genloop
/

llama-3.2-11b-instruct-amazon-product-description-merged-model

@@ -1,199 +1,92 @@
 ---
 library_name: transformers
-tags: []
 ---
-# Model Card for Model ID
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-This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
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 ---
 library_name: transformers
+metrics:
+- meteor
+base_model:
+- meta-llama/Llama-3.2-11B-Vision-Instruct
 ---
+# Model Card
+- **Developed by:** [Genloop.ai](https://huggingface.co/genloop)
+- **Funded by:** [Genloop Labs, Inc.](https://genloop.ai/)
+- **Model type:** Vision Language Model (VLM)
+- **Finetuned from model:** [Meta Llama 3.2 11B Vision Instruct](https://huggingface.co/meta-llama/Llama-3.2-11B-Vision-Instruct)
+- **Usage:** This model is intended for product cataloging, i.e. generating product descriptions from images
 ## How to Get Started with the Model
+Make sure to update your transformers installation via `pip install --upgrade transformers`.
+```python
+import requests
+import torch
+from PIL import Image
+from transformers import MllamaForConditionalGeneration, AutoProcessor
+model_id = "meta-llama/Llama-3.2-11B-Vision-Instruct"
+model = MllamaForConditionalGeneration.from_pretrained(
+    model_id,
+    torch_dtype=torch.bfloat16,
+    device_map="auto",
+)
+processor = AutoProcessor.from_pretrained(model_id)
+url = "insert_your_image_link_here"
+image = Image.open(requests.get(url, stream=True).raw)
+user_prompt= """Create a SHORT Product description based on the provided a given ##PRODUCT NAME## and a ##CATEGORY## and an image of the product.
+Only return description. The description should be SEO optimized and for a better mobile search experience.
+##PRODUCT NAME##: {product_name}
+##CATEGORY##: {prod_category}"""
+product_name = "insert_your_product_name_here"
+product_category = "insert_your_product_category_here"
+messages = [
+    {"role": "user", "content": [
+        {"type": "image"},
+        {"type": "text", "text": user_prompt.format(product_name = product_name, product_category = product_category)}
+    ]}
+]
+input_text = processor.apply_chat_template(messages, add_generation_prompt=True)
+inputs = processor(
+    image,
+    input_text,
+    add_special_tokens=False,
+    return_tensors="pt"
+).to(model.device)
+output = model.generate(**inputs, max_new_tokens=30)
+print(processor.decode(output[0]))
+```
 ## Training Details
+This model has been finetuned on the [Amazon-Product-Descriptions](https://huggingface.co/datasets/philschmid/amazon-product-descriptions-vlm) dataset. The reference descriptions were generated using Gemini Flash.
+### Training hyperparameters
+The following hyperparameters were used during training:
+- learning_rate: 0.0002
+- train_batch_size: 2
+- seed: 3407
+- gradient_accumulation_steps: 4
+- gradient_checkpointing: True
+- total_train_batch_size: 8
+- lr_scheduler_type: linear
+- num_epochs: 3
+#### Results
+| MODEL                             | FINETUNED OR NOT       | INFERENCE LATENCY | METEOR Score |
+|-----------------------------------|------------------------|-------------------|--------------|
+| Llama-3.2-11B-Vision-Instruct     | Not Finetuned          | 1.68              | 0.38         |
+| Llama-3.2-11B-Vision-Instruct     | Finetuned              | 1.68              | 0.53         |