106 lines
4.4 KiB
Markdown
106 lines
4.4 KiB
Markdown
---
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license: apache-2.0
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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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dragon-falcon-7b-v0 part of the dRAGon ("Delivering RAG On ...") model series, RAG-instruct trained on top of a Falcon-7B base model.
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DRAGON models have been fine-tuned with the specific objective of fact-based question-answering over complex business and legal documents with an emphasis on reducing hallucinations and providing short, clear answers for workflow automation.
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### Benchmark Tests
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Evaluated against the benchmark test: [RAG-Instruct-Benchmark-Tester](https://www.huggingface.co/datasets/llmware/rag_instruct_benchmark_tester)
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Average of 2 Test Runs with 1 point for correct answer, 0.5 point for partial correct or blank / NF, 0.0 points for incorrect, and -1 points for hallucinations.
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--**Accuracy Score**: **94** correct out of 100
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--Not Found Classification: 75.0%
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--Boolean: 81.25%
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--Math/Logic: 66.75%
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--Complex Questions (1-5): 3 (Medium)
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--Summarization Quality (1-5): 3 (Coherent, extractive)
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--Hallucinations: No hallucinations observed in test runs.
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For test run results (and good indicator of target use cases), please see the files ("core_rag_test" and "answer_sheet" in this repo).
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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:** Falcon
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- **Language(s) (NLP):** English
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- **License:** Apache 2.0
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- **Finetuned from model:** Falcon-7B-Base
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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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DRAGON 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.
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DRAGON 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 dRAGon is through direct import in transformers:
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from transformers import AutoTokenizer, AutoModelForCausalLM
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tokenizer = AutoTokenizer.from_pretrained("dragon-falcon-7b-v0")
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model = AutoModelForCausalLM.from_pretrained("dragon-falcon-7b-v0")
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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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## Model Card Contact
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Darren Oberst & llmware team
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