What does it take to train #Llama2 on your own data? Only 10 lines of YAML with
@ludwig_ai
. These new capabilities are part of our upcoming v0.8 release. Get a sneak peek and #finetune LLaMa2 with this free colab notebook: https://
colab.research.google.com/drive/1c3AO8l_
H6V_x37RwQ8V7M6A-RmcBf2tG?usp=sharing
….
OPEN SOURCE
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Train Llama2 on Custom Data with Just 10 Lines YAML
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HuggingGPT Agent Connects HuggingFace Models via LangChain
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HuggingGPT Another installment to langchain_experimental Thanks to @itricktreat you can now use an agent which connects to a variety of @huggingface models in @langchain Based on the paper here: https://
arxiv.org/abs/2303.17580 Docs: https://
python.langchain.com/docs/use_cases
/autonomous_agents/hugginggpt.html
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RealChar Team Highlights AI Companions with LangSmith Integration
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The whole @RealCharAI team is awesome Really excited to have this opportunity to help highlight their work! Play around with their GitHub here: https://
github.com/Shaunwei/RealC
har
… Read blog here: https://
blog.langchain.dev/realchar-x-lan
gsmith-ai-companions/
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Hugging Face and MLCommons Launch MedPerf Medical AI Benchmarking
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Hugging Face is thrilled to partner with @MLCommons on MedPerf, a medical benchmarking framework using federated evaluation. Published @NatMachIntell
, this open science initiative is a major step forward towards rigorous and privacy-preserving evaluation of medical AI models. -
Open Science Medical AI Initiative Launches with 50+ Partners
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More open science efforts are needed in medical AI, thus we are proud to contribute to this effort alongside 20+ academic institutions, 9+ hospitals, and 20+ companies. Interested in learning more?
Paper: https://
nature.com/articles/s4225
6-023-00652-2
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Website: https://
medperf.org -
LangChain LLMChain Components: Core Modular Architecture
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Something I was working on at the @agihouse_org hackathon over the weekend The LLMChain (prompt template + llm + output parser) is a core, modular component and is where most customization happens. Understanding this is crucial for understanding LangChain!
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Flash Attention 2 Integration Reduces Lit-GPT Runtime by 11%
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My colleagues already added Flash Attention 2 to our Lit-GPT repo!
So if you are working on the NeurIPS LLM Efficiency Challenge (for which Lit-GPT is the official starter kit, https://
llm-efficiency-challenge.github.io), you can shave ~11% off your total runtime -
Chainlit: Python Framework for Rapid LLM Application Development
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GitHub – Chainlit/chainlit: Build Python LLM apps in minutes
https://bit.ly/42QIzFn #AI #MachineLearning #DeepLearning #LLMs #DataScience -
Open Source LLMs Superior to GPT-4 API for Critical Services
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I am just picturing a case where someone built a crucial service for their company on top of the GPT-4 API . This is a good example why open source LLMs > GPT-4.
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GPT-4 Degradation and Alternative Model Architectures
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Oh, they made GPT-4 worse … again? Dang. I finally started to find it useful.
Thanks for the reference to Retention, looks like it's orthogonal attempt to RWKV and Hyena?