Instructions to use IlyasMoutawwakil/tiny-random-LlavaForConditionalGeneration with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use IlyasMoutawwakil/tiny-random-LlavaForConditionalGeneration with Transformers:

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

pipe = pipeline("image-text-to-text", model="IlyasMoutawwakil/tiny-random-LlavaForConditionalGeneration")

# Load model directly
from transformers import AutoProcessor, AutoModelForMultimodalLM

processor = AutoProcessor.from_pretrained("IlyasMoutawwakil/tiny-random-LlavaForConditionalGeneration")
model = AutoModelForMultimodalLM.from_pretrained("IlyasMoutawwakil/tiny-random-LlavaForConditionalGeneration")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use IlyasMoutawwakil/tiny-random-LlavaForConditionalGeneration with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/IlyasMoutawwakil/tiny-random-LlavaForConditionalGeneration

SGLang

How to use IlyasMoutawwakil/tiny-random-LlavaForConditionalGeneration 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 "IlyasMoutawwakil/tiny-random-LlavaForConditionalGeneration" \
    --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": "IlyasMoutawwakil/tiny-random-LlavaForConditionalGeneration",
		"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 "IlyasMoutawwakil/tiny-random-LlavaForConditionalGeneration" \
        --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": "IlyasMoutawwakil/tiny-random-LlavaForConditionalGeneration",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use IlyasMoutawwakil/tiny-random-LlavaForConditionalGeneration with Docker Model Runner:
```
docker model run hf.co/IlyasMoutawwakil/tiny-random-LlavaForConditionalGeneration
```

Does not contain `<image>` token.

by Xenova - opened Mar 27, 2024

Discussion

Xenova

Mar 27, 2024

From: https://huggingface.co/docs/transformers/model_doc/llava#transformers.LlavaForConditionalGeneration.forward.example

Running

from PIL import Image
import requests
from transformers import AutoProcessor, LlavaForConditionalGeneration

model_id = "IlyasMoutawwakil/tiny-random-LlavaForConditionalGeneration"
model = LlavaForConditionalGeneration.from_pretrained(model_id)
processor = AutoProcessor.from_pretrained(model_id)

prompt = "<image>\nUSER: What's the content of the image?\nASSISTANT:"
url = "https://www.ilankelman.org/stopsigns/australia.jpg"
image = Image.open(requests.get(url, stream=True).raw)

inputs = processor(text=prompt, images=image, return_tensors="pt")

# Generate
generate_ids = model.generate(**inputs, max_length=30)
processor.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0]

results in

ValueError: The input provided to the model are wrong. The number of image tokens is 0 while the number of image given to the model is 1. This prevents correct indexing and breaks batch generation.

This is because you're missing the <image> token in the tokenizer (and the model embedding layer is only 32000 x 16).

I've forked your model and added it with:

processor.tokenizer.add_tokens(["<image>", "<pad>"], special_tokens=True)
model.resize_token_embeddings(len(processor.tokenizer))

The final model can be found at https://huggingface.co/Xenova/tiny-random-LlavaForConditionalGeneration

IlyasMoutawwakil

Owner Mar 27, 2024

Thanks a lot ! I was wondering why the model only worked for image captioning image-to-text with an AutoImageProcessor in my tests 🥲
I'll use yours and delete this one 🙏

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