license: llama3.1
inference: false
DRAGON-LLAMA-3.1-GGUF
dragon-llama-3.1-gguf is RAG-instruct trained on top of a Llama-3.1 base model.
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: 94.0 correct out of 100
--Not Found Classification: 70.0%
--Boolean: 90.0%
--Math/Logic: 72.5%
--Complex Questions (1-5): 4 (Above Average - table-reading, causal)
--Summarization Quality (1-5): 4 (Above Average)
--Hallucinations: No hallucinations but a few instances of drawing on 'background' knowledge.
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: Phi-2B
- Language(s) (NLP): English
- License: Llama-3.1 Community License
- Finetuned from model: Llama-3.1-Base
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-llama-3.1-gguf", trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained("dragon-llama-3.1-gguf", trust_remote_code=True)
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.
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