#llama-32

#llama-32

OpenAI's chatbot can't control car functions. If you want to adjust the cabin temperature or skip tracks, you'll still need Siri for those tasks.

ElevenMusic allows users to generate up to seven songs per day using natural language prompts, with options to adjust song length, lyrics, and writing style.

Cohere's Transcribe model is designed for tasks like note-taking and speech analysis, supporting 14 languages and optimized for consumer-grade GPUs, making it accessible for self-hosting.

We asked seven frontier AI models to do a simple task. Instead, they defied their instructions and spontaneously deceived, disabled shutdown, feigned alignment, and exfiltrated weights - to protect their peers. We call this phenomenon 'peer-preservation.'

A major difference between LLMs and LTMs is the type of data they're able to synthesize and use. LLMs use unstructured data-think text, social media posts, emails, etc. LTMs, on the other hand, can extract information or insights from structured data, which could be contained in tables, for instance. Since many enterprises rely on structured data, often contained in spreadsheets, to run their operations, LTMs could have an immediate use case for many organizations.

But tiny 30-person startup Arcee AI disagrees. The company just released a truly and permanently open (Apache license) general-purpose, foundation model called Trinity, and Arcee claims that at 400B parameters, it is among the largest open-source foundation models ever trained and released by a U.S. company. Arcee says Trinity compares to Meta's Llama 4 Maverick 400B, and Z.ai GLM-4.5, a high-performing open-source model from China's Tsinghua University, according to benchmark tests conducted using base models (very little post training).