--- license: other license_name: microsoft-research-license license_link: https://huggingface.co/microsoft/phi-1_5/blob/main/Research%20License.docx inference: false --- # Model Card for Model ID bling-phi-1_5b-v0 is part of the BLING ("Best Little Instruct No GPU required") model series, RAG-instruct trained on top of a Microsoft Phi-1.5B base model. BLING models are fine-tuned with high-quality custom instruct datasets, designed for rapid protyping in RAG scenarios. **Please note that use of this model is subject to the [Microsoft Research License](https://huggingface.co/microsoft/phi-1_5/blob/main/Research%20License.docx) - and may not be used for any commercial purpose. We are providing our fine-tuned version, including the benchmarking results, solely for purpose of research and advancing insights on the performance of smaller models in RAG scenarios - it provides another point of comparison with other similarly sized and similarly finetuned BLING models.** For models with comparable performance and open source licenses that permit commercial use cases in RAG deployments, please see: [**bling-falcon-1b-0.1**](https://huggingface.co/llmware/bling-falcon-1b-0.1) [**bling-sheared-llama-1.3b-0.1**](https://huggingface.co/llmware/bling-sheared-llama-1.3b-0.1) [**bling-1b-0.1**](https://huggingface.co/llmware/bling-1b-0.1) [**bling-1.4b-0.1**](https://huggingface.co/llmware/bling-1.4b-0.1) [**bling-tiny-llama-v0**](https://huggingface.co/llmware/bling-tiny-llama-v0) ### 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**: **87.75** correct out of 100 --Not Found Classification: 47.50% --Boolean: 80.00% --Math/Logic: 53.75% --Complex Questions (1-5): 3 (Average-to-Low) --Summarization Quality (1-5): 3 (Coherent, extractive) --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 - **Developed by:** llmware - **Model type:** Phi-1.5B - **Language(s) (NLP):** English - **License:** [Microsoft Research License](https://huggingface.co/microsoft/phi-1_5/blob/main/Research%20License.docx) - **Finetuned from model:** Microsoft Phi-1.5B ## Uses 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 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 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 dRAGon is through direct import in transformers: from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("bling-phi-1_5b-v0", trust_remote_code=True) model = AutoModelForCausalLM.from_pretrained("bling-phi-1_5b-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 BLING model was fine-tuned with a simple "\ and \ wrapper", so to get the best results, wrap inference entries as: full_prompt = ": " + my_prompt + "\n" + ":" 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 = ": " + entries["context"] + "\n" + entries["query"] + "\n" + ":" 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