Instructions to use hf-internal-testing/tiny-random-IdeficsForVisionText2Text with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use hf-internal-testing/tiny-random-IdeficsForVisionText2Text with Transformers:

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

pipe = pipeline("image-text-to-text", model="hf-internal-testing/tiny-random-IdeficsForVisionText2Text")

# Load model directly
from transformers import AutoProcessor, AutoModelForImageTextToText

processor = AutoProcessor.from_pretrained("hf-internal-testing/tiny-random-IdeficsForVisionText2Text")
model = AutoModelForImageTextToText.from_pretrained("hf-internal-testing/tiny-random-IdeficsForVisionText2Text")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use hf-internal-testing/tiny-random-IdeficsForVisionText2Text with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "hf-internal-testing/tiny-random-IdeficsForVisionText2Text"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "hf-internal-testing/tiny-random-IdeficsForVisionText2Text",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/hf-internal-testing/tiny-random-IdeficsForVisionText2Text

SGLang

How to use hf-internal-testing/tiny-random-IdeficsForVisionText2Text 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 "hf-internal-testing/tiny-random-IdeficsForVisionText2Text" \
    --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": "hf-internal-testing/tiny-random-IdeficsForVisionText2Text",
		"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 "hf-internal-testing/tiny-random-IdeficsForVisionText2Text" \
        --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": "hf-internal-testing/tiny-random-IdeficsForVisionText2Text",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use hf-internal-testing/tiny-random-IdeficsForVisionText2Text with Docker Model Runner:
```
docker model run hf.co/hf-internal-testing/tiny-random-IdeficsForVisionText2Text
```

tiny-random-IdeficsForVisionText2Text / pytorch_model.bin

hf-transformers-bot

Update tiny models for IdeficsForVisionText2Text

dd95be1 almost 3 years ago

download

history blame

8.73 MB

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:: 6967928d6c49760fc39944dff62818c06ae9ee511af4083f07c43dfa717ded65
Size of remote file:: 8.73 MB
SHA256:: 7763bf1a2027f64d19a8a43d12278daa26b840b6a7a6f2b53267a050c68919c7

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