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---
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
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