---
license: apache-2.0  
inference: false  
---

# dragon-mistral-0.3-gguf

<!-- Provide a quick summary of what the model is/does. -->

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](https://www.huggingface.co/datasets/llmware/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](https://www.huggingface.co/llmware/dragon-mistral-7b-v0).  


### Model Description

<!-- Provide a longer summary of what this model is. -->

- **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