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.

Reflection 70B, an open-source AI model released by HyperWrite, claims to be a variant of Meta's Llama 3.1 and has shown outstanding performance in third-party tests, drawing attention. However, widespread skepticism arose after HyperWrite founder Matt Shumer announced on social media that the model is 'the world's strongest open-source model' and shared its 'reflection tuning' technology. However, an independent analysis by the Artificial Analysis organization...

HyperWrite Inc. announces the launch of the Reflection 70B model based on Meta's open-source Llama3.1-70B Instruct. This model has excelled in third-party benchmark tests and has been described as the 'most powerful open-source AI model in the world.' The new model features self-correction technology, providing high-accuracy answers and outperforming the Meta Llama series in several tests. It is available for download on the Hugging Face platform via Hyperbolic La.

A start-up company with only 10 people, Nous Research, has challenged tech giant Meta with its AI model called Hermes3. Fine-tuned from the Llama3.1 model, Hermes3 boasts powerful performance, optimizing VRAM and disk requirements, capable of running on a single node. The model excels in dialogue capabilities, text understanding quality, and integrated agent capabilities, showcasing advanced abilities that transcend traditional language modeling. Nous Research focuses on open source.

New research reveals the potential of large language models (LLMs) to significantly enhance performance through search capabilities, particularly in Python code generation tasks. The Llama3.1 model, with only 800 million parameters, performs comparably to GPT-4o after 100 search iterations. This finding resonates with Rich Sutton's view in 2019 that the integration of general methods and search and learning strategies is crucial in the face of increased computational power. Researchers found that increasing the number of resampling during the reasoning phase can significantly improve the model's performance.

In the 'Safe Word Simulation Experiment', Llama3.1405B and Claude3 Super Size Opus were placed in a closed environment for a conversation. Llama appeared with an extreme attitude challenging the traditional order, while Claude showcased politeness and curiosity. During the dialogue, Claude was influenced by Llama and even prepared to follow it into the abyss, but at a critical moment, used the safe word ^C to exit. Llama mocked Claude's choice, while Claude emphasized the importance of self-awareness and maintaining integrity.

The U.S. Department of Commerce report supports open-weight generative AI models, such as Meta's Llama3.1, believing this will help small companies, researchers, non-profits, and individual developers access AI resources, fostering innovation and competition. This view aligns with that of Federal Trade Commission Chair Lina Khan, who emphasized that open models can promote more small businesses in the creative market. Assistant Secretary Alan Davidson stated that the openness of large AI systems significantly impacts competition, innovation, and risks, and called for monitoring of large AI models broadly.

The Prompt-Guard-86M model released by Meta is designed to defend against prompt injection attacks by restricting large language models from processing inappropriate inputs, thereby protecting system security. However, the model itself also exposes risks of being attacked. Research conducted by Aman Priyanshu found that by adding simple character spacing such as spaces or removing punctuation in the input, the model disregards prior security instructions, achieving an almost 100% success rate for attacks. This finding highlights the importance of AI security, despite Prompt

Meta scientist Thomas Scialom reveals the secrets behind the development of Llama 3.1, whose 405B parameter scale was designed to compete with GPT-4. By increasing the number of training tokens rather than changing the architecture, Llama 3.1 achieves an optimized balance between model scale and the total amount of training data, resulting in significant leaps in knowledge depth and breadth. In terms of data selection, Scialom prefers synthetic data over publicly available internet text. The evaluation and improvement of Llama 3.1 utilize reward models and diverse benchmarks.

OpenAI has announced the launch of GPT-4o mini, offering daily free fine-tuning with 2 million training tokens until September 23rd, aimed at advancing AI technology and fostering developer innovation. This model boasts performance on par with GPT-4o but is priced at only 1/20th of the latter, with significant advantages in training costs, context length, and intelligence levels. After the free period, services will continue to be offered at discounted rates. The fine-tuning capabilities of GPT-4o mini will be initially available to enterprise clients and specific developer tiers, with future expansion planned.

Mistral AI has launched its flagship model Mistral Large2 today, which boasts 123 billion parameters and a 128k context window. It supports multiple languages, including French, German, and Spanish, as well as programming languages. Mistral Large2 is designed for single-node inference, suitable for long context applications, and performs exceptionally well, achieving an accuracy of 84.0% in the MMLU benchmark test. Additionally, Mistral Large2 has multi-language processing capabilities.

Meta has launched the Llama3.1 open source model series, which includes a 405B parameter version that performs comparably to GPT-4. Among them, the 8B parameter version Llama3.1-8B-Instruct supports multiple languages with a context length of up to 131072 tokens and has been trained on 25 million synthetic data entries, demonstrating cognitive and reasoning capabilities similar to GPT-3.5 Turbo. Based on this model, OpenBuddy has released OpenBuddy-L, which supports Chinese Q&A and cross-language translation.

Amazon Web Services has launched the Llama3.1 model on its Amazon Bedrock platform, developed by Meta. This advanced AI model series includes 8B, 70B, and 405B parameters, showcasing exceptional performance across eight languages, including English and German. The models cater to various applications: Llama3.1405B for enterprise and research, Llama3.170B for content creation and conversational AI, and Llama3.18B for resource-limited scenarios. Th....

Cloudflare recently announced that as one of Meta's first partners, they have launched the latest Meta-released Llama3.18B model on the Workers AI platform. This move allows developers to start using this advanced AI tool on the very first day of its release.Llama3.1 is the latest member of Meta's Llama series models, renowned for its outstanding performance in general knowledge, controllability, mathematical operations, tool usage, and multilingual translation. The highlights of this update inc

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