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title: README |
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colorTo: blue |
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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 building high-quality |
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automation-focused enterprise Agent, RAG and embedding small specialized language models. |
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Our model training initiatives fall into four major categories: |
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**SLIMs** - small, specialized function calling models for stacking in multi-model, Agent-based workflows -- [SLIMs](https://medium.com/@darrenoberst/slims-small-specialized-models-function-calling-and-multi-model-agents-8c935b341398) |
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**BLING/DRAGON** - highly-accurate fact-based question-answering models |
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-- [SMALL MODEL ACCURACY BENCHMARK](https://medium.com/@darrenoberst/best-small-language-models-for-accuracy-and-enterprise-use-cases-benchmark-results-cf71964759c8) | |
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-- [OUR JOURNEY BUILDING ACCURATE ENTERPRISE SMALL MODELS](https://medium.com/@darrenoberst/building-the-most-accurate-small-language-models-our-journey-781474f64d88) |
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**Industry-BERT** - industry fine-tuned embedding models |
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**Private Inference** - Self-Hosting, Packaging and Quantization - GGUF, ONNX, OpenVino |
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Please check out a few of our recent blog postings related to these initiatives: |
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[THINKING DOES NOT HAPPEN ONE TOKEN AT A TIME](https://medium.com/@darrenoberst/thinking-does-not-happen-one-token-at-a-time-0dd0c6a528ec) | |
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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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[BECOMING A MASTER FINETUNING CHEF](https://medium.com/@darrenoberst/6-tips-to-becoming-a-master-llm-fine-tuning-chef-143ad735354b) |
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Interested? [Join us on Discord](https://discord.gg/MhZn5Nc39h) |
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