# Falkor 7B - RAG (dragon) Model

Model merge between Chupacabra 7b v2.04 and dragon-mistral-7b-v0 - ---> [Theme Song](https://www.youtube.com/watch?v=lHytjEj7B9g) <--- # Original Model Card for dragon-mistral-7b-v0 dragon-mistral-7b-v0 part of the dRAGon ("Delivering RAG On ...") model series, RAG-instruct trained on top of a Mistral-7B base model. 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. ### 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**: **96.50** correct out of 100 --Not Found Classification: 92.50% --Boolean: 97.50% --Math/Logic: 81.25% --Complex Questions (1-5): 4 (Medium-High - table-reading, multiple-choice, causal) --Summarization Quality (1-5): 4 (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:** Mistral-7B - **Language(s) (NLP):** English - **License:** Apache 2.0 - **Finetuned from model:** Mistral-7B-Base ### Direct Use DRAGON is designed for enterprise automation use cases, especially in knowledge-intensive industries, such as financial services, legal and regulatory industries with complex information sources. DRAGON 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("dragon-mistral-7b-v0") model = AutoModelForCausalLM.from_pretrained("dragon-mistral-7b-v0") 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 "\ 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 # [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard) Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_perlthoughts__Falkor-7b) | Metric |Value| |---------------------------------|----:| |Avg. |70.33| |AI2 Reasoning Challenge (25-Shot)|68.26| |HellaSwag (10-Shot) |85.84| |MMLU (5-Shot) |63.98| |TruthfulQA (0-shot) |63.08| |Winogrande (5-shot) |80.35| |GSM8k (5-shot) |60.50|

Full Model Card (README)