Image-Text-to-Text
Transformers
Safetensors
Japanese
gemma3
gemma-3
japanese
text-generation
conversational
text-generation-inference
Instructions to use AXCXEPT/EZO2.5-gemma-3-12b-it-Preview with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use AXCXEPT/EZO2.5-gemma-3-12b-it-Preview with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-text-to-text", model="AXCXEPT/EZO2.5-gemma-3-12b-it-Preview") 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, AutoModelForMultimodalLM processor = AutoProcessor.from_pretrained("AXCXEPT/EZO2.5-gemma-3-12b-it-Preview") model = AutoModelForMultimodalLM.from_pretrained("AXCXEPT/EZO2.5-gemma-3-12b-it-Preview") 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]:])) - Inference
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use AXCXEPT/EZO2.5-gemma-3-12b-it-Preview with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "AXCXEPT/EZO2.5-gemma-3-12b-it-Preview" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "AXCXEPT/EZO2.5-gemma-3-12b-it-Preview", "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/AXCXEPT/EZO2.5-gemma-3-12b-it-Preview
- SGLang
How to use AXCXEPT/EZO2.5-gemma-3-12b-it-Preview 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 "AXCXEPT/EZO2.5-gemma-3-12b-it-Preview" \ --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": "AXCXEPT/EZO2.5-gemma-3-12b-it-Preview", "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 "AXCXEPT/EZO2.5-gemma-3-12b-it-Preview" \ --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": "AXCXEPT/EZO2.5-gemma-3-12b-it-Preview", "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 AXCXEPT/EZO2.5-gemma-3-12b-it-Preview with Docker Model Runner:
docker model run hf.co/AXCXEPT/EZO2.5-gemma-3-12b-it-Preview
Upload 3 files
Browse files- chat_template.json +3 -0
- preprocessor_config.json +29 -0
- processor_config.json +4 -0
chat_template.json
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"chat_template": "{{ bos_token }}\n{%- if messages[0]['role'] == 'system' -%}\n {%- if messages[0]['content'] is string -%}\n {%- set first_user_prefix = messages[0]['content'] + '\n\n' -%}\n {%- else -%}\n {%- set first_user_prefix = messages[0]['content'][0]['text'] + '\n\n' -%}\n {%- endif -%}\n {%- set loop_messages = messages[1:] -%}\n{%- else -%}\n {%- set first_user_prefix = \"\" -%}\n {%- set loop_messages = messages -%}\n{%- endif -%}\n{%- for message in loop_messages -%}\n {%- if (message['role'] == 'user') != (loop.index0 % 2 == 0) -%}\n {{ raise_exception(\"Conversation roles must alternate user/assistant/user/assistant/...\") }}\n {%- endif -%}\n {%- if (message['role'] == 'assistant') -%}\n {%- set role = \"model\" -%}\n {%- else -%}\n {%- set role = message['role'] -%}\n {%- endif -%}\n {{ '<start_of_turn>' + role + '\n' + (first_user_prefix if loop.first else \"\") }}\n {%- if message['content'] is string -%}\n {{ message['content'] | trim }}\n {%- elif message['content'] is iterable -%}\n {%- for item in message['content'] -%}\n {%- if item['type'] == 'image' -%}\n {{ '<start_of_image>' }}\n {%- elif item['type'] == 'text' -%}\n {{ item['text'] | trim }}\n {%- endif -%}\n {%- endfor -%}\n {%- else -%}\n {{ raise_exception(\"Invalid content type\") }}\n {%- endif -%}\n {{ '<end_of_turn>\n' }}\n{%- endfor -%}\n{%- if add_generation_prompt -%}\n {{'<start_of_turn>model\n'}}\n{%- endif -%}\n"
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}
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preprocessor_config.json
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{
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"do_convert_rgb": null,
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"do_normalize": true,
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"do_pan_and_scan": null,
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"do_rescale": true,
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"do_resize": true,
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"image_mean": [
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0.5,
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],
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"image_processor_type": "Gemma3ImageProcessor",
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"image_seq_length": 256,
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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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"pan_and_scan_max_num_crops": null,
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"pan_and_scan_min_crop_size": null,
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"pan_and_scan_min_ratio_to_activate": null,
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"processor_class": "Gemma3Processor",
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"resample": 2,
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"rescale_factor": 0.00392156862745098,
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"size": {
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"height": 896,
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"width": 896
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}
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}
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processor_config.json
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{
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"image_seq_length": 256,
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"processor_class": "Gemma3Processor"
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}
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