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 Settings

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

File size: 1,504 Bytes

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  "architectures": [
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  ],
  "attention_probs_dropout_prob": 0.1,
  "bos_token_id": 1,
  "cross_layer_interval": 1,
  "dropout": 0.0,
  "eos_token_id": 2,
  "freeze_lm_head": false,
  "freeze_text_layers": true,
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  "freeze_vision_layers": true,
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  "hidden_size": 32,
  "id2label": {
    "0": "LABEL_0",
    "1": "LABEL_1",
    "2": "LABEL_2"
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  "image_size": 30,
  "initializer_range": 0.02,
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  "label2id": {
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    "LABEL_1": 1,
    "LABEL_2": 2
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  "max_position_embeddings": 512,
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  "torch_dtype": "float32",
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}