|
--- |
|
library_name: transformers |
|
pipeline_tag: text-generation |
|
inference: true |
|
widget: |
|
- text: Hello! |
|
example_title: Hello world |
|
group: Python |
|
base_model: |
|
- Qwen/Qwen2-VL-7B-Instruct |
|
--- |
|
|
|
This model is for debugging. It is randomly initialized using the config from [Qwen/Qwen2-VL-7B-Instruct](https://huggingface.co/Qwen/Qwen2-VL-7B-Instruct) but with smaller size. |
|
|
|
Usage: |
|
```python |
|
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: |
|
```python |
|
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() |
|
``` |