--- license: apache-2.0 inference: false --- # Model Card for Model ID **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 - **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