Instructions to use m-ric/Aria_hf with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use m-ric/Aria_hf with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-text-to-text", model="m-ric/Aria_hf", trust_remote_code=True) 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("m-ric/Aria_hf", trust_remote_code=True) model = AutoModelForImageTextToText.from_pretrained("m-ric/Aria_hf", trust_remote_code=True) 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 Settings
- vLLM
How to use m-ric/Aria_hf with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "m-ric/Aria_hf" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "m-ric/Aria_hf", "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/m-ric/Aria_hf
- SGLang
How to use m-ric/Aria_hf 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 "m-ric/Aria_hf" \ --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": "m-ric/Aria_hf", "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 "m-ric/Aria_hf" \ --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": "m-ric/Aria_hf", "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 m-ric/Aria_hf with Docker Model Runner:
docker model run hf.co/m-ric/Aria_hf
Upload processor
Browse files- preprocessor_config.json +21 -0
- special_tokens_map.json +10 -0
- tokenizer.model +3 -0
- tokenizer_config.json +27 -0
preprocessor_config.json
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{
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"_transform": null,
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"auto_map": {
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"AutoImageProcessor": "vision_processor.AriaVisionProcessor",
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"AutoProcessor": "processing_aria.AriaProcessor"
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},
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"image_mean": [
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0.5,
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0.5,
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0.5
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],
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"image_processor_type": "AriaVisionProcessor",
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"image_std": [
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0.5,
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0.5,
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0.5
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],
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"max_image_size": 980,
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"min_image_size": 336,
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"processor_class": "AriaProcessor"
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}
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special_tokens_map.json
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{
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"pad_token": "<unk>",
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"unk_token": {
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"content": "<unk>",
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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.model
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version https://git-lfs.github.com/spec/v1
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oid sha256:e429a008ed1045d14464933311e0b3258575980efc9db4e61f368e399c719d2a
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size 1696299
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tokenizer_config.json
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{
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"add_bos_token": false,
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"add_eos_token": false,
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"add_prefix_space": true,
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"added_tokens_decoder": {
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"0": {
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"content": "<unk>",
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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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"bos_token": null,
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"chat_template": "{% if not add_generation_prompt is defined %}{% set add_generation_prompt = false %}{% endif %}{% for message in messages %}<|im_start|>{{ message['role'] }}\n{% if message['content'] is string %}{{ message['content'] }}{% elif message['content'] is iterable %}{% for item in message['content'] %}{% if item['type'] == 'text' %}{{ item['text'] }}{% elif item['type'] == 'image' %}<fim_prefix><|img|><fim_suffix>{% endif %}{% endfor %}{% endif %}<|im_end|>\n{% endfor %}{% if add_generation_prompt %}<|im_start|>assistant\n{% endif %}",
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"clean_up_tokenization_spaces": false,
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"eos_token": null,
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"legacy": true,
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"model_max_length": 1000000000000000019884624838656,
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"pad_token": "<unk>",
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"sp_model_kwargs": {},
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"spaces_between_special_tokens": false,
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"tokenizer_class": "LlamaTokenizer",
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"unk_token": "<unk>",
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"use_default_system_prompt": false
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}
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