Long-Context Retrieval Models with Monarch Mixer
hazyresearch.stanford.edu
Long-Context Retrieval Models with Monarch Mixer
One of the focus areas at Together Research is new architectures for long context, improved training, and inference performance over the Transformer architecture. Spinning out of a research program from our team and academic collaborators, with roots in signal processing-inspired sequence models, we are excited to introduce the StripedHyena models.... See more
Paving the way to efficient architectures: StripedHyena-7B, open source models offering a glimpse into a world beyond Transformers
Chain of Density Prompt (original from research paper)
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Article: [link] (or attach a PDF)
You will generate increasingly concise, entity-dense summaries of the above Article.
Repeat the following 2 steps 5 times.
Step 1. Identify 1-3 informative Entities (";" delimited) from the Article which are missing from the previously generated summar
Matei Zaharia, Omar Khattab, Lingjiao Chen, et al. • The Shift From Models to Compound AI Systems
LangChain enables building dynamic, data-aware applications that go beyond what is possible by simply accessing LLMs via API calls.
Ben Auffarth • Generative AI with LangChain: Build large language model (LLM) apps with Python, ChatGPT, and other LLMs
Map reduce chain in LangChain This approach’s implications are that it allows the parallel processing of documents and enables the use of LLMs for reasoning, generating, or analyzing individual documents and combining their outputs.