--- 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](https://medium.com/@darrenoberst/evaluating-llm-performance-in-rag-instruct-use-cases-083dc272a31d) ### 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: 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. ## 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 "\ and \ wrapper", so to get the best results, wrap inference entries as: full_prompt = "\\: " + my_prompt + "\n" + "\\:" 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!