WindyLab

Qwen3-0.6B-cybertown-SFT

Deploy Dedicated

Dedicated Endpoints

Run this model inference on single tenant GPU with unmatched speed and reliability at scale.

Learn more

Container

Run this model inference with full control and performance in your environment.

Learn more

Get help setting up a custom Dedicated Endpoints.

Talk with our engineer to get a quote for reserved GPU instances with discounts.

README

Training Data Distribution

By Source

Table
source	count	ratio
first_plan_success	1083	31.6%
replan_success	2340	68.4%

By Goal Type

Table
goal_type	count	ratio
assembly	1145	33.5%
transport	1010	29.5%
guidance	356	10.4%
emergency_response	355	10.4%
target_following	331	9.7%
traffic_enforcement	226	6.6%

Intended Use

This model is intended for Cybertown semantic task planning and replanning experiments. It outputs structured planning responses for downstream validation and reinforcement learning.

Loading

python
from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "WindyLab/Qwen3-0.6B-cybertown-SFT"
tokenizer = AutoTokenizer.from_pretrained(model_id, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype="auto", device_map="auto")