Image-Text-to-Text
Transformers
TensorBoard
Safetensors
vision-encoder-decoder
Generated from Trainer
Instructions to use Skyler215/SwinV2_Syllable with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Skyler215/SwinV2_Syllable with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-text-to-text", model="Skyler215/SwinV2_Syllable")# Load model directly from transformers import AutoTokenizer, AutoModelForImageTextToText tokenizer = AutoTokenizer.from_pretrained("Skyler215/SwinV2_Syllable") model = AutoModelForImageTextToText.from_pretrained("Skyler215/SwinV2_Syllable") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use Skyler215/SwinV2_Syllable with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "Skyler215/SwinV2_Syllable" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Skyler215/SwinV2_Syllable", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/Skyler215/SwinV2_Syllable
- SGLang
How to use Skyler215/SwinV2_Syllable 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 "Skyler215/SwinV2_Syllable" \ --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": "Skyler215/SwinV2_Syllable", "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 "Skyler215/SwinV2_Syllable" \ --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": "Skyler215/SwinV2_Syllable", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use Skyler215/SwinV2_Syllable with Docker Model Runner:
docker model run hf.co/Skyler215/SwinV2_Syllable
Training in progress, epoch 1
Browse files- config.json +0 -0
- model.safetensors +3 -0
- preprocessor_config.json +22 -0
- runs/Nov29_15-34-44_f507b7b2048e/events.out.tfevents.1732894492.f507b7b2048e.2025.0 +3 -0
- runs/Nov29_15-35-41_f507b7b2048e/events.out.tfevents.1732894542.f507b7b2048e.2025.1 +3 -0
- runs/Nov29_15-36-29_f507b7b2048e/events.out.tfevents.1732894590.f507b7b2048e.5480.0 +3 -0
- runs/Nov29_16-04-45_f507b7b2048e/events.out.tfevents.1732896286.f507b7b2048e.12441.0 +3 -0
- training_args.bin +3 -0
config.json
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model.safetensors
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oid sha256:c61c8f011c6103439de9bd0fa96e87b48dce5dd414beb49e028aa5400752fa50
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preprocessor_config.json
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{
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"do_normalize": true,
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"do_rescale": true,
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"do_resize": true,
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"image_mean": [
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0.485,
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0.456,
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0.406
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],
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"image_processor_type": "ViTImageProcessor",
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"image_std": [
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0.229,
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0.224,
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0.225
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],
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"resample": 3,
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"rescale_factor": 0.00392156862745098,
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"size": {
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"height": 192,
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"width": 192
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}
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}
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runs/Nov29_15-34-44_f507b7b2048e/events.out.tfevents.1732894492.f507b7b2048e.2025.0
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oid sha256:218c14b88b48d1a663ecc2c6629a7c335a2f01803d250ce1488f73eea4c1672d
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size 1228888
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runs/Nov29_15-35-41_f507b7b2048e/events.out.tfevents.1732894542.f507b7b2048e.2025.1
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runs/Nov29_15-36-29_f507b7b2048e/events.out.tfevents.1732894590.f507b7b2048e.5480.0
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runs/Nov29_16-04-45_f507b7b2048e/events.out.tfevents.1732896286.f507b7b2048e.12441.0
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training_args.bin
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oid sha256:07f825b198e55caea385134a31e68015a88b0cfbe2d1600cad0b7d1541b363fa
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size 5496
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