Xavier Martinet - Top-KI-Führungskräfte & Innovatoren | Profile, Meilensteine & Projekte - xix.ai
Option

Entdecken Sie qualitativ hochwertige KI -Werkzeuge

Bringen Sie die weltweit führenden Tools für künstliche Intelligenz zusammen, um die Arbeitseffizienz zu verbessern

Suche nach Al -Tools…
Heim
KI-Prominente
Xavier Martinet
Xavier Martinet

Xavier Martinet

Forschungsingenieur, Meta AI
Geburtsjahr  1990
Nationalität  French

Wichtiger Meilenstein

2019 Meta AI beigetreten

An AI-Infrastruktur bei Meta gearbeitet

2023 LLaMA Paper

Mitverfasstes LLaMA-Forschungspapier

2024 LLaMA 3.1 Bereitstellung

Unterstützte Bereitstellung von LLaMA 3.1 auf Cloud-Plattformen

KI-Produkt

The Llama 4 models are auto-regressive language models that use a mixture-of-experts (MoE) architecture and incorporate early fusion for native multimodality.

Llama3.1 are multilingual and have a significantly longer context length of 128K, state-of-the-art tool use, and overall stronger reasoning capabilities.

Llama 3.1 405B is the first openly available model that rivals the top AI models when it comes to state-of-the-art capabilities in general knowledge, steerability, math, tool use, and multilingual translation.

The Llama 3.2 3B models support context length of 128K tokens and are state-of-the-art in their class for on-device use cases like summarization, instruction following, and rewriting tasks running locally at the edge.

Llama3.1 are multilingual and have a significantly longer context length of 128K, state-of-the-art tool use, and overall stronger reasoning capabilities.

Llama3 is Meta's latest open-source large language model, trained on a 15T corpus, supports an 8K context length, and has been optimized for effectiveness and safety.

Llama 3.1 405B is the first openly available model that rivals the top AI models when it comes to state-of-the-art capabilities in general knowledge, steerability, math, tool use, and multilingual translation.

Llama3.1 are multilingual and have a significantly longer context length of 128K, state-of-the-art tool use, and overall stronger reasoning capabilities.

Llama3.1 are multilingual and have a significantly longer context length of 128K, state-of-the-art tool use, and overall stronger reasoning capabilities.

The Llama 3.2 3B models support context length of 128K tokens and are state-of-the-art in their class for on-device use cases like summarization, instruction following, and rewriting tasks running locally at the edge.

The Llama 4 models are auto-regressive language models that use a mixture-of-experts (MoE) architecture and incorporate early fusion for native multimodality

Llama3 is Meta's latest open-source large language model, trained on a 15T corpus, supports an 8K context length, and has been optimized for effectiveness and safety.

The Mixtral-8x7B Large Language Model (LLM) is a pretrained generative Sparse Mixture of Experts. The Mistral-8x7B outperforms Llama 2 70B on most benchmarks we tested.

Llama 3.1 405B is the first openly available model that rivals the top AI models when it comes to state-of-the-art capabilities in general knowledge, steerability, math, tool use, and multilingual translation.

Llama3.1 are multilingual and have a significantly longer context length of 128K, state-of-the-art tool use, and overall stronger reasoning capabilities.

The Mixtral-8x7B Large Language Model (LLM) is a pretrained generative Sparse Mixture of Experts. The Mistral-8x7B outperforms Llama 2 70B on most benchmarks we tested.

The Llama 4 models are auto-regressive language models that use a mixture-of-experts (MoE) architecture and incorporate early fusion for native multimodality

Llama3.1 are multilingual and have a significantly longer context length of 128K, state-of-the-art tool use, and overall stronger reasoning capabilities.

Llama3.1 are multilingual and have a significantly longer context length of 128K, state-of-the-art tool use, and overall stronger reasoning capabilities.

The Llama 3.2 3B models support context length of 128K tokens and are state-of-the-art in their class for on-device use cases like summarization, instruction following, and rewriting tasks running locally at the edge.

Llama3.1 are multilingual and have a significantly longer context length of 128K, state-of-the-art tool use, and overall stronger reasoning capabilities.

Persönliches Profil

Unterstützte die Infrastruktur von LLaMA für das Training großer Modelle.

Zurück nach oben
OR