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README.md

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-license: apache-2.0
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+---
+license: apache-2.0
+---
+
+# Model Card for Model ID
+
+<!-- Provide a quick summary of what the model is/does. -->
+
+**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.  
+
+[**bling-tiny-llama**](https://huggingface.co/llmware/bling-tiny-llama-v0) is a fact-based question-answering model, optimized for complex business documents.  
+
+To pull the model via API:  
+
+    from huggingface_hub import snapshot_download           
+    snapshot_download("llmware/bling-answer-tool", 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("bling-answer-tool")            
+    response = model.inference(query, add_context=text_sample)  
+
+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.  
+
+
+### Model Description
+
+<!-- Provide a longer summary of what this model is. -->
+
+- **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/)
+  
+
+## Model Card Contact
+
+Darren Oberst & llmware team