Create README.md

README.md

@@ -0,0 +1,48 @@
+---
+license: apache-2.0
+---
+
+# Model Card for Model ID
+
+bling-sheared-llama-1.3b-0.1 is part of the BLING ("Best Little Instruction-following No-GPU-required") model series, instruct trained on top of a Sheared-LLaMA-1.3B base model.
+
+BLING models are fine-tuned with distilled high-quality custom instruct datasets, targeted at a specific subset of instruct tasks with 
+the objective of providing a high-quality Instruct model that is 'inference-ready' on a CPU laptop even 
+without using any advanced quantization optimizations.
+
+### Model Description
+
+- **Developed by:** llmware
+- **Model type:** Instruct-trained decoder 
+- **Language(s) (NLP):** English
+- **License:** Apache 2.0
+- **Finetuned from model [optional]:** princeton-nlp/Sheared-LLaMA-1.3B
+
+  
+## Uses
+
+The intended use of BLING models is two-fold:
+
+1.  Provide high-quality Instruct models that can run on a laptop for local testing.  We have found it extremely useful when building a
+   proof-of-concept, or working with sensitive enterprise data that must be closely guarded, especially in RAG use cases.
+
+2.  Push the state of the art for smaller Instruct-following models in the sub-7B parameter range, especially 1B-3B, as single-purpose
+    automation tools for specific tasks through targeted fine-tuning datasets and focused "instruction" tasks.
+
+
+### Direct Use
+
+BLING is designed for enterprise automation use cases, especially in knowledge-intensive industries, such as financial services,
+legal and regulatory industries with complex information sources.  Rather than try to be "all things to all people," BLING models try to focus on a narrower set of Instructions more suitable to a ~1B parameter GPT model.
+
+BLING is ideal for rapid prototyping, testing, and the ability to perform an end-to-end workflow locally on a laptop without
+having to send sensitive information over an Internet-based API.
+
+The first BLING models have been trained for common RAG scenarios, specifically:   question-answering, key-value extraction, and basic summarization as the core instruction types
+without the need for a lot of complex instruction verbiage - provide a text passage context, ask questions, and get clear fact-based responses.
+
+
+## Bias, Risks, and Limitations
+
+Any model can provide inaccurate or incomplete information, and should be used in conjunction with appropriate safeguards and fact-checking mechanisms.
+