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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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# bling-phi-3 |
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<!-- Provide a quick summary of what the model is/does. --> |
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bling-phi-3 is part of the BLING ("Best Little Instruct No-GPU") model series, RAG-instruct trained on top of a Microsoft Phi-3 base model. |
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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 (temperature=0.0, 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: 97.5% |
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--Math/Logic: 80.0% |
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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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Note: compare results with [bling-phi-2](https://www.huggingface.co/llmware/bling-phi-2-v0), and [dragon-mistral-7b](https://www.huggingface.co/llmware/dragon-mistral-7b-v0). |
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Note: see also the quantized gguf version of the model- [bling-phi-3-gguf](https://www.huggingface.co/llmware/bling-phi-3-gguf). |
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Note: the Pytorch version answered 1 question with "Not Found" while the quantized version answered it correctly, hence the small difference in scores. |
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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:** bling |
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- **Language(s) (NLP):** English |
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- **License:** Apache 2.0 |
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- **Finetuned from model:** Microsoft Phi-3 |
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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 RAG-Instruct models designed for fact-based, no "hallucination" question-answering in connection with an enterprise RAG workflow. |
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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. |
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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. |
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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-phi-3", trust_remote_code=True) |
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model = AutoModelForCausalLM.from_pretrained("llmware/bling-phi-3", trust_remote_code=True) |
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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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(As an aside, we intended to retire "human-bot" and tried several variations of the new Microsoft Phi-3 prompt template and ultimately had slightly better results with the very simple "human-bot" separators, so we opted to keep them.) |
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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 0.0 with do_sample=False 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=False, |
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temperature=0.0, |
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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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