Learning Self-Orchestration in Decentralized Agent Swarms
Paper โข 2610.22322 โข Published โข 7
How to use Xkev/mc_society_sft with Transformers:
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("image-text-to-text", model="Xkev/mc_society_sft") # pip install -U transformers accelerate
# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("Xkev/mc_society_sft", device_map="auto")How to use Xkev/mc_society_sft with vLLM:
# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Xkev/mc_society_sft"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
-H "Content-Type: application/json" \
--data '{
"model": "Xkev/mc_society_sft",
"prompt": "Once upon a time,",
"max_tokens": 512,
"temperature": 0.5
}'docker model run hf.co/Xkev/mc_society_sft
How to use Xkev/mc_society_sft with SGLang:
# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
--model-path "Xkev/mc_society_sft" \
--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": "Xkev/mc_society_sft",
"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 "Xkev/mc_society_sft" \
--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": "Xkev/mc_society_sft",
"prompt": "Once upon a time,",
"max_tokens": 512,
"temperature": 0.5
}'How to use Xkev/mc_society_sft with Docker Model Runner:
docker model run hf.co/Xkev/mc_society_sft
Paper: Learning Self-Orchestration in Decentralized Agent Swarms
Code: https://github.com/Embodied-Minds-Lab/MPT
For the post-trained model initialized from this checkpoint, see Xkev/mc_society.
For the single-agent counterpart, see Xkev/mc_single_sft.
Qwen/Qwen3-VL-4B-InstructTrainerlr=1e-5, lr_warmup_ratio=0.03, cosine schedule, epochs=2Research on multi-agent post-training and embodied agents. Not intended for general dialog or production.
Apache-2.0.
Base model
Qwen/Qwen3-VL-4B-Instruct