from llmware.prompts import Prompt def load_rag_benchmark_tester_ds(): # pull 200 question rag benchmark test dataset from LLMWare HuggingFace repo from datasets import load_dataset ds_name = "llmware/rag_instruct_benchmark_tester" dataset = load_dataset(ds_name) print("update: loading RAG Benchmark test dataset - ", dataset) test_set = [] for i, samples in enumerate(dataset["train"]): test_set.append(samples) # to view test set samples # print("rag benchmark dataset test samples: ", i, samples) return test_set def run_test(model_name, prompt_list): print("\nupdate: Starting RAG Benchmark Inference Test - ", model_name) # pull DRAGON / BLING model directly from catalog, e.g., no from_hf=True prompter = Prompt().load_model(model_name) for i, entries in enumerate(prompt_list): prompt = entries["query"] context = entries["context"] response = prompter.prompt_main(prompt,context=context,prompt_name="default_with_context", temperature=0.3) print("\nupdate: model inference output - ", i, response["llm_response"]) print("update: gold_answer - ", i, entries["answer"]) fc = prompter.evidence_check_numbers(response) sc = prompter.evidence_comparison_stats(response) sr = prompter.evidence_check_sources(response) print("\nFact-Checking Tools") for entries in fc: for f, facts in enumerate(entries["fact_check"]): print("update: fact check - ", f, facts) for entries in sc: print("update: comparison stats - ", entries["comparison_stats"]) for entries in sr: for s, sources in enumerate(entries["source_review"]): print("update: sources - ", s, sources) return 0 if __name__ == "__main__": core_test_set = load_rag_benchmark_tester_ds() # one of the 7 gpu dragon models gpu_model_name = "llmware/dragon-llama-7b-v0" output = run_test(gpu_model_name, core_test_set)