The Open Source AI Revolution Begins Now...

LLMs like ChatGPT are not just fascinating, they're becoming increasing useful in our working lives. They've graduated from novelty to valuable tool. But building those tools is still in the hands of huge companies. Or is it? In this week's episode of Developer Voices, we're learning how you can run LLMs on your own laptop, and how you can customize the system to make a tailored research assistant, a better documentation-searcher, and much more. All you need is a guide on which pieces you need, and how they fit together, and that's exactly what this week's guest—Tobi Fankhänel—is here to take us through. A leaked memo from Google recently outlined how the Big Company Advantage has almost completely eroded, and how the next wave of LLM development is going to come from the open source community. So hackers rise up - the open source AI revolution begins now! -- Kris on Twitter: https://twitter.com/krisajenkins Kris on LinkedIn: https://www.linkedin.com/in/krisjenkins/ Tobias on LinkedIn: https://www.linkedin.com/in/tobias-fankh%C3%A4nel-749712180/ Tobias' blog: https://blog.exxample.eu LangChain: https://python.langchain.com/docs/get_started/introduction.html Embeddings: https://weaviate.io/blog/vector-embeddings-explained Vector Databases: https://en.wikipedia.org/wiki/Vector_database "We have no moat" – Google Employee on Open-source LLMs: https://www.semianalysis.com/p/google-we-have-no-moat-and-neither "Attention is all you need" - https://proceedings.neurips.cc/paper_files/paper/2017/file/3f5ee243547dee91fbd053c1c4a845aa-Paper.pdf Timeline since Meta open-sourced their first-gen models: https://www.semianalysis.com/i/119223672/the-timeline Run LLMs on CPU only or, since May, mix CPU and GPU usage: https://github.com/abetlen/llama-cpp-python Samantha: https://erichartford.com/meet-samantha Embedding model leaderboards: https://huggingface.co/spaces/mteb/leaderboard Open-source LLMs: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard LLaMA: https://ai.facebook.com/blog/large-language-model-llama-meta-ai/ Blog post: Design-pattern 'In-context learning' https://a16z.com/2023/06/20/emerging-architectures-for-llm-applications/#section--2 Tobi's GitHub branch 'In-context learning with LangChain' https://github.com/aviav/turmbauten/blob/spaghetti-code/CHANGELOG.md Prompt Syntax Cheat Sheet: https://github.com/oobabooga/text-generation-webui/tree/main/characters/instruction-following Google Workspace Labs Sign-Up: https://workspace.google.com/labs-sign-up/ GMail Workspace Labs Demo Video, click 'See it in action': https://workspace.google.com/solutions/ai/#m10 Prediction trading on open-source LLMs vs GPT-4: https://manifold.markets/PeterWildeford/will-i-peter-wildeford-think-that-t-c95ff3c1b385