--- license: other --- # Model Card for Model ID **dragon-yi-answer-tool** is a quantized version of DRAGON Yi 6B, with 4_K_M GGUF quantization, providing a fast, small inference implementation for use on CPUs. [**dragon-yi-6b**](https://huggingface.co/llmware/dragon-yi-6b-v0) is a fact-based question-answering model, optimized for complex business documents. ## Benchmark Tests Evaluated against the benchmark test: RAG-Instruct-Benchmark-Tester 1 Test Run (temperature=0.0, sample=False) with 1 point for correct answer, 0.5 point for partial correct or blank / NF, 0.0 points for incorrect, and -1 points for hallucinations. --Accuracy Score: 98.0 correct out of 100 --Not Found Classification: 90.0% --Boolean: 97.5% --Math/Logic: 95% --Complex Questions (1-5): 5 (Very Strong) --Summarization Quality (1-5): 4 (Above Average) --Hallucinations: No hallucinations observed in test runs. For test run results (and good indicator of target use cases), please see the files ("core_rag_test" and "answer_sheet" in this repo). To pull the model via API: from huggingface_hub import snapshot_download snapshot_download("llmware/dragon-yi-answer-tool", local_dir="/path/on/your/machine/", local_dir_use_symlinks=False) Load in your favorite GGUF inference engine, or try with llmware as follows: from llmware.models import ModelCatalog model = ModelCatalog().load_model("dragon-yi-answer-tool") response = model.inference(query, add_context=text_sample) Note: please review [**config.json**](https://huggingface.co/llmware/dragon-yi-answer-tool/blob/main/config.json) in the repository for prompt wrapping information, details on the model, and full test set. ### Model Description - **Developed by:** llmware - **Model type:** GGUF - **Language(s) (NLP):** English - **License:** Yi Community License - **Quantized from model:** [llmware/dragon-yi](https://huggingface.co/llmware/dragon-yi-6b-v0/) ## Model Card Contact Darren Oberst & llmware team