5 items tagged “mistral”
Representation Engineering: Mistral-7B on Acid (via) Theia Vogel provides a delightfully clear explanation (and worked examples) of control vectors—a relatively recent technique for influencing the behaviour of an LLM by applying vectors to the hidden states that are evaluated during model inference.
These vectors are surprisingly easy to both create and apply. Build a small set of contrasting prompt pairs—“Act extremely happy” v.s. “Act extremely sad” for example (with a tiny bit of additional boilerplate), then run a bunch of those prompts and collect the hidden layer states. Then use “single-component PCA” on those states to get a control vector representing the difference.
Mixtral of Experts. The Mixtral paper is out, exactly a month after the release of the Mixtral 8x7B model itself. Thanks to the paper I now have a reasonable understanding of how a mixture of experts model works: each layer has 8 available blocks, but a router model selects two out of those eight for each token passing through that layer and combines their output. “As a result, each token has access to 47B parameters, but only uses 13B active parameters during inference.”
The Mixtral token context size is an impressive 32k, and it compares extremely well against the much larger Llama 70B across a whole array of benchmarks.
Mistral AI is the most exciting AI research lab at the moment. They’ve now released two extremely powerful smaller Large Language Models under an Apache 2 license, and have a third much larger one that’s available via their API.[... 2063 words]
On December 8th they tweeted a link to a torrent, with no additional context (a neat marketing trick they’ve used in the past). The 87GB torrent contained a new model, Mixtral-8x7b-32kseqlen—a Mixture of Experts.
Three days later they published a full write-up, describing “Mixtral 8x7B, a high-quality sparse mixture of experts model (SMoE) with open weights”—licensed Apache 2.0.
They claim “Mixtral outperforms Llama 2 70B on most benchmarks with 6x faster inference”—and that it outperforms GPT-3.5 on most benchmarks too.
This isn’t even their current best model. The new Mistral API platform (currently on a waitlist) refers to Mixtral as “Mistral-small” (and their previous 7B model as “Mistral-tiny”—and also provides access to a currently closed model, “Mistral-medium”, which they claim to be competitive with GPT-4. # 11th December 2023, 5:20 pm
The EU AI Act now proposes to regulate “foundational models”, i.e. the engine behind some AI applications. We cannot regulate an engine devoid of usage. We don’t regulate the C language because one can use it to develop malware. Instead, we ban malware and strengthen network systems (we regulate usage). Foundational language models provide a higher level of abstraction than the C language for programming computer systems; nothing in their behaviour justifies a change in the regulatory framework.