We're excited to introduce Contextual Answers, an API solution where answers are based on organizational knowledge, leaving no room for AI hallucinations. https://
ai21.com/blog/introduci
ng-contextual-answers
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LLMS
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AI21 Labs Launches Contextual Answers API Without Hallucinations
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70B Llama 2 Model Now Available on Replicate Platform
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70B Llama 2 is now live on Replicate. https://
replicate.com/replicate/llam
a70b-v2-chat
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AGI Hype Fades: Reality Check on GPT-5 Capabilities
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The hype of “God like AI” & AGI from GPT-4 & AutoGPT fades into reality. AGI is still sometime away & GPT-5 is unlikely to achieve AGI although there will be massive social media hype again with product promotion. The key is to step back and test it oneself and look at what
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Word and Sentence Embeddings: Building Blocks of Language Models
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Did you know that word and sentence embeddings are the building blocks of most large language models? Learn more about how they work and how they're used to understand human language in this exciting piece by luis_likes_math
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Llama II Release Advances Open-Source Language Model Progress
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Great to see the release of Llama II, open-source LLMs are making good progress! Still a lot of room to improve OS models positioning on the efficiency/performance front — so that they eventually catch up with proprietary solutions. An interesting challenge
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Where to Try LLaMA-2: Perplexity and Hugging Face Demos
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Where to try LLaMA-2(so far): ◆ LLaMa Chat via Perplexity(nice clean interface and super fast, 7B model): https://
llama.perplexity.ai ◆ Demo on Hugging Face(70B): https://
huggingface.co/spaces/ysharma
/Explore_llamav2_with_TGI
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The challenge of building national datasets for AI training
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Avec quelles bdd ? L’anglais c’est la langue d’internet donc c’est obvious de trouver des quantités astronomiques de données. Les seuls autres pays qui ont construit des bdd nationales qui serait de taille ok c’est la Russie et la Chine. En Europe on découvre à peine.
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LLaMA-2 Models Available on Hugging Face with Fine-tuning Support
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Update: All 12 LLaMA-2 models(base models + RLHF tuned models) are on Hugging Face. ◆ Quick text-generation inference with transformers
◆ Supported on HF inference endpoints
◆ Script to fine-tune(with PEFT) LLaMA-2 on your own dataset(
https://
gist.github.com/younesbelkada/
9f7f75c94bdc1981c8ca5cc937d4a4da
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Comparative analysis of Llama-2 and GPT-4: performance and infrastructure
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Llama2 vs GPT4 :
– Llama-2 reste globalement moins bon que GPT4
– bien que gratuit , hoster un modèle comme LLama-2 coûte cher, très cher. – mettre en place et maintenir l’infra coûte cher et prend bcp de temps
– point positif : privacy complète, pas de quota D’autres point ?
