本地配置allenai/Molmo-7B-D-0924

本地配置

conda create --name molmo_env \
    python=3.11 \
    pytorch torchvision pytorch-cuda=12.1 \
    cudatoolkit xformers -c pytorch -c nvidia -c xformers \
    -y

conda activate molmo_env


pip install transformers Pillow requests einops

pip install 'accelerate>=0.26.0' bitsandbytes


pip install --no-deps accelerate bitsandbytes


pip install jupyter

👉👉👉如有问题请联系我的徽信 stoeng

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Code

from transformers import AutoModelForCausalLM, AutoProcessor, GenerationConfig
from PIL import Image
import requests

# load the processor
processor = AutoProcessor.from_pretrained(
    'allenai/Molmo-7B-D-0924',
    trust_remote_code=True,
    torch_dtype='auto',
    device_map='auto'
)

# load the model
model = AutoModelForCausalLM.from_pretrained(
    'allenai/Molmo-7B-D-0924',
    trust_remote_code=True,
    torch_dtype='auto',
    device_map='auto'
)

# process the image and text
inputs = processor.process(
    images=[Image.open(requests.get("https://picsum.photos/id/237/536/354", stream=True).raw)],
    text="Describe this image."
)

# move inputs to the correct device and make a batch of size 1
inputs = {k: v.to(model.device).unsqueeze(0) for k, v in inputs.items()}

# generate output; maximum 200 new tokens; stop generation when <|endoftext|> is generated
output = model.generate_from_batch(
    inputs,
    GenerationConfig(max_new_tokens=200, stop_strings="<|endoftext|>"),
    tokenizer=processor.tokenizer
)

# only get generated tokens; decode them to text
generated_tokens = output[0,inputs['input_ids'].size(1):]
generated_text = processor.tokenizer.decode(generated_tokens, skip_special_tokens=True)

# print the generated text
print(generated_text)

# >>>  This image features an adorable black Labrador puppy, captured from a top-down
#      perspective. The puppy is sitting on a wooden deck, which is composed ...

chainlit

pip install chainlit

chainlit run ui.py --host 0.0.0.0

import chainlit as cl
from transformers import AutoModelForCausalLM, AutoProcessor, GenerationConfig
from PIL import Image
import torch

# Load the processor
processor = AutoProcessor.from_pretrained(
    'allenai/Molmo-7B-D-0924',
    trust_remote_code=True,
    torch_dtype='auto',
    device_map='auto'
)

# Load the model
model = AutoModelForCausalLM.from_pretrained(
    'allenai/Molmo-7B-D-0924',
    trust_remote_code=True,
    torch_dtype='auto',
    device_map='auto'
)

@cl.on_chat_start
async def start():
    await cl.Message("欢迎使用图像分析应用!请上传一张图片，然后输入您的问题或描述要求。").send()

@cl.on_message
async def main(message: cl.Message):
    if not message.elements:
        await cl.Message("请先上传一张图片，然后再输入您的问题。").send()
        return

    image = message.elements[0]
    if not image.mime.startswith("image"):
        await cl.Message("请上传一个有效的图片文件。").send()
        return

    user_prompt = message.content
    if not user_prompt:
        user_prompt = "Describe this image."

    await process_image(image.path, user_prompt)

async def process_image(image_path, user_prompt):
    # Process the image
    inputs = processor.process(
        images=[Image.open(image_path)],
        text=user_prompt
    )
    
    # Move inputs to the correct device and make a batch of size 1
    inputs = {k: v.to(model.device).unsqueeze(0) for k, v in inputs.items()}
    
    # Generate output
    output = model.generate_from_batch(
        inputs,
        GenerationConfig(max_new_tokens=200, stop_strings="<|endoftext|>"),
        tokenizer=processor.tokenizer
    )
    
    # Get generated tokens and decode them to text
    generated_tokens = output[0, inputs['input_ids'].size(1):]
    generated_text = processor.tokenizer.decode(generated_tokens, skip_special_tokens=True)
    
    # Send the generated text as a message
    await cl.Message(content=generated_text).send()

if __name__ == "__main__":
    cl.run()

Where is the short-haired man wearing a white shirt, blue jeans, and white shoes?

Where is the woman wearing a black and white striped top and carrying a red bag?