Instructions to use Sagarjha456/893_Images_Json with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Sagarjha456/893_Images_Json with Transformers:

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

pipe = pipeline("image-text-to-text", model="Sagarjha456/893_Images_Json")

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

tokenizer = AutoTokenizer.from_pretrained("Sagarjha456/893_Images_Json")
model = AutoModelForMultimodalLM.from_pretrained("Sagarjha456/893_Images_Json", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Sagarjha456/893_Images_Json with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Sagarjha456/893_Images_Json"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Sagarjha456/893_Images_Json",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/Sagarjha456/893_Images_Json

SGLang

How to use Sagarjha456/893_Images_Json 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 "Sagarjha456/893_Images_Json" \
    --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": "Sagarjha456/893_Images_Json",
		"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 "Sagarjha456/893_Images_Json" \
        --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": "Sagarjha456/893_Images_Json",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use Sagarjha456/893_Images_Json with Docker Model Runner:
```
docker model run hf.co/Sagarjha456/893_Images_Json
```

893_Images_Json

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

Loss: 0.4844

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: 1
eval_batch_size: 1
seed: 42
gradient_accumulation_steps: 4
total_train_batch_size: 4
optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
lr_scheduler_type: linear
lr_scheduler_warmup_steps: 100
num_epochs: 15
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss
13.6598	1.0754	200	2.7064
6.6749	2.1507	400	1.5175
3.6877	3.2261	600	0.8923
2.1001	4.3015	800	0.6686
1.6768	5.3769	1000	0.5783
1.3900	6.4522	1200	0.5409
1.1434	7.5276	1400	0.5079
0.9839	8.6030	1600	0.5004
0.8089	9.6783	1800	0.4942
0.7735	10.7537	2000	0.4867
0.7466	11.8291	2200	0.4888
0.7352	12.9044	2400	0.4861
0.6183	13.9798	2600	0.4840
0.5964	15.0	2790	0.4844

Framework versions

Transformers 5.10.1
Pytorch 2.11.0+cu128
Datasets 4.0.0
Tokenizers 0.22.2

Downloads last month: 10

Safetensors

Model size

0.2B params

Tensor type

I64

F32

Model tree for Sagarjha456/893_Images_Json

Base model

naver-clova-ix/donut-base

Finetuned

(489)

this model