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Best Open Source LLM:Tencent Hunyuan-Large: The 389 Billion Parameter Model Outperforming Llama 3 and DeepSeek V2

Huge announcement from Meta. Welcome Llama 3.1🔥 This is all you need to know about it: The new models: - The Meta Llama 3.1 family of multilingual large language models (LLMs) is a collection of pre-trained and instruction-tuned generative models

Databricks' new DBRX rivals best open source LLMs

🚀 Breaking News: Meta Unleashes Its Biggest Open-Source AI Model Yet – Llama 3.1 with 405 Billion Parameters!Meta has taken a giant leap forward in AI development by releasing Llama 3.1, their most powerful open-source AI model to date, boasting an

Hello Everyone, Do you think that , Meta's plan to dominate AI sector is kind of different : Firstly make it available for free and if it is totally trained then make business from it. 1. Meta doesn't public their things easily. 2. Meta avails the

Meta has introduced the Llama 3.1 series of large language models (LLMs), featuring a top-tier model with 405 billion parameters, as well as smaller variants with 70 billion and 8 billion parameters. Meta claims that Llama 3.1 matches the performance

Meta's Llama 3 model scales open language models, boasting improved performance and various sizes. With a focus on addressing fatigue, it utilizes diverse training methods and achieves impressive results, strengthening the open LLM ecosystem

Meta has released Llama 3.1, the first frontier-level open source AI model, with features such as expanded context length to 128K, support for eight languages, and the introduction of Llama 3.1 405B. The model offers flexibility and control, enabli

भारत की कम्पनियों ने Llama का लाभ क्यों नहीं उठा पाया, जबकि चीन समेत तमाम देशों की कम्पनियों ने अमेरिका के open source LLM का लाभ उठा रहे हैं ? क्या हमारे Engineer अयोग्य हैं ? Why Indian companies could not take advantage of Llama, while companies

Back To Medial ♥️, After Some Tough time 4-5 da ys, Let's Discuss today , Meta's Llama 3 Model ! Features: • Llama 3 models come in two sizes: • 8 billion parameters and 70 billion parameters Each size has base (pre-trained) and instruction-tuned