How to use JKilpatrick/legal-ft-3 with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("JKilpatrick/legal-ft-3") sentences = [ "What is the mlx-vlm project by Prince Canuma known for in relation to Apple Silicon?", "These abilities are just a few weeks old at this point, and I don’t think their impact has been fully felt yet. If you haven’t tried them out yet you really should.\nBoth Gemini and OpenAI offer API access to these features as well. OpenAI started with a WebSocket API that was quite challenging to use, but in December they announced a new WebRTC API which is much easier to get started with. Building a web app that a user can talk to via voice is easy now!\nPrompt driven app generation is a commodity already\nThis was possible with GPT-4 in 2023, but the value it provides became evident in 2024.", "Prince Canuma’s excellent, fast moving mlx-vlm project brings vision LLMs to Apple Silicon as well. I used that recently to run Qwen’s QvQ.\nWhile MLX is a game changer, Apple’s own “Apple Intelligence” features have mostly been a disappointment. I wrote about their initial announcement in June, and I was optimistic that Apple had focused hard on the subset of LLM applications that preserve user privacy and minimize the chance of users getting mislead by confusing features.", "Things we learned about LLMs in 2024\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nSimon Willison’s Weblog\nSubscribe\n\n\n\n\n\n\nThings we learned about LLMs in 2024\n31st December 2024\nA lot has happened in the world of Large Language Models over the course of 2024. Here’s a review of things we figured out about the field in the past twelve months, plus my attempt at identifying key themes and pivotal moments.\nThis is a sequel to my review of 2023.\nIn this article:" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4]
