Instructions to use subhavarshith/invoice_90_10 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use subhavarshith/invoice_90_10 with Transformers:

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

pipe = pipeline("image-text-to-text", model="subhavarshith/invoice_90_10")

# Load model directly
from transformers import AutoTokenizer, AutoModelForMultimodalLM

tokenizer = AutoTokenizer.from_pretrained("subhavarshith/invoice_90_10")
model = AutoModelForMultimodalLM.from_pretrained("subhavarshith/invoice_90_10")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use subhavarshith/invoice_90_10 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "subhavarshith/invoice_90_10"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "subhavarshith/invoice_90_10",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/subhavarshith/invoice_90_10

SGLang

How to use subhavarshith/invoice_90_10 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 "subhavarshith/invoice_90_10" \
    --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": "subhavarshith/invoice_90_10",
		"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 "subhavarshith/invoice_90_10" \
        --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": "subhavarshith/invoice_90_10",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use subhavarshith/invoice_90_10 with Docker Model Runner:
```
docker model run hf.co/subhavarshith/invoice_90_10
```

subhavarshith commited on May 21, 2024

Commit

e98aaa3

verified ·

1 Parent(s): 4a6ae24

End of training

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Files changed (1) hide show

README.md +2 -2

README.md CHANGED Viewed

@@ -34,13 +34,13 @@ More information needed
 ### Training hyperparameters
 The following hyperparameters were used during training:
-- learning_rate: 2e-05
 - train_batch_size: 4
 - eval_batch_size: 16
 - seed: 42
 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
 - lr_scheduler_type: linear
-- num_epochs: 20
 - mixed_precision_training: Native AMP
 ### Training results

 ### Training hyperparameters
 The following hyperparameters were used during training:
+- learning_rate: 1e-05
 - train_batch_size: 4
 - eval_batch_size: 16
 - 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