Image-Text-to-Text
Transformers
Safetensors
interfaze_lite
feature-extraction
vlm
any-to-text
audio-text-to-text
multimodal
multilingual
mixture-of-architectures
ocr
document-understanding
speech-recognition
speaker-diarization
object-detection
gui-grounding
image-segmentation
translation
time-series-forecasting
guardrails
structured-output
agent
custom_code
Instructions to use interfaze-ai/interfaze-1-lite with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use interfaze-ai/interfaze-1-lite with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-text-to-text", model="interfaze-ai/interfaze-1-lite", trust_remote_code=True)# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("interfaze-ai/interfaze-1-lite", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use interfaze-ai/interfaze-1-lite with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "interfaze-ai/interfaze-1-lite" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "interfaze-ai/interfaze-1-lite", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/interfaze-ai/interfaze-1-lite
- SGLang
How to use interfaze-ai/interfaze-1-lite 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 "interfaze-ai/interfaze-1-lite" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "interfaze-ai/interfaze-1-lite", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'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 "interfaze-ai/interfaze-1-lite" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "interfaze-ai/interfaze-1-lite", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use interfaze-ai/interfaze-1-lite with Docker Model Runner:
docker model run hf.co/interfaze-ai/interfaze-1-lite
Download segmenter/model.pt from interfaze-ai/interfaze-1-lite: direct link, hf CLI and curl.
- Browser
- Download file 898 MB
-
https://huggingface.co/interfaze-ai/interfaze-1-lite/resolve/main/segmenter/model.pt
- Command line
-
hf download hf://interfaze-ai/interfaze-1-lite/segmenter/model.pt
-
curl -L -o model.pt https://huggingface.co/interfaze-ai/interfaze-1-lite/resolve/main/segmenter/model.pt
898 MB
- Xet hash:
- 42db20602fdd6f469fe0e75f5c4dfa0910f97637946bd07757196f787aa46a75
- Size of remote file:
- 898 MB
- SHA256:
- 41e72d7afe9a01681fa88c99a81053562b086e7337345bf6ff4053b8c777f9c8
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