README.md

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license: apache-2.0
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# Model Card for Model ID

<!-- Provide a quick summary of what the model is/does. -->

BLING-1.4b-0.1 is the first model release in the BLING ("Best Little Instruction-following No-GPU-required") model series.

BLING models are designed as custom instruct-following laptop-effective GPT decoder-based models (~1B-2.7B parameters). BLING models are currently built on top of Pythia (GPTNeox architecture) base models and other Apache 2.0-licensed GPT-compatible models with primary focus on 'little' models in the range of 1B, 1.3-1.4B, and 2.7B parameters. (Note: in our testing, we have seen relatively limited success with instruct-following models below <1B parameters.)

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 can be run entirely without a GPU server, with good quality instruct-following capability that can be loaded and run locally on a laptop.

## Model Details

### Model Description

<!-- Provide a longer summary of what this model is. -->

- **Developed by:** llmware
- **Shared by [optional]:** Darren Oberst
- **Model type:** GPTNeoX instruct-trained decoder
- **Language(s) (NLP):** English
- **License:** Apache 2.0
- **Finetuned from model [optional]:** EleutherAI/Pythia-1b-deduped

## Uses

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The intended use of BLING models is two-fold:

1.  Provide a 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 1B - 7B range through improved fine-tuning datasets and targeted "instruction" tasks.


### Direct Use

<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->

BLING is designed for enterprise automation use cases, especially in knowledge-intensive industries, such as financial services,
legal and regulatory industries.   BLING is intended to be an experimental series of little instruct models targeted as specific
RAG automation tasks 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 on question-answering, key-value extraction, and basic summarization as the core instruction types.


[More Information Needed]

### Downstream Use [optional]

<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->

[More Information Needed]

### Out-of-Scope Use

<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->

1.  BLING is not designed for 'chat-bot' or 'consumer-oriented' applications. 

2.  BLING is not optimal for most production applications, other than simple and highly specific use cases.


[More Information Needed]

## Bias, Risks, and Limitations

<!-- This section is meant to convey both technical and sociotechnical limitations. -->

BLING has not been designed for end consumer-oriented applications, and there has been any focus in training on important safeguards to
mitigate potential bias and safety.    We would strongly discourage any use of BLING for any 'chatbot' use case.

[More Information Needed]


## How to Get Started with the Model

The fastest way to get started with BLING is through direct import in transformers:

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

tokenizer = AutoTokenizer.from_pretrained("llmware/bling-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 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]

<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->

## Model Card Contact

Darren Oberst & llmware team

Please reach out anytime if you are interested in this research program and would like to participate and work with us!