All the responses get saved in the cfahlgren1/react-code-instructions dataset. Hopefully we can build one of the biggest, highest quality frontend datasets on the hub 💪
reacted to csabakecskemeti's
post with 🚀😎🤯about 15 hours ago
The deepseek-ai/DeepSeek-V3-Base model has featured today on CNBC tech news. The whale made a splash by using FP8 and shrink the cost of training significantly!
That didn't take long! Nomic AI has finetuned the new ModernBERT-base encoder model into a strong embedding model for search, classification, clustering and more!
Details: 🤖 Based on ModernBERT-base with 149M parameters. 📊 Outperforms both nomic-embed-text-v1 and nomic-embed-text-v1.5 on MTEB! 🏎️ Immediate FA2 and unpacking support for super efficient inference. 🪆 Trained with Matryoshka support, i.e. 2 valid output dimensionalities: 768 and 256. ➡️ Maximum sequence length of 8192 tokens! 2️⃣ Trained in 2 stages: unsupervised contrastive data -> high quality labeled datasets. ➕ Integrated in Sentence Transformers, Transformers, LangChain, LlamaIndex, Haystack, etc. 🏛️ Apache 2.0 licensed: fully commercially permissible
Excited to share insights from Walmart's groundbreaking semantic search system that revolutionizes e-commerce product discovery!
The team at Walmart Global Technology(the team that I am a part of 😬) has developed a hybrid retrieval system that combines traditional inverted index search with neural embedding-based search to tackle the challenging problem of tail queries in e-commerce.
Key Technical Highlights:
• The system uses a two-tower BERT architecture where one tower processes queries and another processes product information, generating dense vector representations for semantic matching.
• Product information is enriched by combining titles with key attributes like category, brand, color, and gender using special prefix tokens to help the model distinguish different attribute types.
• The neural model leverages DistilBERT with 6 layers and projects the 768-dimensional embeddings down to 256 dimensions using a linear layer, achieving optimal performance while reducing storage and computation costs.
• To improve model training, they implemented innovative negative sampling techniques combining product category matching and token overlap filtering to identify challenging negative examples.
Production Implementation Details:
• The system uses a managed ANN (Approximate Nearest Neighbor) service to enable fast retrieval, achieving 99% recall@20 with just 13ms latency.
• Query embeddings are cached with preset TTL (Time-To-Live) to reduce latency and costs in production.
• The model is exported to ONNX format and served in Java, with custom optimizations like fixed input shapes and GPU acceleration using NVIDIA T4 processors.
Results: The system showed significant improvements in both offline metrics and live experiments, with: - +2.84% improvement in NDCG@10 for human evaluation - +0.54% lift in Add-to-Cart rates in live A/B testing
This is a fantastic example of how modern NLP techniques can be successfully deployed at scale to solve real-world e-
1 reply
·
reacted to DamarJati's
post with 🚀➕❤️about 15 hours ago
🎯Triangulum is a collection of pretrained and instruction-tuned generative models, designed for multilingual applications. These models are trained using synthetic datasets based on long chains of thought, enabling them to perform complex reasoning tasks effectively.
Happy New Year, Huggingface community! In 2025, I'll continue my quantization (and some fine-tuning) efforts to support the open-source AI and Make knowledge free for everyone.
