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

Libraries

How to use LFBMS/donut_guv_complex with Transformers:

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

pipe = pipeline("image-text-to-text", model="LFBMS/donut_guv_complex")

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

tokenizer = AutoTokenizer.from_pretrained("LFBMS/donut_guv_complex")
model = AutoModelForMultimodalLM.from_pretrained("LFBMS/donut_guv_complex", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use LFBMS/donut_guv_complex with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/LFBMS/donut_guv_complex

SGLang

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

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

donut_guv_complex

Commit History

Jul 30, 2025

21:19
Adding `safetensors` variant of this model⁠

SFconvertbot

Verified signature
022c1da

Feb 12, 2023

21:48
Training done

LFBMS

59b4d7b
21:48
Training done

LFBMS

b7d752e
21:48
Training in progress, epoch 9

LFBMS

2a37f3d
21:42
Training in progress, epoch 8

LFBMS

4bd0de4
21:35
Training in progress, epoch 7

LFBMS

2e5a58b
21:29
Training in progress, epoch 6

LFBMS

9bb9c88
21:23
Training in progress, epoch 5

LFBMS

c6a0123
21:17
Training in progress, epoch 4

LFBMS

4cc4620
21:11
Training in progress, epoch 3

LFBMS

26802f2
21:04
Training in progress, epoch 2

LFBMS

0d5f332
20:58
Training in progress, epoch 1

LFBMS

01eeacc
20:52
Training in progress, epoch 0

LFBMS

8ea9ac9
20:52
initial commit

LFBMS

cfb610f

Commit History

Jul 30, 2025

Adding `safetensors` variant of this model⁠

Feb 12, 2023

Training done

Training done

Training in progress, epoch 9

Training in progress, epoch 8

Training in progress, epoch 7

Training in progress, epoch 6

Training in progress, epoch 5

Training in progress, epoch 4

Training in progress, epoch 3

Training in progress, epoch 2

Training in progress, epoch 1

Training in progress, epoch 0

initial commit