| | --- |
| | license: apache-2.0 |
| | inference: false |
| | --- |
| | |
| | # dragon-phi-3-answer-tool |
| |
|
| | <!-- Provide a quick summary of what the model is/does. --> |
| |
|
| | dragon-phi-3-answer-tool is part of the DRAGON ("Delivering RAG On ...") model series, RAG-instruct trained on top of a Microsoft Phi-3 base model. |
| |
|
| | DRAGON models are fine-tuned with high-quality custom instruct datasets, designed for production use in RAG scenarios. |
| |
|
| |
|
| | ### Benchmark Tests |
| |
|
| | Evaluated against the benchmark test: [RAG-Instruct-Benchmark-Tester](https://www.huggingface.co/datasets/llmware/rag_instruct_benchmark_tester) |
| | 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. |
| |
|
| | --**Accuracy Score**: **100.0** correct out of 100 |
| | --Not Found Classification: 95.0% |
| | --Boolean: 97.5% |
| | --Math/Logic: 80.0% |
| | --Complex Questions (1-5): 4 (Above Average - multiple-choice, causal) |
| | --Summarization Quality (1-5): 4 (Above Average) |
| | --Hallucinations: No hallucinations observed in test runs. |
| |
|
| | For test run results (and good indicator of target use cases), please see the files ("core_rag_test" and "answer_sheet" in this repo). |
| | |
| | ### Model Description |
| | |
| | <!-- Provide a longer summary of what this model is. --> |
| | |
| | - **Developed by:** llmware |
| | - **Model type:** Dragon |
| | - **Language(s) (NLP):** English |
| | - **License:** Apache 2.0 |
| | - **Finetuned from model:** Microsoft Phi-3 |
| | |
| | ## Uses |
| | |
| | <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> |
| | |
| | The intended use of BLING models is two-fold: |
| | |
| | 1. Provide high-quality RAG-Instruct models designed for fact-based, no "hallucination" question-answering in connection with an enterprise RAG workflow. |
| | |
| | 2. BLING models are fine-tuned on top of leading base foundation models, generally in the 1-3B+ range, and purposefully rolled-out across multiple base models to provide choices and "drop-in" replacements for RAG specific use cases. |
| | |
| | |
| | ### 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 with complex information sources. |
| | |
| | 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 |
| | |
| | <!-- This section is meant to convey both technical and sociotechnical 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("bling-phi-2-v0", trust_remote_code=True) |
| | model = AutoModelForCausalLM.from_pretrained("bling-phi-2-v0", trust_remote_code=True) |
| | |
| | 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. |
| | |
| | The dRAGon 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 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}} |
| | |
| |
|
| | If you are using a HuggingFace generation script: |
| |
|
| | # prepare prompt packaging used in fine-tuning process |
| | new_prompt = "<human>: " + entries["context"] + "\n" + entries["query"] + "\n" + "<bot>:" |
| | |
| | inputs = tokenizer(new_prompt, return_tensors="pt") |
| | start_of_output = len(inputs.input_ids[0]) |
| | |
| | # temperature: set at 0.3 for consistency of output |
| | # max_new_tokens: set at 100 - may prematurely stop a few of the summaries |
| | |
| | outputs = model.generate( |
| | inputs.input_ids.to(device), |
| | eos_token_id=tokenizer.eos_token_id, |
| | pad_token_id=tokenizer.eos_token_id, |
| | do_sample=True, |
| | temperature=0.3, |
| | max_new_tokens=100, |
| | ) |
| | |
| | output_only = tokenizer.decode(outputs[0][start_of_output:],skip_special_tokens=True) |
| | |
| |
|
| | ## Model Card Contact |
| |
|
| | Darren Oberst & llmware team |
| |
|
