microsoft

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Phi-3-vision-128k-instruct

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Microsoft 7.42k

I'm using TextStreamer just fine.
I will share my code for you

from transformers import TextStreamer
from PIL import Image
import torch
from transformers import AutoModelForCausalLM, AutoProcessor
import requests
from io import BytesIO
import argparse


DEFAULT_IMAGE_TOKEN = "<|image_1|>"

def load_image(image_file):
    if image_file.startswith('http://') or image_file.startswith('https://'):
        response = requests.get(image_file)
        image = Image.open(BytesIO(response.content)).convert("RGB")
    else:
        image = Image.open(image_file).convert("RGB")
    return image

def main(args):

    model_id = args.model_base
    model = AutoModelForCausalLM.from_pretrained(model_id, device_map=args.device, trust_remote_code=True, torch_dtype=torch.float16)

    if args.model_path:
        peft_model_id = args.model_path
        model.load_adapter(peft_model_id)

    processor = AutoProcessor.from_pretrained(model_id, trust_remote_code=True)

    messages = [
    {"role": "system", "content": "You are a helpful AI assistant. Provide a useful information about the given image."},
    ]

    image = load_image(args.image_file)

    generation_args = {
        "max_new_tokens": args.max_new_tokens,
        "temperature": args.temperature,
        "do_sample": True if args.temperature > 0 else False,
        "repetition_penalty": args.repetition_penalty,
    }

    while True:
        try:
            inp = input(f"User: ")
        except EOFError:
            inp = ""
        if not inp:
            print("exit...")
            break

        print(f"Assistant: ", end="")

        if image is not None and len(messages) < 2:
            # only putting the image token in the first turn of user.
            # You could just uncomment the system messages or use it.
            inp = DEFAULT_IMAGE_TOKEN + '\n' + inp

        messages.append({"role": "user", "content": inp})

        prompt = processor.tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
        inputs = processor(prompt, image, return_tensors="pt").to(args.device)

        streamer = TextStreamer(processor.tokenizer, skip_prompt=True, skip_special_tokens=True)

        with torch.inference_mode():
            generate_ids = model.generate(
                **inputs, 
                eos_token_id=processor.tokenizer.eos_token_id, 
                streamer=streamer,
                **generation_args,
                use_cache=True
            )

        outputs = processor.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0]
        messages.append({"role":"assistant", "content": outputs})

        if args.debug:
            print("\n", {"prompt": prompt, "outputs": outputs}, "\n")


if __name__ == "__main__":
    parser = argparse.ArgumentParser()
    parser.add_argument("--model-path", type=str, default=None)
    parser.add_argument("--model-base", type=str, default="microsoft/Phi-3-vision-128k-instruct")
    parser.add_argument("--image-file", type=str, required=True)
    parser.add_argument("--device", type=str, default="cuda")
    parser.add_argument("--temperature", type=float, default=0)
    parser.add_argument("--repetition-penalty", type=float, default=1.0)
    parser.add_argument("--max-new-tokens", type=int, default=500)
    parser.add_argument("--debug", action="store_true")
    args = parser.parse_args()
    main(args)

Thanks a lot, I think my problem was I forgot to set the skip_special_tokens=True

Thanks @2U1 , you've helped me as well. I wasn't sure how to add streaming with Transformers, but your example is clear.