124 lines
5.4 KiB
Markdown
124 lines
5.4 KiB
Markdown
---
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license: apache-2.0
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inference: false
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---
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# Model Card for Model ID
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<!-- Provide a quick summary of what the model is/does. -->
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BLING-cerebras-1.3b-0.1 is part of the BLING ("Best Little Instruction-following No-GPU-required") model series, with instruct training on top of the cerebras/Cerebras-GPT-1.3B base.
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BLING models are fine-tuned with distilled high-quality custom instruct datasets, targeted at a specific subset of instruct tasks with
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the objective of providing a high-quality Instruct model that is 'inference-ready' on a CPU laptop even
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without using any advanced quantization optimizations.
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### Model Description
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<!-- Provide a longer summary of what this model is. -->
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- **Developed by:** llmware
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- **Model type:** Instruct-trained GPT decoder
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- **Language(s) (NLP):** English
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- **License:** Apache 2.0
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- **Finetuned from model [optional]:** cerebras/Cerebras-GPT-1.3B
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## Uses
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<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
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The intended use of BLING models is two-fold:
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1. Provide high-quality Instruct models that can run on a laptop for local testing. We have found it extremely useful when building a
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proof-of-concept, or working with sensitive enterprise data that must be closely guarded, especially in RAG use cases.
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2. Push the state of the art for smaller Instruct-following models in the sub-7B parameter range, especially 1B-3B, as single-purpose
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automation tools for specific tasks through targeted fine-tuning datasets and focused "instruction" tasks.
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### Direct Use
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<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
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BLING is designed for enterprise automation use cases, especially in knowledge-intensive industries, such as financial services,
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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.
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BLING is ideal for rapid prototyping, testing, and the ability to perform an end-to-end workflow locally on a laptop without
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having to send sensitive information over an Internet-based API.
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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
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without the need for a lot of complex instruction verbiage - provide a text passage context, ask questions, and get clear fact-based responses.
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## Bias, Risks, and Limitations
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<!-- This section is meant to convey both technical and sociotechnical 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.
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## How to Get Started with the Model
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The fastest way to get started with BLING is through direct import in transformers:
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from transformers import AutoTokenizer, AutoModelForCausalLM
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tokenizer = AutoTokenizer.from_pretrained("llmware/bling-cerebras-1.3b-0.1")
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model = AutoModelForCausalLM.from_pretrained("llmware/bling-cerebras-1.3b-0.1")
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Please refer to the generation_test .py files in the Files repository, which includes 200 samples and script to test the model. The **generation_test_llmware_script.py** includes built-in llmware capabilities for fact-checking, as well as easy integration with document parsing and actual retrieval to swap out the test set for RAG workflow consisting of business documents.
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The BLING model was fine-tuned with a simple "\<human> and \<bot> wrapper", so to get the best results, wrap inference entries as:
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full_prompt = "\<human>\: " + my_prompt + "\n" + "\<bot>\:"
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The BLING model was fine-tuned with closed-context samples, which assume generally that the prompt consists of two sub-parts:
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1. Text Passage Context, and
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2. Specific question or instruction based on the text passage
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To get the best results, package "my_prompt" as follows:
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my_prompt = {{text_passage}} + "\n" + {{question/instruction}}
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If you are using a HuggingFace generation script:
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# prepare prompt packaging used in fine-tuning process
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new_prompt = "<human>: " + entries["context"] + "\n" + entries["query"] + "\n" + "<bot>:"
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inputs = tokenizer(new_prompt, return_tensors="pt")
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start_of_output = len(inputs.input_ids[0])
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# temperature: set at 0.3 for consistency of output
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# max_new_tokens: set at 100 - may prematurely stop a few of the summaries
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outputs = model.generate(
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inputs.input_ids.to(device),
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eos_token_id=tokenizer.eos_token_id,
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pad_token_id=tokenizer.eos_token_id,
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do_sample=True,
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temperature=0.3,
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max_new_tokens=100,
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)
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output_only = tokenizer.decode(outputs[0][start_of_output:],skip_special_tokens=True)
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## Citation [optional]
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This BLING model is built on top of a Cerebras base GPT trained model - for more information about the Cerebras GPT models, please see the following paper:
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{
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Title: Cerebras-GPT: Open Compute-Optimal Language Models Trained on the Cerebras Wafer-Scale Cluster
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Authors: Nolan Dey, Gurpreet Gosal, Zhiming (Charles) Chen, Hemant Khachane, William Marshall, Ribhu Pathria, Marvin Tom, Joe Hestness
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Publication: April 6, 2023
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}
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## Model Card Contact
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Darren Oberst & llmware team
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