Instructions to use optimum-internal-testing/tiny-random-VisionEncoderDecoderModel-donut with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use optimum-internal-testing/tiny-random-VisionEncoderDecoderModel-donut with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-text-to-text", model="optimum-internal-testing/tiny-random-VisionEncoderDecoderModel-donut")# Load model directly from transformers import AutoTokenizer, AutoModelForMultimodalLM tokenizer = AutoTokenizer.from_pretrained("optimum-internal-testing/tiny-random-VisionEncoderDecoderModel-donut") model = AutoModelForMultimodalLM.from_pretrained("optimum-internal-testing/tiny-random-VisionEncoderDecoderModel-donut", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use optimum-internal-testing/tiny-random-VisionEncoderDecoderModel-donut with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "optimum-internal-testing/tiny-random-VisionEncoderDecoderModel-donut" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "optimum-internal-testing/tiny-random-VisionEncoderDecoderModel-donut", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/optimum-internal-testing/tiny-random-VisionEncoderDecoderModel-donut
- SGLang
How to use optimum-internal-testing/tiny-random-VisionEncoderDecoderModel-donut 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 "optimum-internal-testing/tiny-random-VisionEncoderDecoderModel-donut" \ --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": "optimum-internal-testing/tiny-random-VisionEncoderDecoderModel-donut", "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 "optimum-internal-testing/tiny-random-VisionEncoderDecoderModel-donut" \ --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": "optimum-internal-testing/tiny-random-VisionEncoderDecoderModel-donut", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use optimum-internal-testing/tiny-random-VisionEncoderDecoderModel-donut with Docker Model Runner:
docker model run hf.co/optimum-internal-testing/tiny-random-VisionEncoderDecoderModel-donut
File size: 1,598 Bytes
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"architectures": [
"VisionEncoderDecoderModel"
],
"decoder": {
"activation_dropout": 0.0,
"activation_function": "gelu",
"add_cross_attention": true,
"add_final_layer_norm": true,
"attention_dropout": 0.0,
"classifier_dropout": 0.0,
"d_model": 64,
"decoder_attention_heads": 2,
"decoder_ffn_dim": 128,
"decoder_layerdrop": 0.0,
"decoder_layers": 1,
"dropout": 0.1,
"encoder_attention_heads": 2,
"encoder_ffn_dim": 128,
"encoder_layerdrop": 0.0,
"encoder_layers": 1,
"init_std": 0.02,
"is_decoder": true,
"is_encoder_decoder": false,
"max_position_embeddings": 128,
"model_type": "mbart",
"num_hidden_layers": 1,
"scale_embedding": true,
"use_cache": true,
"vocab_size": 57532
},
"dtype": "float32",
"encoder": {
"attention_probs_dropout_prob": 0.0,
"depths": [
1,
1,
1,
1
],
"drop_path_rate": 0.1,
"embed_dim": 8,
"hidden_act": "gelu",
"hidden_dropout_prob": 0.0,
"hidden_size": 64,
"image_size": [
2560,
1920
],
"initializer_range": 0.02,
"layer_norm_eps": 1e-05,
"mlp_ratio": 4.0,
"model_type": "donut-swin",
"num_channels": 3,
"num_heads": [
1,
1,
1,
1
],
"num_layers": 4,
"patch_size": 4,
"path_norm": true,
"qkv_bias": true,
"use_absolute_embeddings": false,
"window_size": 10
},
"is_encoder_decoder": true,
"model_type": "vision-encoder-decoder",
"tie_word_embeddings": false,
"transformers_version": "4.57.3"
}
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