Instructions to use Sprakbanken/norhand-hcr-beta1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Sprakbanken/norhand-hcr-beta1 with Transformers:

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

pipe = pipeline("image-text-to-text", model="Sprakbanken/norhand-hcr-beta1")

# Load model directly
from transformers import AutoTokenizer, AutoModelForMultimodalLM

tokenizer = AutoTokenizer.from_pretrained("Sprakbanken/norhand-hcr-beta1")
model = AutoModelForMultimodalLM.from_pretrained("Sprakbanken/norhand-hcr-beta1", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Sprakbanken/norhand-hcr-beta1 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Sprakbanken/norhand-hcr-beta1"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Sprakbanken/norhand-hcr-beta1",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/Sprakbanken/norhand-hcr-beta1

SGLang

How to use Sprakbanken/norhand-hcr-beta1 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 "Sprakbanken/norhand-hcr-beta1" \
    --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": "Sprakbanken/norhand-hcr-beta1",
		"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 "Sprakbanken/norhand-hcr-beta1" \
        --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": "Sprakbanken/norhand-hcr-beta1",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use Sprakbanken/norhand-hcr-beta1 with Docker Model Runner:
```
docker model run hf.co/Sprakbanken/norhand-hcr-beta1
```

norhand-hcr-beta1

This model is a fine-tuned version of microsoft/trocr-base-stage1 on the NorHand dataset. It achieves the following results on the evaluation set:

Loss: 1.4687
Cer: 0.0439

It achieves the following results on the test set:

CER: 8.3
WER: 22.07

This is work in progress.

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: 5e-05
train_batch_size: 32
eval_batch_size: 1
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
lr_scheduler_warmup_steps: 2000
num_epochs: 10

Training results

Training Loss	Epoch	Step	Validation Loss	Cer
0.7606	0.71	5000	1.0489	0.1266
0.5971	1.43	10000	1.2096	0.1028
0.4448	2.14	15000	1.3738	0.0771
0.3919	2.85	20000	1.2733	0.0670
0.2806	3.56	25000	1.3048	0.0597
0.2055	4.28	30000	1.3568	0.0557
0.2486	4.99	35000	0.9717	0.0527
0.1631	5.7	40000	1.3159	0.0506
0.1159	6.41	45000	1.3730	0.0480
0.0605	7.13	50000	1.3399	0.0469
0.0626	7.84	55000	1.3642	0.0460
0.0532	8.55	60000	1.3870	0.0450
0.0177	9.26	65000	1.4912	0.0439
0.0308	9.98	70000	1.4687	0.0439

Framework versions

Transformers 4.32.1
Pytorch 2.1.1+cu121
Datasets 2.15.0
Tokenizers 0.13.3

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