IBM has officially launched its next-generation open-source large language model, Granite3.1, aiming to secure a leading position in the enterprise AI sector. This series of models features an extended context length of 128K, embedding models, built-in hallucination detection capabilities, and significant performance improvements. IBM claims that the Granite8B Instruct model outperforms its open-source competitors of similar scale, including Meta's Llama3.1, Qwen2.5, and Google's Gemma2.

Zhiyuan Research Institute has launched a dataset called Infinity-Instruct, which comprises millions of instruction tuning data aimed at enhancing the performance of language models in dialogues and other areas. Recently, Infinity-Instruct has completed a new round of iterations, including the Infinity-Instruct-7M foundational instruction dataset and the Infinity-Instruct-Gen dialogue instruction dataset.

Last night, Meta announced the open-source release of its latest large language model, Llama3.1 405B. This significant announcement marks the culmination of a year-long meticulous preparation, from project planning to final review, as the Llama3 series models are finally unveiled to the public. Llama3.1 405B is a multilingual tool usage model with 128 billion parameters. After pre-training with an 8K context length, the model underwent further continuous training with a 128K context length. Acco