llmware / dragon-mistral-0.3-gguf

dragon-mistral-0.3-gguf

dragon-mistral-0.3-gguf is part of the DRAGON model series, RAG-instruct trained for fact-based question-answering use cases on top of a Mistral 7b v0.3 base model.

Benchmark Tests

Evaluated against the benchmark test: RAG-Instruct-Benchmark-Tester
1 Test Run (with temperature = 0.0 and 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 : 99.5 correct out of 100
--Not Found Classification: 95.0%
--Boolean: 82.5%
--Math/Logic: 67.5%
--Complex Questions (1-5): 4 (Above Average - multiple-choice, causal)
--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).

Note: compare results with dragon-mistral-7b .

Model Description

• Developed by: llmware
• Model type: dragon-rag-instruct
• Language(s) (NLP): English
• License: Apache 2.0
• Finetuned from model: Mistral-7B-0.3-Base

Details on the prompt wrapper and other configurations are on the config.json file in the files repository.

How to Get Started with the Model

To pull the model via API:

from huggingface_hub import snapshot_download           
snapshot_download("llmware/dragon-mistral-0.3-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  

# to load the model and make a basic inference
model = ModelCatalog().load_model("llmware/dragon-mistral-0.3-gguf", temperature=0.0, sample=False)
response = model.inference(query, add_context=text_sample)  

Details on the prompt wrapper and other configurations are on the config.json file in the files repository.

Model Card Contact

Darren Oberst & llmware team