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---
license: apache-2.0
inference: false
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
**dragon-qwen-7b-gguf** is a quantized version of a fact-based question answering model, optimized for complex business documents, fine-tuned on top of Qwen2 7B base, and then packaged with 4_K_M GGUF quantization, providing a fast, small inference implementation for use on CPUs.
### 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 sample=False & temperature=0.0 (deterministic output) - 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**: **99.0** correct out of 100
--Not Found Classification: 85.0%
--Boolean: 100.0%
--Math/Logic: 92.5%
--Complex Questions (1-5): 5 (Best in Class)
--Summarization Quality (1-5): 3 (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).
To pull the model via API:
from huggingface_hub import snapshot_download
snapshot_download("llmware/dragon-qwen-7b-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
model = ModelCatalog().load_model("dragon-qwen-7b-gguf")
response = model.inference(query, add_context=text_sample)
Note: please review [**config.json**](https://huggingface.co/llmware/dragon-qwen-7b-gguf/blob/main/config.json) in the repository for prompt wrapping information, details on the model, and full test set.
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** llmware
- **Model type:** GGUF
- **Language(s) (NLP):** English
- **License:** Apache 2.0
- **Quantized from model:** llmware/dragon-qwen
## Model Card Contact
Darren Oberst & llmware team |