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title: README |
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emoji: 📚 |
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--- |
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Welcome to the llmware HuggingFace page. We believe that the ascendence of LLMs creates a major new application pattern and data |
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pipelines that will be transformative in the enterprise, especially in knowledge-intensive industries. Our open source research efforts |
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are focused both on the new "ware" ("middleware" and "software" that will wrap and integrate LLMs), as well as designing |
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automation-focused enterprise RAG models, including both industry-specific embedding models and instruction-focused generation models. |
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Please check out a few of our blog postings related to these initiatives: |
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[BLING](https://medium.com/@darrenoberst/small-instruct-following-llms-for-rag-use-case-54c55e4b41a8) | |
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[RAG-INSTRUCT-TEST-DATASET](https://medium.com/@darrenoberst/how-accurate-is-rag-8f0706281fd9) | |
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[LLMWARE EMERGING STACK](https://medium.com/@darrenoberst/the-emerging-llm-stack-for-rag-deee093af5fa) | |
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[MODEL SIZE TRENDS](https://medium.com/@darrenoberst/are-the-mega-llms-driving-the-future-or-they-already-in-the-past-c3b949f9f5a5) | |
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[OPEN SOURCE RAG](https://medium.com/@darrenoberst/open-source-llms-in-rag-89d397b39511) | |
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[1B-3B-7B LLM CAPABILITIES](https://medium.com/@darrenoberst/rag-instruct-capabilities-they-grow-up-so-fast-2647550cdc0a) |
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