Instructions to use Hellraiser24/git-base-textvqa with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Hellraiser24/git-base-textvqa with Transformers:

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

pipe = pipeline("image-text-to-text", model="Hellraiser24/git-base-textvqa")

# Load model directly
from transformers import AutoProcessor, AutoModelForMultimodalLM

processor = AutoProcessor.from_pretrained("Hellraiser24/git-base-textvqa")
model = AutoModelForMultimodalLM.from_pretrained("Hellraiser24/git-base-textvqa", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Hellraiser24/git-base-textvqa with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Hellraiser24/git-base-textvqa"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Hellraiser24/git-base-textvqa",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/Hellraiser24/git-base-textvqa

SGLang

How to use Hellraiser24/git-base-textvqa 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 "Hellraiser24/git-base-textvqa" \
    --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": "Hellraiser24/git-base-textvqa",
		"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 "Hellraiser24/git-base-textvqa" \
        --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": "Hellraiser24/git-base-textvqa",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use Hellraiser24/git-base-textvqa with Docker Model Runner:
```
docker model run hf.co/Hellraiser24/git-base-textvqa
```

git-base-textvqa / README.md

Hellraiser24

update model card README.md

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metadata

license: mit
tags:
  - generated_from_trainer
datasets:
  - textvqa
model-index:
  - name: git-base-textvqa
    results: []

git-base-textvqa

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

Loss: 0.0472

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: 4
eval_batch_size: 3
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

Training Loss	Epoch	Step	Validation Loss
0.9764	0.2	500	0.0499
0.0524	0.4	1000	0.0492
0.0525	0.6	1500	0.0494
0.0531	0.8	2000	0.0480
0.0515	1.0	2500	0.0477
0.0473	1.2	3000	0.0483
0.0479	1.4	3500	0.0477
0.0473	1.6	4000	0.0476
0.0486	1.8	4500	0.0472
0.0471	2.0	5000	0.0473
0.0454	2.2	5500	0.0473
0.0452	2.4	6000	0.0476
0.0438	2.6	6500	0.0475
0.0463	2.8	7000	0.0474
0.0449	3.0	7500	0.0472

Framework versions

Transformers 4.28.0
Pytorch 2.0.0
Datasets 2.12.0
Tokenizers 0.13.3