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

Libraries

How to use tabtoyou/KoLLaVA-KoVicuna-7b with Transformers:

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

pipe = pipeline("text-generation", model="tabtoyou/KoLLaVA-KoVicuna-7b")

# Load model directly
from transformers import AutoProcessor, AutoModelForCausalLM

processor = AutoProcessor.from_pretrained("tabtoyou/KoLLaVA-KoVicuna-7b")
model = AutoModelForCausalLM.from_pretrained("tabtoyou/KoLLaVA-KoVicuna-7b")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use tabtoyou/KoLLaVA-KoVicuna-7b with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "tabtoyou/KoLLaVA-KoVicuna-7b"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "tabtoyou/KoLLaVA-KoVicuna-7b",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/tabtoyou/KoLLaVA-KoVicuna-7b

SGLang

How to use tabtoyou/KoLLaVA-KoVicuna-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-KoVicuna-7b" \
    --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": "tabtoyou/KoLLaVA-KoVicuna-7b",
		"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 "tabtoyou/KoLLaVA-KoVicuna-7b" \
        --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": "tabtoyou/KoLLaVA-KoVicuna-7b",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

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

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

This model is a large multimodal model (LMM) that combines the LLM(KoVicuna) with visual encoder of CLIP(ViT-14), trained on Korean visual-instruction dataset.

Detail codes are available at KoLLaVA github repository

Training hyperparameters

learning rate : 2e-5
train_batch_size: 16
distributed_type: multi-GPU (A100 80G)
num_devices: 4
gradient_accumulation_steps: 1
total_train_batch_size: 64
total_eval_batch_size: 16
lr_scheduler_type: cosine
num_epochs: 1

Model License: Apache License 2.0

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tabtoyou
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KoLLaVA-KoVicuna-7b

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

Training hyperparameters

Datasets used to train tabtoyou/KoLLaVA-KoVicuna-7b