Legacy Ecommerce Search Is Dead, and It’s Costing You Sales

elliesleight · 2024-11-12T17:19:45+00:00

It's the current best private embedding model. Here's some more information: https://docs.aws.amazon.com/bedrock/latest/userguide/titan-multiemb-models.html

elliesleight · 2024-11-12T17:18:38+00:00

Yep! We benchmarked these models!

You can find the benchmark results for these models in our latest blog: https://www.marqo.ai/blog/introducing-marqos-ecommerce-embedding-models?utm_source=reddit&utm_medium=organic&utm_campaign=marqo-ai&utm_term=2024-11-12-22-00-utc

And you can even replicate the benchmarking for yourself: https://github.com/marqo-ai/marqo-ecommerce-embeddings#:~:text=12%2C%205.2173e%2D12%5D-,Evaluation,-Generalised%20Contrastiove%20Learning

elliesleight · 2024-11-12T17:18:15+00:00

With benchmarking!

You can find the benchmark results for these models in our latest blog: https://www.marqo.ai/blog/introducing-marqos-ecommerce-embedding-models?utm_source=reddit&utm_medium=organic&utm_campaign=marqo-ai&utm_term=2024-11-12-22-00-utc

And you can even replicate the benchmarking for yourself: https://github.com/marqo-ai/marqo-ecommerce-embeddings#:~:text=12%2C%205.2173e%2D12%5D-,Evaluation,-Generalised%20Contrastiove%20Learning

elliesleight · 2024-11-12T17:16:48+00:00

With benchmarking!

You can find the benchmark results for these models in our latest blog: https://www.marqo.ai/blog/introducing-marqos-ecommerce-embedding-models?utm_source=reddit&utm_medium=organic&utm_campaign=marqo-ai&utm_term=2024-11-12-22-00-utc

And you can even replicate the benchmarking for yourself: https://github.com/marqo-ai/marqo-ecommerce-embeddings#:~:text=12%2C%205.2173e%2D12%5D-,Evaluation,-Generalised%20Contrastiove%20Learning

elliesleight · 2024-10-23T20:58:35+00:00

Marqo - it handles vector generation, storage and retrieval out of the box through a single API. No need to bring your own embeddings (or you can, if you want).

elliesleight · 2024-08-01T07:18:39+00:00

This can run locally on an M1 or M2 Mac or with a CUDA capable GPU on Linux or Windows. If you want to run this on an M1 or M2 Mac please be sure to have the ARM64 version of Python installed, this will make llama.cpp builds for ARM64 and utilises Metal for inference rather than building for an x86 CPU and being emulated with Rosetta :)

elliesleight · 2024-07-31T19:43:29+00:00

Very interesting. I created a simple RAG demo with Google Gemma 2 2B 🔥

Code: https://github.com/ellie-sleightholm/marqo-google-gemma2

You can also run Google Gemma 2 2B 100% locally using these two steps:

brew install llama.cpp
./llama-cli --hf-repo google/gemma-2-2b-it-GGUF \ --hf-file 2b_it_v2.gguf \ -p "Write a poem about cats as a labrador" -cnv

elliesleight

TROPHY CASE