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 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 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
Model Card Contact
Darren Oberst & llmware team