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| license: apache-2.0 |
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| # Model Card for Model ID |
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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 |
| 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 |
| model = ModelCatalog().load_model("bling-answer-tool") |
| 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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| <!-- Provide a longer summary of what this model is. --> |
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| - **Developed by:** llmware |
| - **Model type:** GGUF |
| - **Language(s) (NLP):** English |
| - **License:** Apache 2.0 |
| - **Quantized from model:** [llmware/bling-tiny-llama](https://huggingface.co/llmware/bling-tiny-llama-v0/) |
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| ## Model Card Contact |
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| Darren Oberst & llmware team |
