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from transformers import AutoModelForCausalLM, AutoTokenizer |
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import torch |
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def load_rag_benchmark_tester_ds(): |
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from datasets import load_dataset |
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ds_name = "llmware/rag_instruct_benchmark_tester" |
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dataset = load_dataset(ds_name) |
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print("update: loading RAG Benchmark test dataset - ", dataset) |
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test_set = [] |
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for i, samples in enumerate(dataset["train"]): |
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test_set.append(samples) |
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return test_set |
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def run_test(model_name, test_ds): |
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device = "cuda" if torch.cuda.is_available() else "cpu" |
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print("\nRAG Performance Test - 200 questions") |
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print("update: model - ", model_name) |
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print("update: device - ", device) |
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model = AutoModelForCausalLM.from_pretrained(model_name, trust_remote_code=True) |
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model.to(device) |
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tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True) |
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for i, entries in enumerate(test_ds): |
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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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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=True, |
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temperature=0.3, |
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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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eot = output_only.find("<|endoftext|>") |
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if eot > -1: |
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output_only = output_only[:eot] |
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bot = output_only.find("<bot>:") |
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if bot > -1: |
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output_only = output_only[bot+len("<bot>:"):] |
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print("\n") |
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print(i, "llm_response - ", output_only) |
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print(i, "gold_answer - ", entries["answer"]) |
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return 0 |
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if __name__ == "__main__": |
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test_ds = load_rag_benchmark_tester_ds() |
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model_name = "llmware/bling-stable-lm-3b-4e1t-v0" |
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output = run_test(model_name,test_ds) |
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