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 falcon-rw-1b 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: GPTNeoX 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:
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.
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
BLING has not been designed for end consumer-oriented applications, and there has not been any focus in training on safeguards to mitigate potential bias. We would strongly discourage any use of BLING for any 'chatbot' use case.
How to Get Started with the Model
The fastest way to get started with BLING is through direct import in transformers:
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("llmware/bling-sheared-llama-1.3b-0.1")
model = AutoModelForCausalLM.from_pretrained("llmware/bling-sheared-llama-1.3b-0.1")
The BLING model was fine-tuned with a simple "<human> and <bot> wrapper", so to get the best results, wrap inference entries as:
full_prompt = "<human>: " + my_prompt + "\n" + "<bot>: "
The BLING model was fine-tuned with closed-context samples, which assume generally that the prompt consists of two sub-parts:
- Text Passage Context, and
- Specific question or instruction based on the text passage
To get the best results, package "my_prompt" as follows:
my_prompt = {{text_passage}} + "\n" + {{question/instruction}}
Citation [optional]
This BLING model was built on top of a "Sheared Llama" model base - for more information about the "Sheared Llama" model, please see the paper referenced below:
@article{xia2023sheared, title={Sheared LLaMA: Accelerating Language Model Pre-training via Structured Pruning}, author={Xia, Mengzhou and Gao, Tianyu, and Zeng Zhiyuan, and Chen Danqi}, year={2023} }
Model Card Contact
Darren Oberst & llmware team
Please reach out anytime if you are interested in this project and would like to participate and work with us!