Image-Text-to-Text
Transformers
ONNX
Safetensors
English
idefics3
rag
visual-ingestion
image-to-text
document-ai
table-recognition
opsis
cpu
conversational
Instructions to use yafitzdev/opsis-v1-nano-g1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use yafitzdev/opsis-v1-nano-g1 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-text-to-text", model="yafitzdev/opsis-v1-nano-g1") 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)# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForMultimodalLM processor = AutoProcessor.from_pretrained("yafitzdev/opsis-v1-nano-g1") model = AutoModelForMultimodalLM.from_pretrained("yafitzdev/opsis-v1-nano-g1", device_map="auto") 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=256) print(processor.decode(outputs[0][inputs["input_ids"].shape[-1]:])) - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use yafitzdev/opsis-v1-nano-g1 with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "yafitzdev/opsis-v1-nano-g1" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "yafitzdev/opsis-v1-nano-g1", "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/yafitzdev/opsis-v1-nano-g1
- SGLang
How to use yafitzdev/opsis-v1-nano-g1 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 "yafitzdev/opsis-v1-nano-g1" \ --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": "yafitzdev/opsis-v1-nano-g1", "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 "yafitzdev/opsis-v1-nano-g1" \ --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": "yafitzdev/opsis-v1-nano-g1", "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 yafitzdev/opsis-v1-nano-g1 with Docker Model Runner:
docker model run hf.co/yafitzdev/opsis-v1-nano-g1
Download rag_image_parser_config.json from yafitzdev/opsis-v1-nano-g1: direct link, hf CLI and curl.
- Browser
- Download file 635 Bytes
-
https://huggingface.co/yafitzdev/opsis-v1-nano-g1/resolve/main/rag_image_parser_config.json
- Command line
-
hf download hf://yafitzdev/opsis-v1-nano-g1/rag_image_parser_config.json
-
curl -L -o rag_image_parser_config.json https://huggingface.co/yafitzdev/opsis-v1-nano-g1/resolve/main/rag_image_parser_config.json
635 Bytes
| { | |
| "format": "rag-image-parser-split-onnx-v1", | |
| "checkpoint": "yafitzdev/opsis-v1-nano-g1", | |
| "base_architecture": "HuggingFaceTB/SmolVLM-256M-Instruct", | |
| "prompt_version": "rag-image-v3", | |
| "image_longest_edge": 1024, | |
| "recommended_backend": "onnxruntime-cpu", | |
| "variants": [ | |
| "fp32" | |
| ], | |
| "patched_connector_weights": 1, | |
| "patched_vision_weights": 0, | |
| "patched_decoder_weights": 120, | |
| "training": { | |
| "rendered_files": 15000, | |
| "epochs": 1.0, | |
| "learning_rate": 0.0002, | |
| "trainable_parameters": 1024512, | |
| "total_parameters": 257509440, | |
| "lora_rank": 8, | |
| "lora_alpha": 16, | |
| "vision_lora": false | |
| } | |
| } | |