Simon Willison’s Weblog

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4 items tagged “cohere”

2024

Command R+ now ranked 6th on the LMSYS Chatbot Arena. The LMSYS Chatbot Arena Leaderboard is one of the most interesting approaches to evaluating LLMs because it captures their ever-elusive “vibes”—it works by users voting on the best responses to prompts from two initially hidden models

Big news today is that Command R+—the brand new open weights model (Creative Commons non-commercial) by Cohere—is now the highest ranked non-proprietary model, in at position six and beating one of the GPT-4s.

(Linking to my screenshot on Mastodon.)

# 9th April 2024, 4:19 pm / ai, generative-ai, llms, cohere, command-r

llm-command-r. Cohere released Command R Plus today—an open weights (non commercial/research only) 104 billion parameter LLM, a big step up from their previous 35 billion Command R model.

Both models are fine-tuned for both tool use and RAG. The commercial API has features to expose this functionality, including a web-search connector which lets the model run web searches as part of answering the prompt and return documents and citations as part of the JSON response.

I released a new plugin for my LLM command line tool this morning adding support for the Command R models.

In addition to the two models it also adds a custom command for running prompts with web search enabled and listing the referenced documents.

# 4th April 2024, 5:38 pm / plugins, projects, ai, generative-ai, llms, llm, cohere, command-r, rag, llm-tool-use

Cohere int8 & binary Embeddings—Scale Your Vector Database to Large Datasets (via) Jo Kristian Bergum told me “The accuracy retention [of binary embedding vectors] is sensitive to whether the model has been using this binarization as part of the loss function.”

Cohere provide an API for embeddings, and last week added support for returning binary vectors specifically tuned in this way.

250M embeddings (Cohere provide a downloadable dataset of 250M embedded documents from Wikipedia) at float32 (4 bytes) is 954GB.

Cohere claim that reducing to 1 bit per dimension knocks that down to 30 GB (954/32) while keeping “90-98% of the original search quality”.

# 26th March 2024, 6:19 am / embeddings, cohere

Aya (via) “A global initiative led by Cohere For AI involving over 3,000 independent researchers across 119 countries. Aya is a state-of-art model and dataset, pushing the boundaries of multilingual AI for 101 languages through open science.”

Both the model and the training data are released under Apache 2. The training data looks particularly interesting: “513 million instances through templating and translating existing datasets across 114 languages”—suggesting the data is mostly automatically generated.

# 13th February 2024, 5:14 pm / open-source, ai, generative-ai, llms, cohere, training-data