Enterprise LLM-based applications, middleware and specialized models.
huggingface.co
We’re on a journey to advance and democratize artificial intelligence through open source and open science.
huggingface.co
Model Card for Model ID
dragon-yi-6b-v0 part of the dRAGon ("Delivering RAG On ...") model series, RAG-instruct trained on top of a Yi-6B base model.
DRAGON models have been fine-tuned with the specific objective of fact-based question-answering over complex business and legal documents with an emphasis on reducing hallucinations and providing short, clear answers for workflow automation.
Benchmark Tests
Evaluated against the benchmark test:
RAG-Instruct-Benchmark-Tester
Average of 2 Test Runs 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:
99.5 correct out of 100
--Not Found Classification: 90.0%
--Boolean: 87.5%
--Math/Logic: 77.5%
--Complex Questions (1-5): 4 (Above Average)
--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).
Model Description
- Developed by: llmware
- Model type: Yi
- Language(s) (NLP): English
- License: Yi License Link
- Finetuned from model: Yi-6B
Direct Use
DRAGON is designed for enterprise automation use cases, especially in knowledge-intensive industries, such as financial services, legal and regulatory industries with complex information sources.
DRAGON models have been trained for common RAG scenarios, specifically: question-answering, key-value extraction, and basic summarization as the core instruction types without the need for a lot of complex instruction verbiage - provide a text passage context, ask questions, and get clear fact-based responses.
This model is licensed according to the terms of the license of the base model, Yi-6B, at this
link.
Bias, Risks, and Limitations
Any model can provide inaccurate or incomplete information, and should be used in conjunction with appropriate safeguards and fact-checking mechanisms.
How to Get Started with the Model
The fastest way to get started with BLING is through direct import in transformers:
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("dragon-yi-6b-v0")
model = AutoModelForCausalLM.from_pretrained("dragon-yi-6b-v0")
Please refer to the generation_test .py files in the Files repository, which includes 200 samples and script to test the model. The
generation_test_llmware_script.py includes built-in llmware capabilities for fact-checking, as well as easy integration with document parsing and actual retrieval to swap out the test set for RAG workflow consisting of business documents.
github.com
Magicoder: Source Code Is All You Need
OSS-Instruct, a novel approach to enlightening LLMs with open-source code snippets for generating low-bias and high-quality instruction data for code.
