tahamajs
/

plamma

@@ -5,203 +5,64 @@ license: mit
 language:
 - en
 tags:
-- code
 ---
-# 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:** [Taha Majlesi]
-- **Funded by [optional]:** [More Information Needed]
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-- **Language(s) (NLP):** [More Information Needed]
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-- **Finetuned from model [optional]:** [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 0.14.0

 language:
 - en
 tags:
+- vision-language
+- multimodal
+- fine-tuning
+- generative-modeling
 ---
+# Model Card for PaliGemma Fine-Tuned Model
+This model is a **fine-tuned version of Google’s PaliGemma-3B**, designed for **Vision-Language tasks**, particularly **image-based question answering** and **multimodal reasoning**. The model has been optimized using **Parameter-Efficient Fine-Tuning (PEFT)** methods, such as **LoRA and QLoRA**, to reduce computational costs while maintaining high performance.
 ## Model Details
 ### Model Description
 - **Developed by:** [Taha Majlesi]
+- **Funded by:** [More Information Needed]
+- **Model Type:** Vision-Language Model (VLM)
+- **Language(s):** English
+- **License:** MIT
+- **Finetuned from model:** google/paligemma-3b-pt-224
+### Model Sources
 - **Repository:** [More Information Needed]
+- **Paper (if available):** [More Information Needed]
+- **Demo:** [More Information Needed]
 ## Uses
 ### Direct Use
+- **Visual Question Answering (VQA)**
+- **Multimodal reasoning on image-text pairs**
+- **Image captioning with contextual understanding**
+### Downstream Use
+- Custom **fine-tuning** for **domain-specific multimodal datasets**
+- Integration into **AI assistants for visual understanding**
+- Enhancements in **image-text search systems**
 ### Out-of-Scope Use
+- This model is **not designed** for **pure NLP tasks** without visual inputs.
+- The model may **not perform well** on **low-resource languages**.
+- **Not intended for real-time inference on edge devices** due to model size constraints.
 ## Bias, Risks, and Limitations
+- **Bias:** The model may reflect biases present in the training data, especially in image-text relationships.
+- **Limitations:** Performance may degrade on **unseen, highly abstract, or domain-specific images**.
+- **Risks:** Misinterpretation of **ambiguous images** and **hallucination of non-existent details**.
 ### Recommendations
+- Use **dataset-specific fine-tuning** to mitigate biases.
+- Evaluate performance on **diverse benchmarks** before deployment.
+- Implement **human-in-the-loop validation** in sensitive applications.
 ## How to Get Started with the Model
+To use the fine-tuned model, install the required libraries:
+```sh
+pip install transformers peft accelerate bitsandbytes