sentence-transformers How to use Mdean77/snow_ft_2025 with sentence-transformers:
from sentence_transformers import SentenceTransformer
model = SentenceTransformer("Mdean77/snow_ft_2025")
sentences = [
"What are some potential negative uses of Large Language Models as described in the context?",
"I think this means that, as individual users, we don’t need to feel any guilt at all for the energy consumed by the vast majority of our prompts. The impact is likely neglible compared to driving a car down the street or maybe even watching a video on YouTube.\nLikewise, training. DeepSeek v3 training for less than $6m is a fantastic sign that training costs can and should continue to drop.\nFor less efficient models I find it useful to compare their energy usage to commercial flights. The largest Llama 3 model cost about the same as a single digit number of fully loaded passenger flights from New York to London. That’s certainly not nothing, but once trained that model can be used by millions of people at no extra training cost.",
"Here’s the sequel to this post: Things we learned about LLMs in 2024.\nLarge Language Models\nIn the past 24-36 months, our species has discovered that you can take a GIANT corpus of text, run it through a pile of GPUs, and use it to create a fascinating new kind of software.\nLLMs can do a lot of things. They can answer questions, summarize documents, translate from one language to another, extract information and even write surprisingly competent code.\nThey can also help you cheat at your homework, generate unlimited streams of fake content and be used for all manner of nefarious purposes.",
"There’s now a fascinating ecosystem of people training their own models on top of these foundations, publishing those models, building fine-tuning datasets and sharing those too.\nThe Hugging Face Open LLM Leaderboard is one place that tracks these. I can’t even attempt to count them, and any count would be out-of-date within a few hours.\nThe best overall openly licensed LLM at any time is rarely a foundation model: instead, it’s whichever fine-tuned community model has most recently discovered the best combination of fine-tuning data.\nThis is a huge advantage for open over closed models: the closed, hosted models don’t have thousands of researchers and hobbyists around the world collaborating and competing to improve them."
]
embeddings = model.encode(sentences)
similarities = model.similarity(embeddings, embeddings)
print(similarities.shape)
# [4, 4] 
