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Based on our record, Hugging Face seems to be a lot more popular than BLOOM. While we know about 252 links to Hugging Face, we've tracked only 5 mentions of BLOOM. We are tracking product recommendations and mentions on various public social media platforms and blogs. They can help you identify which product is more popular and what people think of it.
According to https://huggingface.co/bigscience/bloom it has 70 layers, so that's not a perfect split, but even so that should still fit. Source: 11 months ago
It's notable that Hugging Face's BLOOM (https://huggingface.co/bigscience/bloom) might already be compliant (ignoring the 'member states' requirement which I'm sure they could comply with easily enough, it's about disclosing EU member where the model is on the market, so them simply listing all EU countries in a doc somewhere may suffice). Some may be tempted to point at the lower scores of OpenAI, Google and... - Source: Hacker News / 11 months ago
I've been reading a lot of the latest posts on hacker news, but a little lost on where to actually start. I'd like to run something on my local macbook pro to play around and does anyone have recommendation of like a top 5 links of who to follow and what to play with? So far I've seen things like https://huggingface.co/bigscience/bloom. - Source: Hacker News / 11 months ago
They do, and they did. It's called BLOOM and it comes in sizes up to 176B parameters. See: https://huggingface.co/bigscience/bloom. - Source: Hacker News / 12 months ago
ChatGPT is a GPT-3.5 model according to OpenAI Docs. So it has 175 billion parameters. For the architecture, people said BLOOM is likely the nearest alternative to GPT-3/3.5 models. Source: about 1 year ago
HuggingFaceEmbeddings is a function that we use for converting our documents to vector which is called embedding, you can use any embedding model from huggingface, it will load the model on your local computer and create embeddings(you can use external api/service to create embeddings), then we just pass this to context and create index and store them into folder so we can reuse them and don't need to recalculate it. - Source: dev.to / 29 days ago
The only requirement for this tutorial is to have an Hugging Face account. In order to get it:. - Source: dev.to / about 1 month ago
Finally, you'll need to download a compatible language model and copy it to the ~/llama.cpp/models directory. Head over to Hugging Face and search for a GGUF-formatted model that fits within your device's available RAM. I'd recommend starting with TinyLlama-1.1B. - Source: dev.to / about 1 month ago
At this point, probably everyone has heard about OpenAI, GPT-4, Claude or any of the popular Large Language Models (LLMs). However, using these LLMs in a production environment can be expensive or nondeterministic regarding its results. I guess that is the downside of being good at everything; you could be better at performing one specific task. This is where HuggingFace can utilized. HuggingFace provides... - Source: dev.to / about 1 month ago
New models can be added by downloading GGUF format models to the models sub-directory from https://huggingface.co/. - Source: dev.to / about 2 months ago
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