Durante unas horas el modelo ha estado filtrado. Pero bueno, no hay prisas. Podemos esperar a mañana… Tampoco es que supiera qué hacer con semejante modelo
LLMS
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Llama 3 405B: Open Source LLM Launch and Capabilities
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Esta semana (mañana) se espera la salida del esperado modelo grande de LLama 3. Mucha curiosidad por las capacidades de este modelo y si se podría convertir en la primera opción open source por encima de los modelos privados. Eso sí… 405B parameters… unos 800GB de modelo
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India’s LLM Evolution: Culturally Attuned Models and Data Challenges
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At the Global IndiaAI Summit 2024 held in New Delhi, experts convened to discuss the evolution and challenges of LLMs in India, emphasizing the need for culturally attuned models and extensive data resources. Large Language Models are gaining prominence in academia and industry
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7th Neural Scaling Laws Workshop at ICML 2024
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We're Live! Join us for the 7th workshop on Neural Scaling Laws at ICML 2024 Join here: https://
lnkd.in/g9PTnKTG -
Simple Diffusion Language Models: New Approach
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Simple Diffusion Language Models https://
bit.ly/3S1z8Ap
#AI #MachineLearning #DeepLearning #LLMs #DataScience -
LLMs Demonstrate Capabilities Beyond Stochastic Parrot Theory
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Oops, looks like LLMs aren’t stochastic parrots after all.
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Graph Learning Meets Language Models at ICML 2024
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Attending Expo day at #ICML2024? Check out our Expo session, "Giving your Graph a Voice: Graph Representations and Large Language Models" today at 1pm CEST (Hall A8) to learn about all the work we've been doing to integrate graph learning with AI!
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Arize Phoenix: LLM Monitoring with One Line of Code
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#solarllm @ArizePhoenix is amazing. Adding just one line can run a monitoring server:
ssession = px.launch_app() You can monitor the entire LLM-related input and output on a chain with one line:
LangChainInstrumentor().instrument() Check out the full example at the SolarLLM -
ArizePhoenix: One-Line LLM Monitoring and Chain Instrumentation
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@ArizePhoenix is amazing. Start the local monitoring server by running one line of code: ssession = px.launch_app() With just one line of code, you can monitor all input and output related to LLMs on the link chain:
LangChainInstrumentor().instrument() Check out the full
