Les LLM peuvent traiter n'importe quel data non structurée et en sortir des insights. Plus besoin de contraintes à structurer de belles bases de données propres.
MACHINE LEARNING
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Model Configuration and Next Predict Layers Parameter Discussion
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yeah, config.json + modeling looks pretty much the same. Still no reference of “num_nextn_predict_layers” in modeling
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Comprehensive Data Scientist Handbook: Essential Learning Resources
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GitHub – andresvourakis/data-scientist-handbook: This is a repo with links to everything you'd ever want to learn about data science https://
bit.ly/3N6nZvo
#AI #MachineLearning #DeepLearning #LLMs #DataScience -
Understanding AI’s Impact on the Workforce
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Understanding AI’s impact on the workforce
#AI #AIio #BigData #ML #NLU #Futureofwork @TunstallAsc @StrategyFintech @TamaraMcCleary @TerenceLeungSF @psb_dc @thomaspower @vinod1975 @ylecun -
2024 Achievements: Master’s Degree, NeurIPS Paper, Research Associate
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2024 wrapped – Completed master of science in engineering AI at Carnegie Mellon
– Contributed to two remarkable papers in multimodal evals & multilingual multimodal LLMs – 1 accepted to NeurIPS 2024(D&B track) and other under review
– Started a research visit/associate at -
DeepSeek V3 now on HuggingFace
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Besides that, DeepSeek V3 is now available on HuggingFace https://
huggingface.co/deepseek-ai/De
epSeek-V3-Base
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Model Checkpoint Configuration Values Analysis
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digging into config.json of both the model checkpoint here, these are actual values picked up by the model vs, defaults above:
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Comparing MoE Architecture: v3 vs v2.5 Model Configurations
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Looking at the config.json for both the models:
v3 (left) vs v2.5 (right) Interesting things: MoE related:
v3: "moe_intermediate_size": 2048, "n_routed_experts": 256, "n_shared_experts": 1, "num_experts_per_tok": 8 v2: "moe_intermediate_size": 1536, "n_routed_experts": 160, -
Understanding the Differences Between Two Expert Selection Methods
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In case people want to understand the difference b/w the two expert selection methods: