---
license: apache-2.0
---

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)





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.