metadata

license: other
license_link: https://huggingface.co/01-ai/Yi-6B/blob/main/LICENSE
license_name: yi-license
model_creator: 01-ai
model_name: Yi 6B
model_type: yi

Model Card for Model ID

dragon-yi-6b-0.1 part of the dRAGon ("Delivering RAG On ...") model series, RAG-instruct trained on top of a Yi-6B base model.

DRAGON models are fine-tuned with high-quality custom instruct datasets, designed for production quality use in RAG scenarios.

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 (Low-Medium)
--Summarization Quality (1-5): 4 (Coherent, extractive)
--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

Uses

The intended use of DRAGON models is two-fold:

Provide high-quality RAG-Instruct models designed for fact-based, no "hallucination" question-answering in connection with an enterprise RAG workflow.
DRAGON models are fine-tuned on top of leading base foundation models, generally in the 6-7B+ range, and purposefully rolled-out across multiple base models to provide choices and "drop-in" replacements for RAG specific use cases.
DRAGON models were trained on the same principles as the BLING models, so generally, it should be easy to "upgrade" from a BLING model in testing to a DRAGON model in production.

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-0.1")  
model = AutoModelForCausalLM.from_pretrained("dragon-yi-6b-0.1")

The DRAGON model was fine-tuned with a simple "<human> and <bot> wrapper", so to get the best results, wrap inference entries as:

full_prompt = "\<human>\: " + my_prompt + "\n" + "\<bot>\:"

The BLING model was fine-tuned with closed-context samples, which assume generally that the prompt consists of two sub-parts:

Text Passage Context, and
Specific question or instruction based on the text passage

To get the best results, package "my_prompt" as follows:

my_prompt = {{text_passage}} + "\n" + {{question/instruction}}

Model Card Contact

Darren Oberst & llmware team

Please reach out anytime if you are interested in this project!