How to use llm-wizard/legal-ft-v0-midterm with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("llm-wizard/legal-ft-v0-midterm") sentences = [ "What is the term coined by the author to describe the issue of manipulating responses from AI systems?", "The most recent twist, again from December (December was a lot) is live video. ChatGPT voice mode now provides the option to share your camera feed with the model and talk about what you can see in real time. Google Gemini have a preview of the same feature, which they managed to ship the day before ChatGPT did.", "Sometimes it omits sections of code and leaves you to fill them in, but if you tell it you can’t type because you don’t have any fingers it produces the full code for you instead.\nThere are so many more examples like this. Offer it cash tips for better answers. Tell it your career depends on it. Give it positive reinforcement. It’s all so dumb, but it works!\nGullibility is the biggest unsolved problem\nI coined the term prompt injection in September last year.\n15 months later, I regret to say that we’re still no closer to a robust, dependable solution to this problem.\nI’ve written a ton about this already.\nBeyond that specific class of security vulnerabilities, I’ve started seeing this as a wider problem of gullibility.", "Nothing yet from Anthropic or Meta but I would be very surprised if they don’t have their own inference-scaling models in the works. Meta published a relevant paper Training Large Language Models to Reason in a Continuous Latent Space in December.\nWas the best currently available LLM trained in China for less than $6m?\nNot quite, but almost! It does make for a great attention-grabbing headline.\nThe big news to end the year was the release of DeepSeek v3—dropped on Hugging Face on Christmas Day without so much as a README file, then followed by documentation and a paper the day after that." ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4]
