At NeurIPS this week, reach out if you want to discuss our work around LLM at @DeepMind (Chinchilla, Retro, Flamingo), and if you're interested in working with us!
LLMS
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OpenAI Releases Improved GPT-3 Model on Playground
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@OpenAI just released a new GPT-3 model! They’re promising better performance across a variety of tasks. You can try it now on the OpenAI Playground. -
AI21 Labs Launches Jurassic-1 LLM on AWS SageMaker
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Our Co-founder & Co-CEO @origoshen kicking off @awscloud #reInvent2022 with an exciting announcement: you can now deploy our LLM Jurassic-1 on your private SageMaker environment!
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Cerebras Sparse ML Techniques Preserve GPT Model Accuracy
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At Cerebras, we are creating sparse ML techniques paired with our CS-2 hardware. Our initial work has shown that high degrees of sparsity can be induced into large-scale GPT models while still preserving model accuracy Learn more here: https://
tinyurl.com/yc22pbbz #Neurisp2022 #ai -
Create Custom Memory Modules for LangChain Chains
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And here's how you can create your own custom memory module! https://
langchain.readthedocs.io/en/latest/exam
ples/memory/custom_memory.html
… This can be used to implement your own ideal memory structure and then easily plug it into any chain. -
Expanding Memory Types in LangChain With Community Contributions
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There are many more types of memory we hope to add! @sjwhitmore has been pioneering this effort so far, and we hope a lot of these come from the community. Check out this idea from @BrennanErbz for inspiration
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Contribute Memory Implementations to LangChain Core Library
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If you do have a memory implementation you think is generally useful, we'd LOVE to have it contributed back into the core library! We are super excited to see what people build with this https://
github.com/hwchase17/lang
chain
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Getting Started with Memory in Conversation Chains
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Here is a notebook for getting started with memory. It walks through maybe the most clear application of memory: a conversation chain. It showcases short term memory – remembering the conversation to date https://
langchain.readthedocs.io/en/latest/gett
ing_started/memory.html
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Add Memory to Any LLM Chain and Agent
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But you don't just have to use the predefined conversation chain! Here's how you can add memory to any LLM chain: https://
langchain.readthedocs.io/en/latest/exam
ples/memory/adding_memory.html
… And here's how you can add memory to an agent: https://
langchain.readthedocs.io/en/latest/exam
ples/memory/agent_with_memory.html
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LangChain Chains Now Support Stateful Conversations and Memory
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So far all the chains/agents in LangChain have been stateless, meaning each interaction is independent This is far from ideal for some real world settings (eg chatbots) where you want the chain/agent to remember previous conversations