Ko-LLaVA series
Collection
3 items • Updated
How to use etri-vilab/Ko-LLaVA-13b with Transformers:
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("text-generation", model="etri-vilab/Ko-LLaVA-13b") # Load model directly
from transformers import AutoProcessor, AutoModelForCausalLM
processor = AutoProcessor.from_pretrained("etri-vilab/Ko-LLaVA-13b")
model = AutoModelForCausalLM.from_pretrained("etri-vilab/Ko-LLaVA-13b")How to use etri-vilab/Ko-LLaVA-13b with vLLM:
# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "etri-vilab/Ko-LLaVA-13b"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
-H "Content-Type: application/json" \
--data '{
"model": "etri-vilab/Ko-LLaVA-13b",
"prompt": "Once upon a time,",
"max_tokens": 512,
"temperature": 0.5
}'docker model run hf.co/etri-vilab/Ko-LLaVA-13b
How to use etri-vilab/Ko-LLaVA-13b with SGLang:
# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
--model-path "etri-vilab/Ko-LLaVA-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": "etri-vilab/Ko-LLaVA-13b",
"prompt": "Once upon a time,",
"max_tokens": 512,
"temperature": 0.5
}'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 "etri-vilab/Ko-LLaVA-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": "etri-vilab/Ko-LLaVA-13b",
"prompt": "Once upon a time,",
"max_tokens": 512,
"temperature": 0.5
}'How to use etri-vilab/Ko-LLaVA-13b with Docker Model Runner:
docker model run hf.co/etri-vilab/Ko-LLaVA-13b
Llama 2 is licensed under the LLAMA 2 Community License, Copyright (c) Meta Platforms, Inc. All Rights Reserved.
This work was supported by the Institute of Information & communications Technology Planning & Evaluation(IITP) grants funded by the Korea government(MSIT) (No. 2022- 0-00871, Development of AI Autonomy and Knowledge Enhancement for AI Agent Collaboration) and (No. RS- 2022-00187238, Development of Large Korean Language Model Technology for Efficient Pre-training).
Yong-Ju Lee(yongju@etri.re.kr)