Instructions to use anymodality/llava-v1.5-13b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use anymodality/llava-v1.5-13b with Transformers:

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

pipe = pipeline("text-generation", model="anymodality/llava-v1.5-13b")

# Load model directly
from transformers import AutoProcessor, AutoModelForCausalLM

processor = AutoProcessor.from_pretrained("anymodality/llava-v1.5-13b")
model = AutoModelForCausalLM.from_pretrained("anymodality/llava-v1.5-13b")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use anymodality/llava-v1.5-13b with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "anymodality/llava-v1.5-13b"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "anymodality/llava-v1.5-13b",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/anymodality/llava-v1.5-13b

SGLang

How to use anymodality/llava-v1.5-13b 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 "anymodality/llava-v1.5-13b" \
    --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": "anymodality/llava-v1.5-13b",
		"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 "anymodality/llava-v1.5-13b" \
        --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": "anymodality/llava-v1.5-13b",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use anymodality/llava-v1.5-13b with Docker Model Runner:
```
docker model run hf.co/anymodality/llava-v1.5-13b
```

LLaVA Model Card

Model details

This is a fork from origianl liuhaotian/llava-v1.5-13b. This repo added code/inference.py and code/requirements.txt to provide customize inference script and environment for SageMaker deployment.

Model type: LLaVA is an open-source chatbot trained by fine-tuning LLaMA/Vicuna on GPT-generated multimodal instruction-following data. It is an auto-regressive language model, based on the transformer architecture.

Model date: LLaVA-v1.5-13B was trained in September 2023.

Paper or resources for more information: https://llava-vl.github.io/

How to Deploy on SageMaker

Following deploy_llava.ipynb (full tutorial here) , bundle llava model weights and code into a model.tar.gz and upload to S3:

from sagemaker.s3 import S3Uploader

# upload model.tar.gz to s3
s3_model_uri = S3Uploader.upload(local_path="./model.tar.gz", desired_s3_uri=f"s3://{sess.default_bucket()}/llava-v1.5-13b")

print(f"model uploaded to: {s3_model_uri}")

Then use HuggingfaceModel to deploy our real-time inference endpoint on SageMaker:

from sagemaker.huggingface.model import HuggingFaceModel

# create Hugging Face Model Class
huggingface_model = HuggingFaceModel(
   model_data=s3_model_uri,      # path to your model and script
   role=role,                    # iam role with permissions to create an Endpoint
   transformers_version="4.28.1",  # transformers version used
   pytorch_version="2.0.0",       # pytorch version used
   py_version='py310',            # python version used
   model_server_workers=1
)

# deploy the endpoint endpoint
predictor = huggingface_model.deploy(
    initial_instance_count=1,
    instance_type="ml.g5.xlarge",
)

Inference on SageMaker

Default conv_mode for llava-1.5 is setup as llava_v1 to process raw_prompt into meaningful prompt. You can also setup conv_mode as raw to directly use raw_prompt.

data = {
    "image" : 'https://raw.githubusercontent.com/haotian-liu/LLaVA/main/images/llava_logo.png', 
    "question" : "Describe the image and color details.",
    # "max_new_tokens" : 1024,
    # "temperature" : 0.2,
    # "conv_mode" : "llava_v1"
}
output = predictor.predict(data)
print(output)

Or use SageMakerRuntime to setup endpoint invoking client.

License

Where to send questions or comments about the model: https://github.com/haotian-liu/LLaVA/issues

Intended use

Primary intended uses: The primary use of LLaVA is research on large multimodal models and chatbots.

Primary intended users: The primary intended users of the model are researchers and hobbyists in computer vision, natural language processing, machine learning, and artificial intelligence.

Training dataset

558K filtered image-text pairs from LAION/CC/SBU, captioned by BLIP.
158K GPT-generated multimodal instruction-following data.
450K academic-task-oriented VQA data mixture.
40K ShareGPT data.

Evaluation dataset

A collection of 12 benchmarks, including 5 academic VQA benchmarks and 7 recent benchmarks specifically proposed for instruction-following LMMs.

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