hchoi256/MBA-Bench
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How to use hchoi256/mba with Transformers:
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("image-text-to-text", model="hchoi256/mba") # Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("hchoi256/mba", device_map="auto")How to use hchoi256/mba with vLLM:
# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "hchoi256/mba"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
-H "Content-Type: application/json" \
--data '{
"model": "hchoi256/mba",
"prompt": "Once upon a time,",
"max_tokens": 512,
"temperature": 0.5
}'docker model run hf.co/hchoi256/mba
How to use hchoi256/mba with SGLang:
# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
--model-path "hchoi256/mba" \
--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": "hchoi256/mba",
"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 "hchoi256/mba" \
--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": "hchoi256/mba",
"prompt": "Once upon a time,",
"max_tokens": 512,
"temperature": 0.5
}'How to use hchoi256/mba with Docker Model Runner:
docker model run hf.co/hchoi256/mba
Official models for MBA: Multimodal Benchmark and Agents for Real-World Business Ideation.
docker model run hf.co/hchoi256/mba