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Convert to ONNX
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Convert a Hugging Face model to ONNX format
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Amin24
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How to save image embeddings in vector DB?
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#63 opened 29 days ago
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Vision Language Model Alignment in TRL ⚡️
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Exciting breakthrough in E-commerce Recommendation Systems!
Just read a fascinating paper from @eBay 's research team on "LLM-PKG" - a novel approach that combines Large Language Models with Product Knowledge Graphs for explainable recommendations.
Here's what makes it groundbreaking:
>> Technical Architecture
- The system uses a two-module approach: offline construction and online serving
- LLM generates initial product relationships and rationales, which are transformed into RDF triplets (Subject, Predicate, Object) to build the knowledge graph
- The system employs rigorous validation using LLM-based scoring (1-10 scale) to evaluate recommendation quality and prune low-quality nodes (score < 6)
>> Under the Hood
- Product mapping uses BERT embeddings and KNN indexing for semantic matching between LLM recommendations and actual inventory
- The system caches graph triplets in key-value databases for lightning-fast retrieval during online serving
- Supports both item-centric and user-centric recommendation scenarios
>> Real-World Impact
The A/B testing results are impressive:
- 5.19% increase in clicks
- 7.59% boost in transactions
- 8.56% growth in Gross Merchandise Bought
- 10.84% increase in ad revenue
This is a game-changer for e-commerce platforms looking to provide transparent, explainable recommendations while maintaining high performance at scale.
Just read a fascinating paper from @eBay 's research team on "LLM-PKG" - a novel approach that combines Large Language Models with Product Knowledge Graphs for explainable recommendations.
Here's what makes it groundbreaking:
>> Technical Architecture
- The system uses a two-module approach: offline construction and online serving
- LLM generates initial product relationships and rationales, which are transformed into RDF triplets (Subject, Predicate, Object) to build the knowledge graph
- The system employs rigorous validation using LLM-based scoring (1-10 scale) to evaluate recommendation quality and prune low-quality nodes (score < 6)
>> Under the Hood
- Product mapping uses BERT embeddings and KNN indexing for semantic matching between LLM recommendations and actual inventory
- The system caches graph triplets in key-value databases for lightning-fast retrieval during online serving
- Supports both item-centric and user-centric recommendation scenarios
>> Real-World Impact
The A/B testing results are impressive:
- 5.19% increase in clicks
- 7.59% boost in transactions
- 8.56% growth in Gross Merchandise Bought
- 10.84% increase in ad revenue
This is a game-changer for e-commerce platforms looking to provide transparent, explainable recommendations while maintaining high performance at scale.
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Google's SigLIP is another alternative to openai's CLIP, and it just got merged to 🤗transformers and it's super easy to use!
To celebrate this, I have created a repository including notebooks and bunch of Spaces on various SigLIP based projects 🥳
Search for art 👉 merve/draw_to_search_art
Compare SigLIP with CLIP 👉 merve/compare_clip_siglip
How does SigLIP work?
SigLIP an vision-text pre-training technique based on contrastive learning. It jointly trains an image encoder and text encoder such that the dot product of embeddings are most similar for the appropriate text-image pairs
The image below is taken from CLIP, where this contrastive pre-training takes place with softmax, but SigLIP replaces softmax with sigmoid. 📎
Highlights from the paper on why you should use it ✨
🖼️📝 Authors used medium sized B/16 ViT for image encoder and B-sized transformer for text encoder
😍 More performant than CLIP on zero-shot
🗣️ Authors trained a multilingual model too!
⚡️ Super efficient, sigmoid is enabling up to 1M items per batch, but the authors chose 32k because the performance saturates after that
It's super easy to use thanks to transformers 👇
For all the SigLIP notebooks on similarity search and indexing, you can check this [repository](https://github.com/merveenoyan/siglip) out. 🤗
To celebrate this, I have created a repository including notebooks and bunch of Spaces on various SigLIP based projects 🥳
Search for art 👉 merve/draw_to_search_art
Compare SigLIP with CLIP 👉 merve/compare_clip_siglip
How does SigLIP work?
SigLIP an vision-text pre-training technique based on contrastive learning. It jointly trains an image encoder and text encoder such that the dot product of embeddings are most similar for the appropriate text-image pairs
The image below is taken from CLIP, where this contrastive pre-training takes place with softmax, but SigLIP replaces softmax with sigmoid. 📎
Highlights from the paper on why you should use it ✨
🖼️📝 Authors used medium sized B/16 ViT for image encoder and B-sized transformer for text encoder
😍 More performant than CLIP on zero-shot
🗣️ Authors trained a multilingual model too!
⚡️ Super efficient, sigmoid is enabling up to 1M items per batch, but the authors chose 32k because the performance saturates after that
It's super easy to use thanks to transformers 👇
from transformers import pipeline
from PIL import Image
import requests
# load pipe
image_classifier = pipeline(task="zero-shot-image-classification", model="google/siglip-base-patch16-256-i18n")
# load image
url = 'http://images.cocodataset.org/val2017/000000039769.jpg'
image = Image.open(requests.get(url, stream=True).raw)
# inference
outputs = image_classifier(image, candidate_labels=["2 cats", "a plane", "a remote"])
outputs = [{"score": round(output["score"], 4), "label": output["label"] } for output in outputs]
print(outputs)For all the SigLIP notebooks on similarity search and indexing, you can check this [repository](https://github.com/merveenoyan/siglip) out. 🤗
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