This model is for debugging. It is randomly initialized using the config from Qwen/Qwen2-VL-7B-Instruct but with smaller size.
Usage:
from PIL import Image
import requests
import torch
from torchvision import io
from typing import Dict
from transformers import Qwen2VLForConditionalGeneration, AutoTokenizer, AutoProcessor
model_id = "yujiepan/qwen2-vl-tiny-random"
# Load the model in half-precision on the available device(s)
model = Qwen2VLForConditionalGeneration.from_pretrained(
model_id, torch_dtype="auto", device_map="auto"
)
processor = AutoProcessor.from_pretrained(model_id)
# Image
url = "https://qianwen-res.oss-cn-beijing.aliyuncs.com/Qwen-VL/assets/demo.jpeg"
image = Image.open(requests.get(url, stream=True).raw)
conversation = [
{
"role": "user",
"content": [
{
"type": "image",
},
{"type": "text", "text": "Describe this image."},
],
}
]
text_prompt = processor.apply_chat_template(conversation, add_generation_prompt=True)
# Excepted output: '<|im_start|>system\nYou are a helpful assistant.<|im_end|>\n<|im_start|>user\n<|vision_start|><|image_pad|><|vision_end|>Describe this image.<|im_end|>\n<|im_start|>assistant\n'
inputs = processor(
text=[text_prompt], images=[image], padding=True, return_tensors="pt"
)
inputs = inputs.to("cuda")
output_ids = model.generate(**inputs, max_new_tokens=128)
generated_ids = [
output_ids[len(input_ids) :]
for input_ids, output_ids in zip(inputs.input_ids, output_ids)
]
output_text = processor.batch_decode(
generated_ids, skip_special_tokens=True, clean_up_tokenization_spaces=True
)
print(output_text)
Codes:
import os
from typing import Dict
import requests
import torch
import transformers
from PIL import Image
from torchvision import io
from transformers import (AutoConfig, AutoModelForCausalLM, AutoProcessor,
AutoTokenizer, GenerationConfig, pipeline, set_seed)
from transformers.models.qwen2_vl import Qwen2VLForConditionalGeneration
model_id = "Qwen/Qwen2-VL-7B-Instruct"
repo_id = "yujiepan/qwen2-vl-tiny-random"
save_path = f"/tmp/{repo_id}"
config = AutoConfig.from_pretrained(model_id, trust_remote_code=True)
config.hidden_size = 16
config.intermediate_size = 32
config.num_attention_heads = 2
config.num_hidden_layers = 2
config.num_key_value_heads = 1
config.vision_config.embed_dim = 16
config.vision_config.num_heads = 2
config.vision_config.hidden_size = 16
config.vision_config.depth = 2
config.rope_scaling['mrope_section'] = [1, 1, 2] # sum needs to be 4 here
model = Qwen2VLForConditionalGeneration(config=config)
model = model.to(torch.bfloat16).cuda().eval()
model.generation_config = GenerationConfig.from_pretrained(
model_id, trust_remote_code=True,
)
set_seed(42)
with torch.no_grad():
for _, p in sorted(model.named_parameters()):
torch.nn.init.uniform_(p, -0.3, 0.3)
processor = AutoProcessor.from_pretrained(model_id)
model.save_pretrained(save_path)
processor.save_pretrained(save_path)
os.system(f"ls -alh {save_path}")
def try_inference():
url = "https://qianwen-res.oss-cn-beijing.aliyuncs.com/Qwen-VL/assets/demo.jpeg"
image = Image.open(requests.get(url, stream=True).raw)
conversation = [
{
"role": "user",
"content": [
{
"type": "image",
},
{"type": "text", "text": "Describe this image."},
],
}
]
processor = AutoProcessor.from_pretrained(save_path)
model = Qwen2VLForConditionalGeneration.from_pretrained(
save_path, torch_dtype=torch.bfloat16, device_map='cuda')
text_prompt = processor.apply_chat_template(
conversation, add_generation_prompt=True)
inputs = processor(
text=[text_prompt], images=[image], padding=True, return_tensors="pt"
)
inputs = inputs.to("cuda")
output_ids = model.generate(**inputs, max_new_tokens=16)
generated_ids = [
output_ids[len(input_ids):]
for input_ids, output_ids in zip(inputs.input_ids, output_ids)
]
output_text = processor.batch_decode(
generated_ids, skip_special_tokens=True, clean_up_tokenization_spaces=True
)
print(output_text)
try_inference()