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--- |
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license: apache-2.0 |
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inference: false |
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--- |
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# DRAGON-MISTRAL-0.3 |
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<!-- Provide a quick summary of what the model is/does. --> |
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dragon-mistral-0.3 is part of the dRAGon ("Delivering RAG On ...") model series, RAG-instruct trained on top of a Mistral 7b (0.3) 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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1 Test Run (with temperature = 0.0 and sample = False) 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**: **99.5** correct out of 100 |
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--Not Found Classification: 95.0% |
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--Boolean: 82.5% |
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--Math/Logic: 67.5% |
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--Complex Questions (1-5): 4 (Above Average - multiple-choice, causal) |
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--Summarization Quality (1-5): 4 (Above Average) |
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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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Please note that these test results were achieved using the 4_K_M quantized version of this model - [dragon-mistral-0.3-gguf](https://www.huggingface.co/llmware/dragon-mistral-0.3-gguf). |
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Note: compare results with [dragon-mistral-7b](https://www.huggingface.co/llmware/dragon-mistral-7b-v0). |
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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:** mistral |
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- **Language(s) (NLP):** English |
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- **License:** Apache 2.0 |
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- **Finetuned from model:** Mistral-7b-base-0.3 |
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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-mistral-0.3") |
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model = AutoModelForCausalLM.from_pretrained("dragon-mistral-0.3") |
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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 dRAGon 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 |
