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README.md
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<!-- Provide a quick summary of what the model is/does. -->
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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/
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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, add_context=text_sample)
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Note: please review [**config.json**](https://huggingface.co/llmware/
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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/
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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
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