If you are using jupyter notebooks for Python and Data Science, try these 7 magic commands that will save you a ton of time: 1. Jupyter AI: Select any model and chat with it right from the Jupyter Notebook.
GENERATIVE AI
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DataRobot Launches Generative AI Catalyst Program for Enterprise
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ICYMI: Last week, we launched the DataRobot Generative AI Catalyst Program to jumpstart and accelerate the delivery of #generativeAI use cases. The new program provides a complete value-driven #generativeAI playbook including: Custom roadmaps for high-impact use cases
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Multi Motion Brush Adds Realistic Motion to Video Scenes
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Use Multi Motion Brush to add realistic motion throughout your scenes.
— Runway (@runwayml) 30 janvier 2024
Learn how with today's Runway Academy. pic.twitter.com/BTyRCCQcdDUse Multi Motion Brush to add realistic motion throughout your scenes. Learn how with today's Runway Academy.
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Baichuan 3: 100B+ Parameter LLM Surpasses GPT-4 Chinese
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Baichuan AI released LLM Baichuan 3 with parameters exceeding 100 billion. It has substantial improvements in foundational capabilities, surpassing GPT-4 in various authoritative evaluations of Chinese tasks and emerging as the top-performing LLM for Chinese medical tasks.
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AI Models Scale Exponentially: Trillions Parameters Nine Orders Magnitude
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In just a few years, cutting-edge models have gone from using millions of parameters to trillions. The amount of computation used to train the largest AI models has increased by nine orders of magnitude. Here's what we should do next:
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Building Chatbots with Source Citations Using LangChain
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Question Answering with Citations When building a chatbot over documents, it's often useful to ask it to cite it's sources. This can help build trust and check responses We've recently added a detailed guide on how to do this! Guide: https://
python.langchain.com/docs/use_cases
/question_answering/citations
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Codellama-70b-instruct success rate, prompt variations
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FWIW I wasn’t able to reproduce, using http://
labs.perplexity.ai with codellama-70b-instruct. I guess it depends on some detail of system prompt / format. I got 10/10 success using both prompts from the replies here and my own. -
MLX Community Releases Quantized Code Llama Models
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Just finished uploading 4 MLX models. Quantized Code Llama 7B and 13B, Python and Instruct! Link here https://
huggingface.co/mlx-community -
CodeLlama-70B-Instruct Now Available on Perplexity Labs API
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CodeLlama-70B-Instruct is now on Perplexity Labs and PPLX-API. Take it for a spin on https://t.co/2yYQB9jln7 pic.twitter.com/r33knimyST
— Perplexity (@perplexity_ai) 30 janvier 2024CodeLlama-70B-Instruct is now on Perplexity Labs and PPLX-API. Take it for a spin on http://
labs.pplx.ai -
Meta’s Strategy: Commoditizing LLMs for Personal AI Dominance
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Meta's strategy is interesting because it disrupts LLM providers with no effect on their own business. The largest opportunity for them is to be the AI in your glasses, on your phone, with access to your data – not your LLM provider. They basically commoditized LLMs.