Update README.md

README.md

@@ -1,199 +1,176 @@
 ---
 library_name: transformers
-tags: []
+license: mit
+base_model:
+- meta-llama/Llama-3.2-11B-Vision-Instruct
+tags:
+- vision-language
+- product-descriptions
+- e-commerce
+- fine-tuned
+- lora
+- llama
+datasets:
+- philschmid/amazon-product-descriptions-vlm
+language:
+- en
+pipeline_tag: image-text-to-text
 ---
 
-# Model Card for Model ID
-
-<!-- Provide a quick summary of what the model is/does. -->
-
+# Finetuned Llama 3.2 Vision for Product Description Generation
 
+A fine-tuned version of Meta's Llama-3.2-11B-Vision-Instruct model specialized for generating SEO-optimized product descriptions from product images, names, and categories.
 
 ## Model Details
 
 ### Model Description
 
-<!-- Provide a longer summary of what this model is. -->
-
-This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
-
-- **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]
+This model generates concise, SEO-optimized product descriptions for e-commerce applications. Given a product image, name, and category, it produces mobile-friendly descriptions suitable for online marketplaces and product catalogs.
 
-### Model Sources [optional]
+- **Developed by:** Aayush672
+- **Model type:** Vision-Language Model (Multimodal)
+- **Language(s):** English
+- **License:** MIT
+- **Finetuned from model:** meta-llama/Llama-3.2-11B-Vision-Instruct
 
-<!-- Provide the basic links for the model. -->
+### Model Sources
 
-- **Repository:** [More Information Needed]
-- **Paper [optional]:** [More Information Needed]
-- **Demo [optional]:** [More Information Needed]
+- **Repository:** [Aayush672/Finetuned-llama3.2-Vision-Model](https://huggingface.co/Aayush672/Finetuned-llama3.2-Vision-Model)
+- **Base Model:** [meta-llama/Llama-3.2-11B-Vision-Instruct](https://huggingface.co/meta-llama/Llama-3.2-11B-Vision-Instruct)
 
 ## 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]
-
-### Downstream Use [optional]
+The model is designed for generating product descriptions in e-commerce scenarios:
+- Product catalog automation
+- SEO-optimized content generation
+- Mobile-friendly product descriptions
+- Marketplace listing optimization
 
-<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
+### Example Usage
 
-[More Information Needed]
+```python
+from transformers import AutoModelForVision2Seq, AutoProcessor
+from PIL import Image
 
-### Out-of-Scope Use
-
-<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
+model = AutoModelForVision2Seq.from_pretrained("Aayush672/Finetuned-llama3.2-Vision-Model")
+processor = AutoProcessor.from_pretrained("Aayush672/Finetuned-llama3.2-Vision-Model")
 
-[More Information Needed]
+# Prepare your inputs
+image = Image.open("product_image.jpg")
+product_name = "Wireless Bluetooth Headphones"
+category = "Electronics | Audio | Headphones"
 
-## Bias, Risks, and Limitations
+prompt = f"""Create a Short Product description based on the provided ##PRODUCT NAME## and ##CATEGORY## and image.
+Only return description. The description should be SEO optimized and for a better mobile search experience.
 
-<!-- This section is meant to convey both technical and sociotechnical limitations. -->
+##PRODUCT NAME##: {product_name}
+##CATEGORY##: {category}"""
 
-[More Information Needed]
+messages = [{
+    "role": "user",
+    "content": [
+        {"type": "text", "text": prompt},
+        {"type": "image", "image": image}
+    ]
+}]
 
-### Recommendations
+text = processor.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
+inputs = processor(text=text, images=[image], return_tensors="pt")
 
-<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
+output = model.generate(**inputs, max_new_tokens=200, temperature=0.7)
+description = processor.tokenizer.decode(output[0], skip_special_tokens=True)
+```
 
-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.
+### Out-of-Scope Use
 
-[More Information Needed]
+- General conversation or chat applications
+- Complex reasoning tasks
+- Non-commercial product descriptions
+- Content outside e-commerce domain
 
 ## 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]
+The model was fine-tuned on the [philschmid/amazon-product-descriptions-vlm](https://huggingface.co/datasets/philschmid/amazon-product-descriptions-vlm) dataset, which contains Amazon product images with corresponding names, categories, and descriptions.
 
 ### Training Procedure
 
-<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
-
-#### Preprocessing [optional]
-
-[More Information Needed]
-
+#### Fine-tuning Method
+- **Technique:** LoRA (Low-Rank Adaptation) with PEFT
+- **Target modules:** q_proj, v_proj
+- **LoRA rank (r):** 8
+- **LoRA alpha:** 16
+- **LoRA dropout:** 0.05
 
 #### Training Hyperparameters
 
-- **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]
+- **Training regime:** bf16 mixed precision with 4-bit quantization (QLoRA)
+- **Number of epochs:** 1
+- **Batch size:** 8 per device
+- **Gradient accumulation steps:** 4
+- **Learning rate:** 2e-4
+- **Optimizer:** AdamW (torch fused)
+- **LR scheduler:** Constant
+- **Warmup ratio:** 0.03
+- **Max gradient norm:** 0.3
+- **Quantization:** 4-bit with double quantization (nf4)
 
-#### Factors
+#### Hardware & Software
 
-<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
-
-[More Information Needed]
-
-#### Metrics
-
-<!-- These are the evaluation metrics being used, ideally with a description of why. -->
-
-[More Information Needed]
-
-### Results
-
-[More Information Needed]
-
-#### Summary
+- **Quantization:** BitsAndBytesConfig with 4-bit precision
+- **Gradient checkpointing:** Enabled
+- **Memory optimization:** QLoRA technique
+- **Framework:** Transformers, TRL, PEFT
 
+## Bias, Risks, and Limitations
 
+### Limitations
 
-## Model Examination [optional]
+- Trained specifically on Amazon product data, may not generalize well to other e-commerce platforms
+- Limited to English language descriptions
+- Optimized for mobile/SEO format, may not suit all description styles
+- Performance depends on image quality and product visibility
 
-<!-- Relevant interpretability work for the model goes here -->
+### Recommendations
 
-[More Information Needed]
+- Test thoroughly on your specific product categories before production use
+- Consider additional fine-tuning for domain-specific products
+- Implement content moderation for generated descriptions
+- Validate SEO effectiveness for your target keywords
 
 ## 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).
+Training utilized quantized models (4-bit) to reduce computational requirements and carbon footprint compared to full-precision training.
 
-- **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
 
-## Technical Specifications [optional]
+### Model Architecture
 
-### Model Architecture and Objective
-
-[More Information Needed]
+- **Base Architecture:** Llama 3.2 Vision (11B parameters)
+- **Vision Encoder:** Integrated multimodal architecture
+- **Fine-tuning:** LoRA adapters (trainable parameters: ~16M)
+- **Quantization:** 4-bit with double quantization
 
 ### Compute Infrastructure
 
-[More Information Needed]
-
-#### Hardware
-
-[More Information Needed]
-
-#### 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]
+- **Training:** Optimized with gradient checkpointing and mixed precision
+- **Memory:** Reduced via 4-bit quantization and LoRA
+- **Inference:** Supports both quantized and full precision modes
 
-## Model Card Authors [optional]
+## Citation
 
-[More Information Needed]
+```bibtex
+@misc{finetuned-llama32-vision-product,
+  title={Fine-tuned Llama 3.2 Vision for Product Description Generation},
+  author={Aayush672},
+  year={2025},
+  howpublished={\url{https://huggingface.co/Aayush672/Finetuned-llama3.2-Vision-Model}}
+}
+```
 
 ## Model Card Contact
 
-[More Information Needed]
+For questions or issues, please open an issue in the model repository or contact the model author.