Instructions to use frontrx/ECGv3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use frontrx/ECGv3 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-text-to-text", model="frontrx/ECGv3") 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("frontrx/ECGv3") model = AutoModelForImageTextToText.from_pretrained("frontrx/ECGv3") 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 frontrx/ECGv3 with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "frontrx/ECGv3" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "frontrx/ECGv3", "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/frontrx/ECGv3
- SGLang
How to use frontrx/ECGv3 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 "frontrx/ECGv3" \ --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": "frontrx/ECGv3", "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 "frontrx/ECGv3" \ --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": "frontrx/ECGv3", "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 frontrx/ECGv3 with Docker Model Runner:
docker model run hf.co/frontrx/ECGv3
Upload Idefics3ForConditionalGeneration
Browse files- config.json +3 -18
- model.safetensors +2 -2
config.json
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"image_token_id": 49153,
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"model_type": "idefics3",
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"pad_token_id": 128002,
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"quantization_config": {
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"_load_in_4bit": true,
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"_load_in_8bit": false,
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"bnb_4bit_compute_dtype": "bfloat16",
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"bnb_4bit_quant_storage": "uint8",
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"bnb_4bit_quant_type": "nf4",
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"bnb_4bit_use_double_quant": true,
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"llm_int8_enable_fp32_cpu_offload": false,
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"llm_int8_has_fp16_weight": false,
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"llm_int8_skip_modules": null,
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"llm_int8_threshold": 6.0,
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"load_in_4bit": true,
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"load_in_8bit": false,
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"quant_method": "bitsandbytes"
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},
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"scale_factor": 3,
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"text_config": {
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"_flash_attn_2_enabled": true,
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"qk_layer_norms": false,
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"rms_norm_eps": 1e-05,
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"rope_theta": 273768.0,
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"torch_dtype": "
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"use_resampler": false,
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"vocab_size": 49155
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"tie_word_embeddings": false,
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"transformers_version": "4.49.0",
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"image_token_id": 49153,
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"model_type": "idefics3",
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"pad_token_id": 128002,
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"scale_factor": 3,
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"text_config": {
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"_flash_attn_2_enabled": true,
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"qk_layer_norms": false,
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"rms_norm_eps": 1e-05,
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"rope_theta": 273768.0,
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"torch_dtype": "bfloat16",
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"vocab_size": 49155
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"vision_config": {
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size 4492630912
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