optional]
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- ### Model Architecture and Objective
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- [More Information Needed]
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- ### Compute Infrastructure
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- [More Information Needed]
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- #### Hardware
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- [More Information Needed]
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- #### Software
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- [More Information Needed]
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- ## Citation [optional]
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- <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
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- **BibTeX:**
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- [More Information Needed]
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- **APA:**
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- [More Information Needed]
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- ## Glossary [optional]
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- <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
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- [More Information Needed]
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- ## More Information [optional]
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- [More Information Needed]
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- ## Model Card Authors [optional]
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- [More Information Needed]
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- ## Model Card Contact
 
 
 
 
 
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- [More Information Needed]
 
 
 
 
 
 
 
 
 
 
 
 
 
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  license: apache-2.0
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  language:
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  - en
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  metrics:
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+ - rouge
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  base_model:
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  - microsoft/phi-2
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  pipeline_tag: question-answering
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  library_name: fastai
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  ---
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+ # Model Card for PEFT-Fine-Tuned Model
 
 
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+ 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.
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  ## Model Details
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  ### Model Description
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+ - **Developed by:** JamieAi33
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+ - **Finetuned from model:** `microsoft/phi-2`
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+ - **Model type:** PEFT fine-tuned transformer
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+ - **Language(s) (NLP):** English
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+ - **License:** Apache 2.0
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+ 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.
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+ ---
 
 
 
 
 
 
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+ ### Model Sources
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+ - **Repository:** https://huggingface.co/JamieAi33/Phi-2-QLora
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+ - **Paper:** [Optional: Add a reference to PEFT or any relevant paper]
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+ - **Demo:** [Optional: Link to your Hugging Face Space or demo]
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+ ---
 
 
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  ## Uses
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  ### Direct Use
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+ This model can be used out-of-the-box for question-answering tasks.
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+ ### Downstream Use
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+ The model can be fine-tuned further on domain-specific datasets for improved performance.
 
 
 
 
 
 
 
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  ### Out-of-Scope Use
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+ Avoid using this model for tasks outside question-answering or where fairness, bias, and ethical considerations are critical without further validation.
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+ ---
 
 
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  ## Bias, Risks, and Limitations
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+ Users should be aware that:
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+ - The model is trained on publicly available data and may inherit biases present in the training data.
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+ - It is optimized for English and may perform poorly in other languages.
 
 
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+ ---
 
 
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  ## How to Get Started with the Model
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+ Here鈥檚 an example of loading the model:
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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+ ```python
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+ from transformers import AutoModel
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+ from peft import PeftModel
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+ base_model = AutoModel.from_pretrained("microsoft/phi-2")
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+ adapter_model = PeftModel.from_pretrained(base_model, "JamieAi33/Phi-2-QLora")
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+ # Model Name: PEFT Fine-Tuned `microsoft/phi-2`
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+ 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.
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+ ---
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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+ ## Metrics
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+ The model's performance was evaluated using the ROUGE metric. Below are the results:
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+ | **Metric** | **Original Model** | **PEFT Model** | **Absolute Improvement** |
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+ |-----------------|--------------------|----------------|---------------------------|
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+ | **ROUGE-1** | 29.76% | 44.51% | +14.75% |
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+ | **ROUGE-2** | 10.76% | 15.68% | +4.92% |
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+ | **ROUGE-L** | 21.69% | 30.95% | +9.25% |
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+ | **ROUGE-Lsum** | 22.75% | 31.49% | +8.74% |
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+ ---
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+ ## Training Configuration
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+ | Hyperparameter | Value |
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+ |-----------------------|-------------------------|
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+ | **Batch Size** | 1 |
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+ | **Learning Rate** | 2e-4 |
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+ | **Max Steps** | 1000 |
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+ | **Optimizer** | Paged AdamW (8-bit) |
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+ | **Logging Steps** | 25 |
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+ | **Evaluation Steps** | 25 |
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+ | **Gradient Checkpointing** | Enabled |