Instructions to use hf-internal-testing/tiny-random-Qwen25VLForCondGen with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hf-internal-testing/tiny-random-Qwen25VLForCondGen with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-text-to-text", model="hf-internal-testing/tiny-random-Qwen25VLForCondGen") messages = [ { "role": "user", "content": [ {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"}, {"type": "text", "text": "What animal is on the candy?"} ] }, ] pipe(text=messages)# Load model directly from transformers import AutoProcessor, AutoModelForImageTextToText processor = AutoProcessor.from_pretrained("hf-internal-testing/tiny-random-Qwen25VLForCondGen") model = AutoModelForImageTextToText.from_pretrained("hf-internal-testing/tiny-random-Qwen25VLForCondGen") messages = [ { "role": "user", "content": [ {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"}, {"type": "text", "text": "What animal is on the candy?"} ] }, ] inputs = processor.apply_chat_template( messages, add_generation_prompt=True, tokenize=True, return_dict=True, return_tensors="pt", ).to(model.device) outputs = model.generate(**inputs, max_new_tokens=40) print(processor.decode(outputs[0][inputs["input_ids"].shape[-1]:])) - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use hf-internal-testing/tiny-random-Qwen25VLForCondGen with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "hf-internal-testing/tiny-random-Qwen25VLForCondGen" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "hf-internal-testing/tiny-random-Qwen25VLForCondGen", "messages": [ { "role": "user", "content": [ { "type": "text", "text": "Describe this image in one sentence." }, { "type": "image_url", "image_url": { "url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg" } } ] } ] }'Use Docker
docker model run hf.co/hf-internal-testing/tiny-random-Qwen25VLForCondGen
- SGLang
How to use hf-internal-testing/tiny-random-Qwen25VLForCondGen with SGLang:
Install from pip and serve model
# Install SGLang from pip: pip install sglang # Start the SGLang server: python3 -m sglang.launch_server \ --model-path "hf-internal-testing/tiny-random-Qwen25VLForCondGen" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "hf-internal-testing/tiny-random-Qwen25VLForCondGen", "messages": [ { "role": "user", "content": [ { "type": "text", "text": "Describe this image in one sentence." }, { "type": "image_url", "image_url": { "url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg" } } ] } ] }'Use Docker images
docker run --gpus all \ --shm-size 32g \ -p 30000:30000 \ -v ~/.cache/huggingface:/root/.cache/huggingface \ --env "HF_TOKEN=<secret>" \ --ipc=host \ lmsysorg/sglang:latest \ python3 -m sglang.launch_server \ --model-path "hf-internal-testing/tiny-random-Qwen25VLForCondGen" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "hf-internal-testing/tiny-random-Qwen25VLForCondGen", "messages": [ { "role": "user", "content": [ { "type": "text", "text": "Describe this image in one sentence." }, { "type": "image_url", "image_url": { "url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg" } } ] } ] }' - Docker Model Runner
How to use hf-internal-testing/tiny-random-Qwen25VLForCondGen with Docker Model Runner:
docker model run hf.co/hf-internal-testing/tiny-random-Qwen25VLForCondGen
Upload tokenizer
Browse files- added_tokens.json +16 -0
- chat_template.jinja +7 -0
- merges.txt +0 -0
- special_tokens_map.json +31 -0
- tokenizer_config.json +144 -0
- vocab.json +0 -0
added_tokens.json
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{
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"<|box_end|>": 151649,
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"<|box_start|>": 151648,
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"<|endoftext|>": 151643,
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"<|im_end|>": 151645,
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"<|im_start|>": 151644,
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"<|image_pad|>": 151655,
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"<|object_ref_end|>": 151647,
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"<|object_ref_start|>": 151646,
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"<|quad_end|>": 151651,
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"<|quad_start|>": 151650,
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"<|video_pad|>": 151656,
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"<|vision_end|>": 151653,
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"<|vision_pad|>": 151654,
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"<|vision_start|>": 151652
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}
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chat_template.jinja
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{% set image_count = namespace(value=0) %}{% set video_count = namespace(value=0) %}{% for message in messages %}{% if loop.first and message['role'] != 'system' %}<|im_start|>system
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You are a helpful assistant.<|im_end|>
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{% endif %}<|im_start|>{{ message['role'] }}
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{% if message['content'] is string %}{{ message['content'] }}<|im_end|>
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{% else %}{% for content in message['content'] %}{% if content['type'] == 'image' or 'image' in content or 'image_url' in content %}{% set image_count.value = image_count.value + 1 %}{% if add_vision_id %}Picture {{ image_count.value }}: {% endif %}<|vision_start|><|image_pad|><|vision_end|>{% elif content['type'] == 'video' or 'video' in content %}{% set video_count.value = video_count.value + 1 %}{% if add_vision_id %}Video {{ video_count.value }}: {% endif %}<|vision_start|><|video_pad|><|vision_end|>{% elif 'text' in content %}{{ content['text'] }}{% endif %}{% endfor %}<|im_end|>
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{% endif %}{% endfor %}{% if add_generation_prompt %}<|im_start|>assistant
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{% endif %}
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merges.txt
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The diff for this file is too large to render.
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special_tokens_map.json
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{
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"additional_special_tokens": [
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"<|im_start|>",
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"<|im_end|>",
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"<|object_ref_start|>",
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"<|object_ref_end|>",
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"<|box_start|>",
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"<|box_end|>",
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"<|quad_start|>",
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"<|quad_end|>",
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"<|vision_start|>",
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"<|vision_end|>",
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"<|vision_pad|>",
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"<|image_pad|>",
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"<|video_pad|>"
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],
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"eos_token": {
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"content": "<|im_end|>",
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"lstrip": false,
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"normalized": false,
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"rstrip": false,
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"single_word": false
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},
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"pad_token": {
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"content": "<|endoftext|>",
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"lstrip": false,
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"normalized": false,
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"rstrip": false,
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"single_word": false
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}
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}
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tokenizer_config.json
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{
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"add_prefix_space": false,
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"added_tokens_decoder": {
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"151643": {
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"content": "<|endoftext|>",
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"lstrip": false,
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"normalized": false,
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"rstrip": false,
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"single_word": false,
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"special": true
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},
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"151644": {
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"content": "<|im_start|>",
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"lstrip": false,
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"normalized": false,
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"rstrip": false,
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"single_word": false,
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"special": true
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},
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"151645": {
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"content": "<|im_end|>",
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"lstrip": false,
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"normalized": false,
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"rstrip": false,
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"single_word": false,
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"special": true
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},
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"151646": {
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"content": "<|object_ref_start|>",
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"lstrip": false,
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"normalized": false,
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"rstrip": false,
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"single_word": false,
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"special": true
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},
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"151647": {
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"content": "<|object_ref_end|>",
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"lstrip": false,
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"normalized": false,
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"rstrip": false,
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"single_word": false,
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"special": true
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},
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"151648": {
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"content": "<|box_start|>",
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"lstrip": false,
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"normalized": false,
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"rstrip": false,
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"single_word": false,
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"special": true
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},
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"151649": {
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"content": "<|box_end|>",
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"lstrip": false,
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"normalized": false,
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"rstrip": false,
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"single_word": false,
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"special": true
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},
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"151650": {
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"content": "<|quad_start|>",
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"lstrip": false,
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"normalized": false,
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"rstrip": false,
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"single_word": false,
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"special": true
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},
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"151651": {
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"content": "<|quad_end|>",
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"lstrip": false,
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"normalized": false,
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"rstrip": false,
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"single_word": false,
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"special": true
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},
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"151652": {
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"content": "<|vision_start|>",
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"lstrip": false,
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"normalized": false,
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| 80 |
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"rstrip": false,
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"single_word": false,
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"special": true
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},
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"151653": {
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"content": "<|vision_end|>",
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"lstrip": false,
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"normalized": false,
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"rstrip": false,
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"single_word": false,
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"special": true
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},
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"151654": {
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"content": "<|vision_pad|>",
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"lstrip": false,
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"normalized": false,
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"rstrip": false,
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"single_word": false,
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"special": true
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},
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"151655": {
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"content": "<|image_pad|>",
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"lstrip": false,
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"normalized": false,
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"rstrip": false,
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"single_word": false,
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"special": true
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},
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"151656": {
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"content": "<|video_pad|>",
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"lstrip": false,
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"normalized": false,
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"rstrip": false,
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"single_word": false,
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"special": true
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}
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},
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"additional_special_tokens": [
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| 118 |
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"<|im_start|>",
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"<|im_end|>",
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| 120 |
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"<|object_ref_start|>",
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| 121 |
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"<|object_ref_end|>",
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| 122 |
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"<|box_start|>",
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| 123 |
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"<|box_end|>",
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| 124 |
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"<|quad_start|>",
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| 125 |
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"<|quad_end|>",
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| 126 |
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"<|vision_start|>",
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| 127 |
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"<|vision_end|>",
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| 128 |
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"<|vision_pad|>",
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| 129 |
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"<|image_pad|>",
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| 130 |
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"<|video_pad|>"
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| 131 |
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],
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| 132 |
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"bos_token": null,
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| 133 |
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"clean_up_tokenization_spaces": false,
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| 134 |
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"eos_token": "<|im_end|>",
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| 135 |
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"errors": "replace",
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| 136 |
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"extra_special_tokens": {},
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| 137 |
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"model_max_length": 32768,
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| 138 |
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"pad_token": "<|endoftext|>",
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| 139 |
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"padding_side": "left",
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| 140 |
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"processor_class": "Qwen2VLProcessor",
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| 141 |
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"split_special_tokens": false,
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| 142 |
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"tokenizer_class": "Qwen2Tokenizer",
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| 143 |
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"unk_token": null
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| 144 |
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}
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vocab.json
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