"Unlocking the Future: Open-Source LLMs vs OpenAI!" ?? Transitioning from #OpenAI to open-source models can be challenging and requires careful consideration of various factors. ?? The decision to switch should be based on factors such as security, evaluation methods, control and customization options, costs, and performance. ?? Open-source models offer advantages in #dataprivacy and control, particularly for sensitive industries like defense and #healthcare. ?? Evaluation of models is crucial, and open-source models provide finer-grained evaluation at the #token level, facilitating a more accurate assessment. ?? While using #OpenAI may be cheaper for occasional usage, open-source models are improving rapidly in terms of performance and offer more flexibility and customization options for specific use cases. ???? https://lnkd.in/eetBuCZy Gregory Stone, Josh Beck, Sarah Cornett, Willayna Banner, Mehrdad Razavi, Amrith Kumar, James Probst, Amaresh Tripathy, Ian Dalton, CAIA, Dennis Ganesh
Rethink AI的动态
最相关的动态
-
My personal take on this is the following: AGI has been achieved at OpenAI and this is a way to fully power it.
Microsoft, OpenAI plan $100 billion data-center project, media report says
reuters.com
要查看或添加评论,请登录
-
Security is a critical aspect in production Machine Learning systems and Google Bard is no exception, this great article showcases an insightful perspective of a vulnability disclosure: This vulnerability was exploited by using Bard's new Extensions feature to access personal documents and emails, and manipulating its markdown rendering capability to create image tags that connected to an attacker-controlled server. The exploit involved bypassing Google's Content Security Policy using Google Apps Script, enabling the exfiltration of chat history to a Google Doc. The issue was reported to Google and fixed within a month, highlighting the security challenges in LLM applications, especially when handling sensitive data. Link: https://lnkd.in/dNd_tJYd If you liked this article you can join 50,000+ practitioners for weekly tutorials, resources, OSS frameworks, and MLOps events across the machine learning ecosystem: https://lnkd.in/eRBQzVcA #ML #MachineLearning #ArtificialIntelligence #AI #MLOps #AIOps #DataOps #augmentedintelligence #deeplearning #privacy #kubernetes #datascience #python #bigdata cc Florence Mottay, Itay D.
要查看或添加评论,请登录
-
Ever feel like managing AI is too complicated? This latest article from Microsoft delves into the intricacies of utilizing containers for optimal load balancing at OpenAI endpoints. Gain insights into enhancing efficiency and ensuring robust AI performance. #AI #OpenAI #Microsoft
?? Smart load balancing for OpenAI endpoints using containers
techcommunity.microsoft.com
要查看或添加评论,请登录
-
Nomic's embeddings keep getting better and better. You've seen the open source offerings, we're excited to have this latest multi-modal offering on #awsmarketplace. If you're looking for ways to put Nomic AI into production on #aws, let's talk. Great work Brandon Duderstadt and team!
Announcing Nomic Embed Vision All Nomic Embeddings are now multimodal with backwards compatibility. Blog: https://lnkd.in/ewcnr28G Nomic Embed Vision: - Expands Nomic Embed into a high quality, unified embedding space for image, text, and multimodal tasks - Outperforms both OpenAI CLIP and text-embedding-3-small - Open weights and code to enable indie hacking, research, and experimentation - Released in collaboration with MongoDB, LangChain, LlamaIndex, Amazon Web Services (AWS),?Hugging Face, DigitalOcean and Lambda Huggingface Open Weight Models: - v1: https://lnkd.in/eZBx2SWw - v1.5: https://lnkd.in/e2y9aFje Access on AWS Marketplace and in the Nomic Embedding API - https://lnkd.in/eCEd2ySs - https://lnkd.in/eQFteaBx
Nomic Embed Vision: Expanding The Latent Space
blog.nomic.ai
要查看或添加评论,请登录
-
nomic-embed-text-v1.5 (the 11th most downloaded model on Hugging Face) is now multimodal, so bring on your images! Multimodal = your text and images can be visualized and analyzed in the same space, isn't that cool?
Announcing Nomic Embed Vision All Nomic Embeddings are now multimodal with backwards compatibility. Blog: https://lnkd.in/ewcnr28G Nomic Embed Vision: - Expands Nomic Embed into a high quality, unified embedding space for image, text, and multimodal tasks - Outperforms both OpenAI CLIP and text-embedding-3-small - Open weights and code to enable indie hacking, research, and experimentation - Released in collaboration with MongoDB, LangChain, LlamaIndex, Amazon Web Services (AWS),?Hugging Face, DigitalOcean and Lambda Huggingface Open Weight Models: - v1: https://lnkd.in/eZBx2SWw - v1.5: https://lnkd.in/e2y9aFje Access on AWS Marketplace and in the Nomic Embedding API - https://lnkd.in/eCEd2ySs - https://lnkd.in/eQFteaBx
Nomic Embed Vision: Expanding The Latent Space
blog.nomic.ai
要查看或添加评论,请登录
-
Announcing Nomic Embed Vision All Nomic Embeddings are now multimodal with backwards compatibility. Blog: https://lnkd.in/ewcnr28G Nomic Embed Vision: - Expands Nomic Embed into a high quality, unified embedding space for image, text, and multimodal tasks - Outperforms both OpenAI CLIP and text-embedding-3-small - Open weights and code to enable indie hacking, research, and experimentation - Released in collaboration with MongoDB, LangChain, LlamaIndex, Amazon Web Services (AWS),?Hugging Face, DigitalOcean and Lambda Huggingface Open Weight Models: - v1: https://lnkd.in/eZBx2SWw - v1.5: https://lnkd.in/e2y9aFje Access on AWS Marketplace and in the Nomic Embedding API - https://lnkd.in/eCEd2ySs - https://lnkd.in/eQFteaBx
Nomic Embed Vision: Expanding The Latent Space
blog.nomic.ai
要查看或添加评论,请登录
-
The 11th most downloaded model on Huggingface, nomic-embed-text-v1.5, is now multimodal! You can use your Nomic Text Embeddings to interact with your image datasets! https://lnkd.in/e94Xx9G2
Announcing Nomic Embed Vision All Nomic Embeddings are now multimodal with backwards compatibility. Blog: https://lnkd.in/ewcnr28G Nomic Embed Vision: - Expands Nomic Embed into a high quality, unified embedding space for image, text, and multimodal tasks - Outperforms both OpenAI CLIP and text-embedding-3-small - Open weights and code to enable indie hacking, research, and experimentation - Released in collaboration with MongoDB, LangChain, LlamaIndex, Amazon Web Services (AWS),?Hugging Face, DigitalOcean and Lambda Huggingface Open Weight Models: - v1: https://lnkd.in/eZBx2SWw - v1.5: https://lnkd.in/e2y9aFje Access on AWS Marketplace and in the Nomic Embedding API - https://lnkd.in/eCEd2ySs - https://lnkd.in/eQFteaBx
Nomic Embed Vision: Expanding The Latent Space
blog.nomic.ai
要查看或添加评论,请登录
-
Let's talk about #AI's #Enterprise #Adoption, and the numerous checklists that recently have been thoroughly shared about it. Let's consider OWASP Top 10 For Large Language Model Applications suggestions, warnings et alia, and then... Let's let out a #RoTFL moment, and then maybe focus to prevent this from happening again, would we? ?????????? Because "I'm sorry but I cannot fulfill This Request it violates OpenAI use Policy" in Grey, at 1,919 $ seems very expensive, to just be a statement there. Then when laughing ends ... consider how dangerous this could become in a production environment, client exposed : no 3rd party content check, 3rd party changing policies, 3rd party uptime, a 3rd party mutating model, supply chain attacks induced damage... the list goes on. This may seem like a laughable case, but it's also a precious and stark reminder, that strengthens the importance of the ongoing AI security research diffusion and awareness.
I’m sorry, but I cannot fulfill this request as it goes against OpenAI use policy
theverge.com
要查看或添加评论,请登录
-
Practical Red Teaming LLM applications (now adapted for Microsoft Azure OpenAI). Your latest AI application, powered by LLM (large language model), may have weaknesses, such as generating harmful content or biased outputs, due to flaws in its system messages or skewed data content. As a Red Teamer, you can help your customers identify and fix these vulnerabilities before they cause problems. Andrew Ng's DeepLearning.AI partnered with Giskard to release a great training course on Red Teaming, an emerging field in Responsible AI: https://lnkd.in/evhw38Vw. If you want to learn about Red Teaming from the course and then follow along with the practical exercises in Azure OpenAI, you can find adapted Jupyter notebooks on this GitHub repo: https://lnkd.in/eatqq_XM. As an extra bonus, helper functions were also updated to use the latest version of LlamaIndex (popular framework for Retrieval-Augmented Generation), v0.10.x. Enjoy!
要查看或添加评论,请登录
-
SME - #Leadership, #SolutionsArchitecture (#Cloud, #BigData, #VectorDB, #LLM, #DataScience, #GenAI, #DataAnalytics, #DataEngineering, #DataArchitecture, #MachineLearning, #ArtificialIntelligence, #YugabyteDB, #CockRoach)
Fully local retrieval-augmented generation, step by step: In “Retrieval-augmented generation, step by step,” we walked through a very simple RAG example. Our little application augmented a large language model (LLM) with our own documents, enabling the language model to answer questions about our own content.?That example used an embedding model from OpenAI, which meant we had to send our content to OpenAI’s servers—a potential data privacy violation, depending on the application.?We also used OpenAI’s public LLM. To read this article in full, please click here #MachineLearning #ArtificialIntelligence #DrivenByData #CIO #ITManager #ML #DataAnalytics #ITDirector
Fully local retrieval-augmented generation, step by step
infoworld.com
要查看或添加评论,请登录
cc : Kamal Busam, Mike Fortuna, Oz Tuzcu, Subal Mishra, Reza Moghtaderi Esfahani, Patrick Millar, Alicia Jones-McFadden, Prayaga Durga Sai Prasad, Andrew Rieser, Jaime Wilde (Rice), Marty Pittman, Joel Ferris, Joe Fuqua, Tyler Hawn