Instructions to use JandC/donut-base-full_text_wt_val_1008 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use JandC/donut-base-full_text_wt_val_1008 with Transformers:

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

pipe = pipeline("image-text-to-text", model="JandC/donut-base-full_text_wt_val_1008")

# Load model directly
from transformers import AutoTokenizer, AutoModelForMultimodalLM

tokenizer = AutoTokenizer.from_pretrained("JandC/donut-base-full_text_wt_val_1008")
model = AutoModelForMultimodalLM.from_pretrained("JandC/donut-base-full_text_wt_val_1008")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use JandC/donut-base-full_text_wt_val_1008 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "JandC/donut-base-full_text_wt_val_1008"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "JandC/donut-base-full_text_wt_val_1008",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/JandC/donut-base-full_text_wt_val_1008

SGLang

How to use JandC/donut-base-full_text_wt_val_1008 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 "JandC/donut-base-full_text_wt_val_1008" \
    --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": "JandC/donut-base-full_text_wt_val_1008",
		"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 "JandC/donut-base-full_text_wt_val_1008" \
        --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": "JandC/donut-base-full_text_wt_val_1008",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use JandC/donut-base-full_text_wt_val_1008 with Docker Model Runner:
```
docker model run hf.co/JandC/donut-base-full_text_wt_val_1008
```

donut-base-full_text_wt_val_1008

This model is a fine-tuned version of naver-clova-ix/donut-base on the imagefolder dataset. It achieves the following results on the evaluation set:

Loss: 0.1060

Model description

More information needed

Intended uses & limitations

More information needed

Training and evaluation data

More information needed

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

learning_rate: 2e-05
train_batch_size: 2
eval_batch_size: 8
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
num_epochs: 10
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss
1.9227	0.2	100	0.6985
0.6852	0.4	200	0.4421
0.5102	0.6	300	0.3346
0.4178	0.79	400	0.2886
0.4476	0.99	500	0.2455
0.2931	1.19	600	0.2287
0.2647	1.39	700	0.2072
0.2418	1.59	800	0.1905
0.3031	1.79	900	0.1754
0.2306	1.98	1000	0.1667
0.2031	2.18	1100	0.1619
0.1918	2.38	1200	0.1536
0.1802	2.58	1300	0.1504
0.1646	2.78	1400	0.1436
0.1816	2.98	1500	0.1379
0.1344	3.17	1600	0.1395
0.1752	3.37	1700	0.1336
0.1388	3.57	1800	0.1306
0.1402	3.77	1900	0.1262
0.1123	3.97	2000	0.1277
0.144	4.17	2100	0.1248
0.1077	4.37	2200	0.1226
0.1134	4.56	2300	0.1186
0.1192	4.76	2400	0.1179
0.1142	4.96	2500	0.1194
0.1426	5.16	2600	0.1202
0.1022	5.36	2700	0.1165
0.0815	5.56	2800	0.1164
0.1096	5.75	2900	0.1166
0.0866	5.95	3000	0.1121
0.1148	6.15	3100	0.1122
0.0771	6.35	3200	0.1129
0.0996	6.55	3300	0.1096
0.0622	6.75	3400	0.1099
0.0985	6.94	3500	0.1092
0.0684	7.14	3600	0.1097
0.0669	7.34	3700	0.1086
0.0624	7.54	3800	0.1088
0.0763	7.74	3900	0.1069
0.0579	7.94	4000	0.1060
0.0623	8.13	4100	0.1083
0.0599	8.33	4200	0.1058
0.0625	8.53	4300	0.1073
0.0499	8.73	4400	0.1059
0.0628	8.93	4500	0.1059
0.0684	9.13	4600	0.1063
0.0472	9.33	4700	0.1056
0.068	9.52	4800	0.1057
0.06	9.72	4900	0.1062
0.0636	9.92	5000	0.1060

Framework versions

Transformers 4.38.0.dev0
Pytorch 2.1.0+cu121
Datasets 2.16.1
Tokenizers 0.15.1

Downloads last month: 2

Safetensors

Model size

0.2B params

Tensor type

I64

F32

Inference Providers NEW

Image-Text-to-Text

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Model tree for JandC/donut-base-full_text_wt_val_1008

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