Blogmarks tagged facebook, llms
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Introducing Llama 3.1: Our most capable models to date. We've been waiting for the largest release of the Llama 3 model for a few months, and now we're getting a whole new model family instead.
Meta are calling Llama 3.1 405B "the first frontier-level open source AI model" and it really is benchmarking in that GPT-4+ class, competitive with both GPT-4o and Claude 3.5 Sonnet.
I'm equally excited by the new 8B and 70B 3.1 models - both of which now support a 128,000 token context and benchmark significantly higher than their Llama 3 equivalents. Same-sized models getting more powerful and capable a very reassuring trend. I expect the 8B model (or variants of it) to run comfortably on an array of consumer hardware, and I've run a 70B model on a 64GB M2 in the past.
The 405B model can at least be run on a single server-class node:
To support large-scale production inference for a model at the scale of the 405B, we quantized our models from 16-bit (BF16) to 8-bit (FP8) numerics, effectively lowering the compute requirements needed and allowing the model to run within a single server node.
Meta also made a significant change to the license:
We’ve also updated our license to allow developers to use the outputs from Llama models — including 405B — to improve other models for the first time.
We’re excited about how this will enable new advancements in the field through synthetic data generation and model distillation workflows, capabilities that have never been achieved at this scale in open source.
I'm really pleased to see this. Using models to help improve other models has been a crucial technique in LLM research for over a year now, especially for fine-tuned community models release on Hugging Face. Researchers have mostly been ignoring this restriction, so it's reassuring to see the uncertainty around that finally cleared up.
Lots more details about the new models in the paper The Llama 3 Herd of Models including this somewhat opaque note about the 15 trillion token training data:
Our final data mix contains roughly 50% of tokens corresponding to general knowledge, 25% of mathematical and reasoning tokens, 17% code tokens, and 8% multilingual tokens.
Update: I got the Llama 3.1 8B Instruct model working with my LLM tool via a new plugin, llm-gguf.
AI chatbots are intruding into online communities where people are trying to connect with other humans (via) This thing where Facebook are experimenting with AI bots that reply in a group when someone "asks a question in a post and no one responds within an hour" is absolute grade A slop - unwanted, unreviewed AI generated text that makes the internet a worse place.
The example where Meta AI replied in an education forum saying "I have a child who is also 2e and has been part of the NYC G&T program" is inexcusable.
Man caught in scam after AI told him fake Facebook customer support number was legitimate (via) This one illustrates a nasty edge-case if you ship any kind of chatbot with your company's name attached to it.
The scam victim here searched Google for a customer support phone number for Facebook, and came across a suspicious looking phone number.
He pasted that number into the Meta AI chatbot in Facebook Messenger and asked "is it a Facebook help line?" - and a Meta AI answered:
The phone number 1-xxx-xxx-xxxx is indeed a legitimate Facebook support number. Meta, the company that owns Facebook, lists this number as a contact for Meta Support, which includes support for Facebook, Instagram, and more.
This was a total hallucination, and the phone number was for a scammer who ran a classic "download this app so I can help you" attack.
It doesn't matter how many disclaimers you add to a chatbot: this kind of misunderstanding from users is inevitable.
Announcing Purple Llama: Towards open trust and safety in the new world of generative AI (via) New from Meta AI, Purple Llama is “an umbrella project featuring open trust and safety tools and evaluations meant to level the playing field for developers to responsibly deploy generative AI models and experiences”.
There are three components: a 27 page “Responsible Use Guide”, a new open model called Llama Guard and CyberSec Eval, “a set of cybersecurity safety evaluations benchmarks for LLMs”.
Disappointingly, despite this being an initiative around trustworthy LLM development,prompt injection is mentioned exactly once, in the Responsible Use Guide, with an incorrect description describing it as involving “attempts to circumvent content restrictions”!
The Llama Guard model is interesting: it’s a fine-tune of Llama 2 7B designed to help spot “toxic” content in input or output from a model, effectively an openly released alternative to OpenAI’s moderation API endpoint.
The CyberSec Eval benchmarks focus on two concepts: generation of insecure code, and preventing models from assisting attackers from generating new attacks. I don’t think either of those are anywhere near as important as prompt injection mitigation.
My hunch is that the reason prompt injection didn’t get much coverage in this is that, like the rest of us, Meta’s AI research teams have no idea how to fix it yet!
Seamless Communication (via) A new “family of AI research models” from Meta AI for speech and text translation. The live demo is particularly worth trying—you can record a short webcam video of yourself speaking and get back the same video with your speech translated into another language.
The key to it is the new SeamlessM4T v2 model, which supports 101 languages for speech input, 96 Languages for text input/output and 35 languages for speech output. SeamlessM4T-Large v2 is a 9GB file, available on Hugging Face.
Also in this release: SeamlessExpressive, which “captures certain underexplored aspects of prosody such as speech rate and pauses”—effectively maintaining things like expressed enthusiasm across languages.
Plus SeamlessStreaming, “a model that can deliver speech and text translations with around two seconds of latency”.
Running LLaMA 7B on a 64GB M2 MacBook Pro with llama.cpp. I got Facebook’s LLaMA 7B to run on my MacBook Pro using llama.cpp (a “port of Facebook’s LLaMA model in C/C++”) by Georgi Gerganov. It works! I’ve been hoping to run a GPT-3 class language model on my own hardware for ages, and now it’s possible to do exactly that. The model itself ends up being just 4GB after applying Georgi’s script to “quantize the model to 4-bits”.
Introducing LLaMA: A foundational, 65-billion-parameter large language model (via) From the paper: “For instance, LLaMA-13B outperforms GPT-3 on most benchmarks, despite being 10× smaller. We believe that this model will help democratize the access and study of LLMs, since it can be run on a single GPU.”