llmware / dragon-yi-9b-gguf

DRAGON-YI-9B-GGUF

dragon-yi-9b-gguf is a fact-based question-answering model, optimized for complex business documents, finetuned on top of 01-ai/yi-v1.5-9b base and quantizedwith 4_K_M GGUF quantization, providing an inference implementation for use on CPUs.

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-9b-gguf", 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-9b-gguf")            
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: Apache 2.0

Model Card Contact

Darren Oberst & llmware team