Instructions to use ChayanM/Swin-Bert_Mimic with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ChayanM/Swin-Bert_Mimic with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-text-to-text", model="ChayanM/Swin-Bert_Mimic")# Load model directly from transformers import AutoTokenizer, AutoModelForImageTextToText tokenizer = AutoTokenizer.from_pretrained("ChayanM/Swin-Bert_Mimic") model = AutoModelForImageTextToText.from_pretrained("ChayanM/Swin-Bert_Mimic") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use ChayanM/Swin-Bert_Mimic with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "ChayanM/Swin-Bert_Mimic" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "ChayanM/Swin-Bert_Mimic", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/ChayanM/Swin-Bert_Mimic
- SGLang
How to use ChayanM/Swin-Bert_Mimic 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 "ChayanM/Swin-Bert_Mimic" \ --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": "ChayanM/Swin-Bert_Mimic", "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 "ChayanM/Swin-Bert_Mimic" \ --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": "ChayanM/Swin-Bert_Mimic", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use ChayanM/Swin-Bert_Mimic with Docker Model Runner:
docker model run hf.co/ChayanM/Swin-Bert_Mimic
Training in progress, epoch 1
Browse files- config.json +0 -0
- model.safetensors +3 -0
- preprocessor_config.json +22 -0
- training_args.bin +3 -0
config.json
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model.safetensors
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version https://git-lfs.github.com/spec/v1
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oid sha256:8dcd3c5652832a33fe47301db09555da21599eae9dc42f33a64f462c9af92f9b
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size 906269048
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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": "ViTFeatureExtractor",
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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": 384,
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"width": 384
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}
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}
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training_args.bin
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version https://git-lfs.github.com/spec/v1
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oid sha256:114bd1ac2d602bab8b5420904654e2bde716fa3af4874302f37366684926e872
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size 4411
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