Instructions to use davelotito/donut-base-sroie-bayesian-optimization with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use davelotito/donut-base-sroie-bayesian-optimization with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("image-text-to-text", model="davelotito/donut-base-sroie-bayesian-optimization")

# Load model directly
from transformers import AutoTokenizer, AutoModelForMultimodalLM

tokenizer = AutoTokenizer.from_pretrained("davelotito/donut-base-sroie-bayesian-optimization")
model = AutoModelForMultimodalLM.from_pretrained("davelotito/donut-base-sroie-bayesian-optimization")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use davelotito/donut-base-sroie-bayesian-optimization with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "davelotito/donut-base-sroie-bayesian-optimization"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "davelotito/donut-base-sroie-bayesian-optimization",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/davelotito/donut-base-sroie-bayesian-optimization

SGLang

How to use davelotito/donut-base-sroie-bayesian-optimization 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 "davelotito/donut-base-sroie-bayesian-optimization" \
    --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": "davelotito/donut-base-sroie-bayesian-optimization",
		"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 "davelotito/donut-base-sroie-bayesian-optimization" \
        --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": "davelotito/donut-base-sroie-bayesian-optimization",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use davelotito/donut-base-sroie-bayesian-optimization with Docker Model Runner:
```
docker model run hf.co/davelotito/donut-base-sroie-bayesian-optimization
```

davelotito commited on Apr 29, 2024

Commit

65cb95c

verified ·

1 Parent(s): 37e674c

Training in progress, epoch 1

Browse files

Files changed (3) hide show

model.safetensors +1 -1
runs/Apr29_16-39-41_ip-172-16-28-163.ec2.internal/events.out.tfevents.1714408782.ip-172-16-28-163.ec2.internal.29809.4 +3 -0
training_args.bin +1 -1

model.safetensors CHANGED Viewed

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:f06bad808e9ed0b191212594df08099daf3799e8ad5bd1149cd447f970b10457
 size 809070744

 version https://git-lfs.github.com/spec/v1
+oid sha256:13ec7e14a3c5b5e946d136483ab1681d55a60288393e0b83bd0b94409bc42d15
 size 809070744

runs/Apr29_16-39-41_ip-172-16-28-163.ec2.internal/events.out.tfevents.1714408782.ip-172-16-28-163.ec2.internal.29809.4 ADDED Viewed

	@@ -0,0 +1,3 @@

+version https://git-lfs.github.com/spec/v1
+oid sha256:d75a6e9106b098ffde915869d907c9e45d0918a3f4061ca5cd29f00d792c9aad
+size 10398

training_args.bin CHANGED Viewed

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:b2a8d5c15d5ad93a7bbcd681757af483f57ec02f7d122dd29c5ea02d8740b470
 size 5304

 version https://git-lfs.github.com/spec/v1
+oid sha256:e0d9ad66da4dad5ed605b420ebdd9aae9b9a29d88f8e6327d049eb8628cc0c1b
 size 5304