Instructions to use TomasFAV/Pix2StructCzechInvoiceV0123 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use TomasFAV/Pix2StructCzechInvoiceV0123 with Transformers:

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

pipe = pipeline("image-text-to-text", model="TomasFAV/Pix2StructCzechInvoiceV0123")

# Load model directly
from transformers import AutoProcessor, AutoModelForMultimodalLM

processor = AutoProcessor.from_pretrained("TomasFAV/Pix2StructCzechInvoiceV0123")
model = AutoModelForMultimodalLM.from_pretrained("TomasFAV/Pix2StructCzechInvoiceV0123", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use TomasFAV/Pix2StructCzechInvoiceV0123 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "TomasFAV/Pix2StructCzechInvoiceV0123"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "TomasFAV/Pix2StructCzechInvoiceV0123",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/TomasFAV/Pix2StructCzechInvoiceV0123

SGLang

How to use TomasFAV/Pix2StructCzechInvoiceV0123 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 "TomasFAV/Pix2StructCzechInvoiceV0123" \
    --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": "TomasFAV/Pix2StructCzechInvoiceV0123",
		"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 "TomasFAV/Pix2StructCzechInvoiceV0123" \
        --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": "TomasFAV/Pix2StructCzechInvoiceV0123",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use TomasFAV/Pix2StructCzechInvoiceV0123 with Docker Model Runner:
```
docker model run hf.co/TomasFAV/Pix2StructCzechInvoiceV0123
```

TomasFAV commited on Mar 15

Commit

084afc3

verified ·

1 Parent(s): fef599b

Model save

Browse files

Files changed (3) hide show

README.md +14 -14
model.safetensors +1 -1
runs/Mar15_01-23-47_77b291cf41b2/events.out.tfevents.1773539087.77b291cf41b2.25725.1 +3 -0

README.md CHANGED Viewed

@@ -5,19 +5,19 @@ tags:
 metrics:
 - f1
 model-index:
-- name: Pix2StructCzechInvoiceV2
   results: []
 ---
 <!-- This model card has been generated automatically according to the information the Trainer had access to. You
 should probably proofread and complete it, then remove this comment. -->
-# Pix2StructCzechInvoiceV2
 This model was trained from scratch on an unknown dataset.
 It achieves the following results on the evaluation set:
-- Loss: 0.2521
-- F1: 0.7311
 ## Model description
@@ -50,16 +50,16 @@ The following hyperparameters were used during training:
 | Training Loss | Epoch | Step | Validation Loss | F1     |
 |:-------------:|:-----:|:----:|:---------------:|:------:|
-| 0.3432        | 1.0   | 115  | 0.2771          | 0.6644 |
-| 0.1942        | 2.0   | 230  | 0.2611          | 0.6745 |
-| 0.1934        | 3.0   | 345  | 0.2521          | 0.7311 |
-| 0.1325        | 4.0   | 460  | 0.2665          | 0.7133 |
-| 0.1131        | 5.0   | 575  | 0.2686          | 0.6762 |
-| 0.1125        | 6.0   | 690  | 0.2601          | 0.7277 |
-| 0.1011        | 7.0   | 805  | 0.2962          | 0.7118 |
-| 0.1229        | 8.0   | 920  | 0.2893          | 0.7095 |
-| 0.0861        | 9.0   | 1035 | 0.3019          | 0.6931 |
-| 0.0860        | 10.0  | 1150 | 0.3167          | 0.7186 |
 ### Framework versions

 metrics:
 - f1
 model-index:
+- name: Pix2StructCzechInvoiceV3
   results: []
 ---
 <!-- This model card has been generated automatically according to the information the Trainer had access to. You
 should probably proofread and complete it, then remove this comment. -->
+# Pix2StructCzechInvoiceV3
 This model was trained from scratch on an unknown dataset.
 It achieves the following results on the evaluation set:
+- Loss: 0.1542
+- F1: 0.8404
 ## Model description
 | Training Loss | Epoch | Step | Validation Loss | F1     |
 |:-------------:|:-----:|:----:|:---------------:|:------:|
+| 0.3277        | 1.0   | 23   | 0.1958          | 0.7239 |
+| 0.2366        | 2.0   | 46   | 0.1446          | 0.8037 |
+| 0.1780        | 3.0   | 69   | 0.1247          | 0.8060 |
+| 0.1153        | 4.0   | 92   | 0.1178          | 0.8316 |
+| 0.0895        | 5.0   | 115  | 0.1279          | 0.8312 |
+| 0.0774        | 6.0   | 138  | 0.1542          | 0.8404 |
+| 0.0766        | 7.0   | 161  | 0.1530          | 0.7972 |
+| 0.0697        | 8.0   | 184  | 0.1385          | 0.8372 |
+| 0.0804        | 9.0   | 207  | 0.1433          | 0.7963 |
+| 0.0664        | 10.0  | 230  | 0.1614          | 0.7991 |
 ### Framework versions

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