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README.md
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**dragon-llama-answer-tool** is a quantized version of DRAGON Llama 7B, with 4_K_M GGUF quantization, providing a fast, small inference implementation for use on CPUs.
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from huggingface_hub import snapshot_download
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snapshot_download("llmware/dragon-llama-answer-tool", local_dir="/path/on/your/machine/", local_dir_use_symlinks=False)
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Load in your favorite GGUF inference engine, or try with llmware as follows:
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from llmware.models import ModelCatalog
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model = ModelCatalog().load_model("
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response = model.inference(query, text_sample)
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Note: please review [**config.json**](https://huggingface.co/llmware/dragon-llama-answer-tool/blob/main/config.json) in the repository for prompt wrapping information, details on the model, and full test set.
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**dragon-llama-answer-tool** is a quantized version of DRAGON Llama 7B, with 4_K_M GGUF quantization, providing a fast, small inference implementation for use on CPUs.
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[**dragon-llama-7b**](https://huggingface.co/llmware/dragon-llama-7b-v0) is a fact-based question-answering model, optimized for complex business documents.
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To pull the model via API:
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from huggingface_hub import snapshot_download
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snapshot_download("llmware/dragon-llama-answer-tool", local_dir="/path/on/your/machine/", local_dir_use_symlinks=False)
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Load in your favorite GGUF inference engine, or try with llmware as follows:
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from llmware.models import ModelCatalog
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model = ModelCatalog().load_model("dragon-llama-answer-tool")
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response = model.inference(query, add_context=text_sample)
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Note: please review [**config.json**](https://huggingface.co/llmware/dragon-llama-answer-tool/blob/main/config.json) in the repository for prompt wrapping information, details on the model, and full test set.
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