Deepseek R1 is available in DataRobot With our model-agnostic approach, you can use the best model for the job with confidence. Try today: https://
datarobot.com/trial/ #deepseek #LLMs #genAI #AIgovernance #AI
LLMS
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DeepSeek R1 Now Available in DataRobot Platform
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Button switches model to o1
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This button changes the model to o1 so the response you see is generated by o1 too
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Mistral AI releases Mistral Small 3
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Mistral AI announced Mistral Small 3, a latency-optimized 24B-parameter model released under the Apache 2.0 license.
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LLM Agents Production: Architectures Challenges Best Practices
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LLM Agents in Production: Architectures, Challenges, and Best Practices – ZenML Blog https://
bit.ly/4gGcPtW
#AI #MachineLearning #DeepLearning #LLMs #DataScience -
New 24B Open-Source AI Model Achieves 81% MMLU Performance
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A new model to hasten AI progress. 24B, 81% MMLU, no RL for now! We're super excited to see the latest development in international open-source AI (kudos to Deepseek!), and cannot wait to bring new contributions to it. We're renewing our commitment to using Apache licenses. AI
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Small 3: New Efficient 24B Open Source Language Model
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Introducing Small 3, our most efficient and versatile model yet! Pre-trained and instructed version, Apache 2.0, 24B, 81% MMLU, 150 tok/s. No synthetic data so great base for anything reasoning – happy building!
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Mistral-Small-3-Instruct Model Released for Download
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magnet:?xt=urn:btih:11f2d1ca613ccf5a5c60104db9f3babdfa2e6003&dn=Mistral-Small-3-Instruct&tr=udp%
http://
2Ftracker.opentrackr.org1337announce&tr=http%https://t.co/ua2yzvEYLu1337announce -
Faster Performance and Two Deepseek R1 Model Versions Available
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It will be much faster and smoother today Plus we have two versions of Deepseek R1! The full model and the fast one!
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RAG Technology Evolution and Modern AI Applications
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Read more https://
open.substack.com/pub/louisbouch
ard/p/rag-is-old-news?r=25qlky&utm_campaign=post&utm_medium=web&showWelcomeOnShare=true
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Evolution of LLM Context Windows and RAG Technology
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In the early days of LLMs, context windows, which is what we send them as text, were small, often capped at just 4,000 tokens (or 3,000 words), making it impossible to load all relevant context. This limitation gave rise to approaches like Retrieval-Augmented Generation (RAG) in