Update README.md

README.md

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-base_model: google/paligemma-3b-pt-224
-library_name: peft
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-
-# Model Card for Model ID
-
-<!-- Provide a quick summary of what the model is/does. -->
-
+# Model Card for Fine-Tuned Paligemma-3B-PT-224 Model
 
+This model is a fine-tuned version of `google/paligemma-3b-pt-224` using the `peft` library. The fine-tuning process involved the `Multimodal-Fatima/VQAv2_sample_train` dataset, focusing on vision-language tasks.
 
 ## Model Details
 
 ### Model Description
 
-<!-- Provide a longer summary of what this model is. -->
-
-
+This model is designed for vision-language tasks, fine-tuned to answer questions based on images and textual prompts. It leverages advanced quantization techniques and specific configurations to optimize performance and efficiency.
 
-- **Developed by:** [More Information Needed]
-- **Funded by [optional]:** [More Information Needed]
-- **Shared by [optional]:** [More Information Needed]
-- **Model type:** [More Information Needed]
-- **Language(s) (NLP):** [More Information Needed]
-- **License:** [More Information Needed]
-- **Finetuned from model [optional]:** [More Information Needed]
+- **Developed by:** [AmmarAbdelhady](https://ammar-abdelhady-ai.github.io/Ammar-Abdelhady-Portfolio/)
+- **Model type:** Vision-Language Model
+- **Language(s) (NLP):** English
+- **Finetuned from model:** `google/paligemma-3b-pt-224`
 
-### Model Sources [optional]
+### Model Sources
 
-<!-- Provide the basic links for the model. -->
-
-- **Repository:** [More Information Needed]
-- **Paper [optional]:** [More Information Needed]
-- **Demo [optional]:** [More Information Needed]
+- **Repository:** [Vision-Language-Model-Fine-Tuning Notebook](https://github.com/Ammar-Abdelhady-ai/Vision-Language-Model-Fine-Tuning/blob/main/fine-tuning-of-paligemma-vision-language-model.ipynb)
+- **Demo:** [Vision-Language-Model-Fine-Tuning](https://github.com/Ammar-Abdelhady-ai/Vision-Language-Model-Fine-Tuning)
 
 ## Uses
 
-<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
-
 ### Direct Use
 
-<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
-
-[More Information Needed]
+This model can be used directly for vision-language tasks, including image captioning and visual question answering.
 
-### Downstream Use [optional]
+### Downstream Use
 
-<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
-
-[More Information Needed]
+The model can be fine-tuned further for specific tasks or integrated into larger systems requiring vision-language capabilities.
 
 ### Out-of-Scope Use
 
-<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
-
-[More Information Needed]
+The model is not suitable for tasks unrelated to vision-language processing, such as purely text-based or purely image-based tasks without multimodal interaction.
 
 ## Bias, Risks, and Limitations
 
-<!-- This section is meant to convey both technical and sociotechnical limitations. -->
-
-[More Information Needed]
+The model may inherit biases from the training dataset, particularly in terms of visual and textual content. It is crucial to evaluate and mitigate these biases in downstream applications.
 
 ### Recommendations
 
-<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
-
-Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
+Users should be aware of the model's limitations and potential biases. It is recommended to perform thorough evaluations on diverse datasets to understand the model's performance across different scenarios.
 
 ## How to Get Started with the Model
 
-Use the code below to get started with the model.
-
-[More Information Needed]
-
-## Training Details
-
-### Training Data
-
-<!-- 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. -->
-
-[More Information Needed]
-
-### Training Procedure
-
-<!-- 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]
-
-[More Information Needed]
-
-
-#### 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 -->
-
-#### Speeds, Sizes, Times [optional]
-
-<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
-
-[More Information Needed]
-
-## Evaluation
-
-<!-- This section describes the evaluation protocols and provides the results. -->
-
-### Testing Data, Factors & Metrics
-
-#### Testing Data
-
-<!-- This should link to a Dataset Card if possible. -->
-
-[More Information Needed]
-
-#### Factors
-
-<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
-
-[More Information Needed]
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-#### Metrics
-
-<!-- These are the evaluation metrics being used, ideally with a description of why. -->
-
-[More Information Needed]
-
-### Results
-
-[More Information Needed]
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-#### Summary
-
-
-
-## Model Examination [optional]
-
-<!-- Relevant interpretability work for the model goes here -->
-
-[More Information Needed]
-
-## 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).
-
-- **Hardware Type:** [More Information Needed]
-- **Hours used:** [More Information Needed]
-- **Cloud Provider:** [More Information Needed]
-- **Compute Region:** [More Information Needed]
-- **Carbon Emitted:** [More Information Needed]
-
-## Technical Specifications [optional]
-
-### Model Architecture and Objective
-
-[More Information Needed]
-
-### Compute Infrastructure
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-[More Information Needed]
-
-#### Hardware
-
-[More Information Needed]
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-#### Software
-
-[More Information Needed]
-
-## Citation [optional]
-
-<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
-
-**BibTeX:**
-
-[More Information Needed]
-
-**APA:**
-
-[More Information Needed]
-
-## Glossary [optional]
-
-<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
-
-[More Information Needed]
-
-## More Information [optional]
-
-[More Information Needed]
-
-## Model Card Authors [optional]
-
-[More Information Needed]
+```python
+from transformers import PaliGemmaForConditionalGeneration, PaliGemmaProcessor
+import torch
+from PIL import Image
+import requests
 
-## Model Card Contact
+model = PaliGemmaForConditionalGeneration.from_pretrained('your_model_path')
+processor = PaliGemmaProcessor.from_pretrained('your_model_path')
 
-[More Information Needed]
-### Framework versions
+prompt = "What is on the flower?"
+image_url = "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/bee.jpg?download=true"
+raw_image = Image.open(requests.get(image_url, stream=True).raw)
+inputs = processor(prompt, raw_image, return_tensors="pt")
+output = model.generate(**inputs, max_new_tokens=20)
 
-- PEFT 0.12.0
+print(processor.decode(output[0], skip_special_tokens=True))