Model Card for Model ID

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bling-falcon-1b-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.

PERFORMANCE on BASIC RAG TEST DATASET

Model Params (B) Sourcing GPU/CPU Output Tokens Out as % of Input Process Time (secs) Score (0-100)
gpt-4 <=1000 Closed Multi-GPU 2665 10.53% 183.8 100
gpt-3.5-turbo-instruct <=175 Closed Multi-GPU 2621 11.49% 62.7 100
claude-instant-v1 <=50 Closed Multi-GPU 6337 26.50% 154 100
aib-read-gpt 7 Closed GPU 1964 9.30% 114 96
bling_falcon-1b-0.1 1.3 Open CPU 3204 14.55% 696 77
bling_pythia-1.4b-0.1 1.4 Open CPU 2589 11.75% 593.5 65
bling_pythia-1b-0.1 1.0 Open CPU 2753 12.49% 428 59
bling_cerebras-1.3b 1.3 Open CPU 3202 20.01% 690.1 52
bling_pythia_410m 0.41 NA CPU 2349 10.66% 189 36
bling_cerebras_590m 0.59 NA CPU 4407 20.01% 400.8 30

For more details on this evaluation, please see the dataset: llmware/rag_instruct_test_dataset_0.1 and BLOG

Model Description

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Uses

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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

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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

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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.

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-falcon-1b-0.1")
model = AutoModelForCausalLM.from_pretrained("llmware/bling-falcon-1b-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:

  1. Text Passage Context, and
  2. 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 Falcon model base - for more information about the Falcon model, please see the paper referenced below:

@article{refinedweb, title={The {R}efined{W}eb dataset for {F}alcon {LLM}: outperforming curated corpora with web data, and web data only}, author={Guilherme Penedo and Quentin Malartic and Daniel Hesslow and Ruxandra Cojocaru and Alessandro Cappelli and Hamza Alobeidli and Baptiste Pannier and Ebtesam Almazrouei and Julien Launay}, journal={arXiv preprint arXiv:2306.01116}, eprint={2306.01116}, eprinttype = {arXiv}, url={https://arxiv.org/abs/2306.01116}, 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!