Instructions to use tedad09/PolizzeDonut-ProvaFreeze-tmp with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use tedad09/PolizzeDonut-ProvaFreeze-tmp with Transformers:

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

pipe = pipeline("image-text-to-text", model="tedad09/PolizzeDonut-ProvaFreeze-tmp")

# Load model directly
from transformers import AutoTokenizer, AutoModelForMultimodalLM

tokenizer = AutoTokenizer.from_pretrained("tedad09/PolizzeDonut-ProvaFreeze-tmp")
model = AutoModelForMultimodalLM.from_pretrained("tedad09/PolizzeDonut-ProvaFreeze-tmp", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use tedad09/PolizzeDonut-ProvaFreeze-tmp with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "tedad09/PolizzeDonut-ProvaFreeze-tmp"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "tedad09/PolizzeDonut-ProvaFreeze-tmp",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/tedad09/PolizzeDonut-ProvaFreeze-tmp

SGLang

How to use tedad09/PolizzeDonut-ProvaFreeze-tmp 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 "tedad09/PolizzeDonut-ProvaFreeze-tmp" \
    --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": "tedad09/PolizzeDonut-ProvaFreeze-tmp",
		"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 "tedad09/PolizzeDonut-ProvaFreeze-tmp" \
        --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": "tedad09/PolizzeDonut-ProvaFreeze-tmp",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use tedad09/PolizzeDonut-ProvaFreeze-tmp with Docker Model Runner:
```
docker model run hf.co/tedad09/PolizzeDonut-ProvaFreeze-tmp
```

tedad09 commited on Apr 23, 2024

Commit

5bd7e95

verified ·

1 Parent(s): dccd4c9

End of training

Browse files

Files changed (4) hide show

README.md +1 -1
model.safetensors +1 -1
runs/Apr23_12-57-36_B80-C-VMICTGPU01/events.out.tfevents.1713877056.B80-C-VMICTGPU01.42738.0 +3 -0
training_args.bin +1 -1

README.md CHANGED Viewed

@@ -40,7 +40,7 @@ The following hyperparameters were used during training:
 - seed: 42
 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
 - lr_scheduler_type: linear
-- num_epochs: 3
 - mixed_precision_training: Native AMP
 ### Training results

 - seed: 42
 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
 - lr_scheduler_type: linear
+- num_epochs: 5
 - mixed_precision_training: Native AMP
 ### Training results

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