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@@ -6,23 +6,23 @@ license: apache-2.0
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  <!-- Provide a quick summary of what the model is/does. -->
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- **dragon-mistral-answer-tool** is a quantized version of DRAGON Mistral 7B, with 4_K_M GGUF quantization, providing a fast, small inference implementation for use on CPUs.
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- [**dragon-mistral-7b**](https://huggingface.co/llmware/dragon-mistral-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-mistral-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-mistral-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-mistral-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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  ### Model Description
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  - **Model type:** GGUF
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  - **Language(s) (NLP):** English
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  - **License:** Apache 2.0
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- - **Quantized from model:** [llmware/dragon-mistral](https://huggingface.co/llmware/dragon-mistral-7b-v0/)
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  ## Model Card Contact
 
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  <!-- Provide a quick summary of what the model is/does. -->
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+ **bling-answer-tool** is a quantized version of BLING Tiny-Llama 1B, with 4_K_M GGUF quantization, providing a very fast, very small inference implementation for use on CPUs.
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+ [**bling-tiny-llama**](https://huggingface.co/llmware/bling-tiny-llama-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/bling-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("bling-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/bling-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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  ### Model Description
 
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  - **Model type:** GGUF
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  - **Language(s) (NLP):** English
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  - **License:** Apache 2.0
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+ - **Quantized from model:** [llmware/dragon-mistral](https://huggingface.co/llmware/bling-tiny-llama-v0/)
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  ## Model Card Contact