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Delete sample_inference.py
Browse files- sample_inference.py +0 -108
sample_inference.py
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from PIL import Image
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import requests
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import torch
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from transformers import AutoModelForCausalLM
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from transformers import AutoProcessor
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model_path = "./"
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kwargs = {}
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kwargs['torch_dtype'] = torch.bfloat16
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processor = AutoProcessor.from_pretrained(model_path, trust_remote_code=True)
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model = AutoModelForCausalLM.from_pretrained(model_path, trust_remote_code=True, torch_dtype="auto").cuda()
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user_prompt = '<|user|>\n'
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assistant_prompt = '<|assistant|>\n'
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prompt_suffix = "<|end|>\n"
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#################################################### text-only ####################################################
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prompt = f"{user_prompt}what is the answer for 1+1? Explain it.{prompt_suffix}{assistant_prompt}"
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print(f">>> Prompt\n{prompt}")
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inputs = processor(prompt, images=None, return_tensors="pt").to("cuda:0")
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generate_ids = model.generate(**inputs,
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max_new_tokens=1000,
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eos_token_id=processor.tokenizer.eos_token_id,
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)
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generate_ids = generate_ids[:, inputs['input_ids'].shape[1]:]
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response = processor.batch_decode(generate_ids,
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skip_special_tokens=True,
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clean_up_tokenization_spaces=False)[0]
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print(f'>>> Response\n{response}')
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#################################################### text-only 2 ####################################################
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prompt = f"{user_prompt}Give me the code for sloving two-sum problem.{prompt_suffix}{assistant_prompt}"
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print(f">>> Prompt\n{prompt}")
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inputs = processor(prompt, images=None, return_tensors="pt").to("cuda:0")
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generate_ids = model.generate(**inputs,
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max_new_tokens=1000,
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eos_token_id=processor.tokenizer.eos_token_id,
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)
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generate_ids = generate_ids[:, inputs['input_ids'].shape[1]:]
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response = processor.batch_decode(generate_ids,
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skip_special_tokens=True,
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clean_up_tokenization_spaces=False)[0]
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print(f'>>> Response\n{response}')
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#################################################### EXAMPLE 1 ####################################################
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# single-image prompt
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prompt = f"{user_prompt}<|image_1|>\nWhat is shown in this image?{prompt_suffix}{assistant_prompt}"
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url = "https://www.ilankelman.org/stopsigns/australia.jpg"
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print(f">>> Prompt\n{prompt}")
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image = Image.open(requests.get(url, stream=True).raw)
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inputs = processor(prompt, image, return_tensors="pt").to("cuda:0")
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generate_ids = model.generate(**inputs,
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max_new_tokens=1000,
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eos_token_id=processor.tokenizer.eos_token_id,
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)
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generate_ids = generate_ids[:, inputs['input_ids'].shape[1]:]
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response = processor.batch_decode(generate_ids,
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skip_special_tokens=True,
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clean_up_tokenization_spaces=False)[0]
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print(f'>>> Response\n{response}')
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#################################################### EXAMPLE 2 ####################################################
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# chat template
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chat = [
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{"role": "user", "content": "<|image_1|>\nWhat is shown in this image?"},
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{"role": "assistant", "content": "The image depicts a street scene with a prominent red stop sign in the foreground. The background showcases a building with traditional Chinese architecture, characterized by its red roof and ornate decorations. There are also several statues of lions, which are common in Chinese culture, positioned in front of the building. The street is lined with various shops and businesses, and there's a car passing by."},
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{"role": "user", "content": "What is so special about this image"}
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]
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url = "https://www.ilankelman.org/stopsigns/australia.jpg"
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image = Image.open(requests.get(url, stream=True).raw)
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prompt = processor.tokenizer.apply_chat_template(chat, tokenize=False, add_generation_prompt=True)
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# need to remove last <|endoftext|> if it is there, which is used for training, not inference. For training, make sure to add <|endoftext|> in the end.
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if prompt.endswith("<|endoftext|>"):
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prompt = prompt.rstrip("<|endoftext|>")
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print(f">>> Prompt\n{prompt}")
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inputs = processor(prompt, [image], return_tensors="pt").to("cuda:0")
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generate_ids = model.generate(**inputs,
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max_new_tokens=1000,
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eos_token_id=processor.tokenizer.eos_token_id,
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)
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generate_ids = generate_ids[:, inputs['input_ids'].shape[1]:]
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response = processor.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0]
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print(f'>>> Response\n{response}')
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############################# to markdown #############################
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# single-image prompt
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prompt = f"{user_prompt}<|image_1|>\nCan you convert the table to markdown format?{prompt_suffix}{assistant_prompt}"
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url = "https://support.content.office.net/en-us/media/3dd2b79b-9160-403d-9967-af893d17b580.png"
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image = Image.open(requests.get(url, stream=True).raw)
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inputs = processor(prompt, image, return_tensors="pt").to("cuda:0")
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print(f">>> Prompt\n{prompt}")
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generate_ids = model.generate(**inputs,
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max_new_tokens=1000,
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eos_token_id=processor.tokenizer.eos_token_id,
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)
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generate_ids = generate_ids[:, inputs['input_ids'].shape[1]:]
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response = processor.batch_decode(generate_ids,
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skip_special_tokens=False,
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clean_up_tokenization_spaces=False)[0]
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print(f'>>> Response\n{response}')
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