license: apache-2.0
Model Card for Model ID
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
- Developed by: llmware
- Model type: GPTNeoX instruct-trained decoder
- Language(s) (NLP): English
- License: Apache 2.0
- Finetuned from model [optional]: tiiuae/falcon-rw-1b
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
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:
- 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 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!