Instructions to use martinsinnona/visdecode_plotqa_2k with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use martinsinnona/visdecode_plotqa_2k with Transformers:

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

pipe = pipeline("image-text-to-text", model="martinsinnona/visdecode_plotqa_2k")

# Load model directly
from transformers import AutoProcessor, AutoModelForMultimodalLM

processor = AutoProcessor.from_pretrained("martinsinnona/visdecode_plotqa_2k")
model = AutoModelForMultimodalLM.from_pretrained("martinsinnona/visdecode_plotqa_2k", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use martinsinnona/visdecode_plotqa_2k with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "martinsinnona/visdecode_plotqa_2k"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "martinsinnona/visdecode_plotqa_2k",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/martinsinnona/visdecode_plotqa_2k

SGLang

How to use martinsinnona/visdecode_plotqa_2k 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 "martinsinnona/visdecode_plotqa_2k" \
    --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": "martinsinnona/visdecode_plotqa_2k",
		"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 "martinsinnona/visdecode_plotqa_2k" \
        --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": "martinsinnona/visdecode_plotqa_2k",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use martinsinnona/visdecode_plotqa_2k with Docker Model Runner:
```
docker model run hf.co/martinsinnona/visdecode_plotqa_2k
```

visdecode_plotqa_2k / model.safetensors

martinsinnona

Upload Pix2StructForConditionalGeneration

0734a55 verified almost 2 years ago

Download

History Blame Contribute Delete

1.13 GB

This file is stored with Xet . It is too big to display, but you can still download it.

Xet Pointer Details

( Raw pointer file )

Xet hash:: 925b8b90f5a8192fa82b6a19231f2d1a613c239b2bf341a8e73f68444fbc9aef
Size of remote file:: 1.13 GB
SHA256:: 3a5f1f9ccc64e6c2fa1754c7b3bcf05867d3b617137f98f463a4ccbbdcc19aa1

Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.