Instructions to use bingaochen/hl-planner-simple-subgoal with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use bingaochen/hl-planner-simple-subgoal with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("/inspire/qb-ilm/project/robot-learning-system/public/wudongming/models/Qwen/Qwen3-VL-4B-Instruct") model = PeftModel.from_pretrained(base_model, "bingaochen/hl-planner-simple-subgoal") - Notebooks
- Google Colab
- Kaggle
metadata
license: mit
base_model: Qwen/Qwen3-VL-4B-Instruct
library_name: peft
pipeline_tag: image-text-to-text
tags:
- lora
- peft
- qwen3-vl
- robotics
- subgoal-prediction
hl-planner-simple-subgoal
LoRA adapter on Qwen3-VL-4B-Instruct for robot subgoal prediction from setup video + task goal.
- Base:
Qwen/Qwen3-VL-4B-Instruct - Adapter: LoRA (~132 MB safetensors)
- License: MIT
Inference env vars (required)
These MUST match training; setting them higher (or leaving the defaults) silently degrades quality.
export IMAGE_MAX_TOKEN_NUM=128
export VIDEO_MAX_TOKEN_NUM=128
export FPS_MAX_FRAMES=10
Use
Load via peft on top of Qwen3-VL with the env vars above set BEFORE
importing swift.llm / transformers.