Instructions to use tabtoyou/KoLLaVA-v1.5-Synatra-7b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use tabtoyou/KoLLaVA-v1.5-Synatra-7b with Transformers:

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

pipe = pipeline("text-generation", model="tabtoyou/KoLLaVA-v1.5-Synatra-7b")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoProcessor, AutoModelForCausalLM

processor = AutoProcessor.from_pretrained("tabtoyou/KoLLaVA-v1.5-Synatra-7b")
model = AutoModelForCausalLM.from_pretrained("tabtoyou/KoLLaVA-v1.5-Synatra-7b")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = processor.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(processor.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use tabtoyou/KoLLaVA-v1.5-Synatra-7b with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "tabtoyou/KoLLaVA-v1.5-Synatra-7b"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "tabtoyou/KoLLaVA-v1.5-Synatra-7b",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/tabtoyou/KoLLaVA-v1.5-Synatra-7b

SGLang

How to use tabtoyou/KoLLaVA-v1.5-Synatra-7b 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 "tabtoyou/KoLLaVA-v1.5-Synatra-7b" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "tabtoyou/KoLLaVA-v1.5-Synatra-7b",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

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 "tabtoyou/KoLLaVA-v1.5-Synatra-7b" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "tabtoyou/KoLLaVA-v1.5-Synatra-7b",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use tabtoyou/KoLLaVA-v1.5-Synatra-7b with Docker Model Runner:
```
docker model run hf.co/tabtoyou/KoLLaVA-v1.5-Synatra-7b
```

KoLLaVA : Korean Large Language and Vision Assistant (feat. LLaVA)

This model is a large multimodal model (LMM) that combines the LLM (Synatra) with visual encoder of CLIP (clip-vit-large-patch14-336 ), trained on Korean visual-instruction dataset (KoLLaVA-v1.5-Instruct-581k).

Detail codes are available at KoLLaVA github repository

License

This model is strictly non-commercial (cc-by-sa-4.0) use, Under 5K MAU The "Model" is completely free (ie. base model, derivates, merges/mixes) to use for non-commercial purposes as long as the the included cc-by-sa-4.0 license in any parent repository, and the non-commercial use statute remains, regardless of other models' licences. If your service has over 5K MAU contact me for license approval.

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