Instructions to use acrowth/donutchequeep3samples30152 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use acrowth/donutchequeep3samples30152 with Transformers:

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

pipe = pipeline("image-text-to-text", model="acrowth/donutchequeep3samples30152")

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForMultimodalLM

tokenizer = AutoTokenizer.from_pretrained("acrowth/donutchequeep3samples30152")
model = AutoModelForMultimodalLM.from_pretrained("acrowth/donutchequeep3samples30152", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use acrowth/donutchequeep3samples30152 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "acrowth/donutchequeep3samples30152"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "acrowth/donutchequeep3samples30152",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/acrowth/donutchequeep3samples30152

SGLang

How to use acrowth/donutchequeep3samples30152 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 "acrowth/donutchequeep3samples30152" \
    --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": "acrowth/donutchequeep3samples30152",
		"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 "acrowth/donutchequeep3samples30152" \
        --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": "acrowth/donutchequeep3samples30152",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use acrowth/donutchequeep3samples30152 with Docker Model Runner:
```
docker model run hf.co/acrowth/donutchequeep3samples30152
```

donutchequeep3samples30152

Commit History

Jul 30, 2025

20:39
Adding `safetensors` variant of this model⁠

SFconvertbot

Verified signature
6665f09

Nov 6, 2022

16:54
Training done

acrowth

7537f92
16:54
Training done

acrowth

e371ec6
16:54
Training in progress, epoch 2

acrowth

2d152c2
15:51
Training in progress, epoch 1

acrowth

b952c74
14:48
Training in progress, epoch 0

acrowth

cc39818
14:47
initial commit

acrowth

690830f

Commit History

Jul 30, 2025

Adding `safetensors` variant of this model⁠

Nov 6, 2022

Training done

Training done

Training in progress, epoch 2

Training in progress, epoch 1

Training in progress, epoch 0

initial commit