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license: apache-2.0 |
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--- |
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Some GGUF v2 quantizations of the model [llmware/bling-sheared-llama-1.3b-0.1](https://huggingface.co/llmware/bling-sheared-llama-1.3b-0.1) |
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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. |
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BLING models are fine-tuned with distilled high-quality custom instruct datasets, targeted at a specific subset of instruct tasks with |
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the objective of providing a high-quality Instruct model that is 'inference-ready' on a CPU laptop even |
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without using any advanced quantization optimizations. |
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### Model Description |
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- **Developed by:** llmware |
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- **Model type:** Instruct-trained decoder |
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- **Language(s) (NLP):** English |
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- **License:** Apache 2.0 |
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- **Finetuned from model [optional]:** princeton-nlp/Sheared-LLaMA-1.3B |
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## Uses |
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The intended use of BLING models is two-fold: |
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1. Provide high-quality Instruct models that can run on a laptop for local testing. We have found it extremely useful when building a |
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proof-of-concept, or working with sensitive enterprise data that must be closely guarded, especially in RAG use cases. |
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2. Push the state of the art for smaller Instruct-following models in the sub-7B parameter range, especially 1B-3B, as single-purpose |
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automation tools for specific tasks through targeted fine-tuning datasets and focused "instruction" tasks. |
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## Prompt Format |
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``` |
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<human>: Anything that you want to say |
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<bot: |
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``` |
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or |
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``` |
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<human>: Context |
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Instruction/Question |
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<bot: |
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``` |
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### Direct Use |
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BLING is designed for enterprise automation use cases, especially in knowledge-intensive industries, such as financial services, |
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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. |
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BLING is ideal for rapid prototyping, testing, and the ability to perform an end-to-end workflow locally on a laptop without |
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having to send sensitive information over an Internet-based API. |
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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 |
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without the need for a lot of complex instruction verbiage - provide a text passage context, ask questions, and get clear fact-based responses. |
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## Bias, Risks, and Limitations |
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Any model can provide inaccurate or incomplete information, and should be used in conjunction with appropriate safeguards and fact-checking mechanisms. |
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