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LLMS
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Meta Llama 3.2 AI Investment Middle East Southeast Asia
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We continue to be amazed by the growth of AI globally. 🌎
— SambaNova (@SambaNovaAI) 6 novembre 2024
🎥 @RodrigoLiang talks about our investment in the Middle East & Southeast Asia & the value of Sovereign #AI in those regions. @aramco #GenAI
Build with fast AI inference on @AIatMeta's Llama 3.2 ⤵️We continue to be amazed by the growth of AI globally. @RodrigoLiang talks about our investment in the Middle East & Southeast Asia & the value of Sovereign #AI in those regions. @aramco #GenAI Build with fast AI inference on @AIatMeta
's Llama 3.2 -
HTML Outperforms Plain Text for RAG Knowledge Retrieval
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HtmlRAG HTML is Better Than Plain Text for Modeling Retrieved Knowledge in RAG Systems
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Dataset Diversity Requirements for Language Model Scaling
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Isn't it also related to diversity in your dataset? 2B = not diverse enough to scale, 10B = maybe okay?
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Tencent Releases Hunyuan Large Language Model with Multimodal Capabilities
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Paper: https://
arxiv.org/abs/2411.02265 Model: https://
huggingface.co/tencent/Tencen
t-Hunyuan-Large
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Hunyuan-Large Post-Training Pipeline: SFT and DPO Strategy
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Interestingly, Hunyuan-Large follows a traditional post-training pipeline with SFT and DPO. SFT data is sourced from public sources and evolved to increase complexity Data quality is ensured through 3 stages: rules → 70B critique model → human review If I understood
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Qwen Models: Why Practitioners and AI Teams Follow Their Releases
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Two type of teams carefully follow Qwen releases:
– practitioners cutting through the hype to find the best models to run locally
– teams training state-of-the-art LLMs who knows how hard it is to beat Qwen models (notice how they are often absent from comparisons…) I’m at -
The Tensor Cookbook: Essential Resource for Machine Learning Deep Learning
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The Tensor Cookbook https://
bit.ly/3zx8UQx
#AI #MachineLearning #DeepLearning #LLMs #DataScience -
Hunyuan-Large: 389B Parameter Open-Source MoE Model
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introduc ing Hunyuan-Large, which is currently the largest open-source Transformer-based mixture of experts model, with a total of 389 billion parameters and 52 billion activation parameters, capable of handling up to 256K tokens.
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Tencent Releases Hunyuan-Large Open-Source MoE Model
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Tencent released Hunyuan-Large: An Open-Source MoE Model with 52 Billion Activated Parameters https://
llm.hunyuan.tencent.com https://
github.com/Tencent/Hunyua
n-Large
… https://
huggingface.co/tencent/Hunyua
n-Large/tree/main
… https://
huggingface.co/spaces/tencent
/Hunyuan-Large
… https://
arxiv.org/abs/2411.02265