title: README
emoji: 📚
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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 pipelines that will be transformative in the enterprise, especially in knowledge-intensive industries. Our open source research efforts are focused both on the new "ware" ("middleware" and "software" that will wrap and integrate LLMs), as well as building high-quality automation-focused enterprise Agent, RAG and embedding models.
Our model training initiatives fall into four major categories:
--SLIMs (Structured Language Instruction Models) - small, specialized function calling models for stacking in multi-model, Agent-based workflows
--BLING/DRAGON - highly-accurate fact-based question-answering models
--Industry-BERT - industry fine-tuned embedding models
--Private Inference Self-Hosting, Packaging and Quantization - GGUF, ONNX, OpenVino
Please check out a few of our recent blog postings related to these initiatives:
SMALL MODEL ACCURACY BENCHMARK |
OUR JOURNEY BUILDING ACCURATE ENTERPRISE SMALL MODELS |
THINKING DOES NOT HAPPEN ONE TOKEN AT A TIME |
SLIMs |
BLING |
RAG-INSTRUCT-TEST-DATASET |
LLMWARE EMERGING STACK |
MODEL SIZE TRENDS |
OPEN SOURCE RAG
1B-3B-7B LLM CAPABILITIES
Interested? Join us on Discord