How to use from the
Use from the
PEFT library
from peft import PeftModel
from transformers import AutoModelForCausalLM

base_model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen3.5-4B")
model = PeftModel.from_pretrained(base_model, "maxbittker/opus-4b-dsl-step170-2026-04-29")

opus-4b-dsl-step170-2026-04-29

LoRA adapter (rank 32) trained with RL on a custom Opus-Magnum-style motion-planning task using the dsl answer representation. Snapshot at training step 170 / 300.

Source training run

  • wandb: opus-4b-dsl-2026-04-29 (onya5lq7)
  • tinker checkpoint: tinker://883a6f46-abad-5e6e-b740-9c0051523362:train:0/sampler_weights/000170
  • distances: 1, 2, 3
  • task types: move, transmute
  • learning rate: 1e-5
  • group size: 8, groups per batch: 16
  • renderer: qwen3_5_disable_thinking

Usage

from peft import PeftModel
from transformers import AutoModelForCausalLM, AutoTokenizer

base = "Qwen/Qwen3.5-4B"
adapter = "maxbittker/opus-4b-dsl-step170-2026-04-29"
tok = AutoTokenizer.from_pretrained(base)
model = AutoModelForCausalLM.from_pretrained(base, device_map="auto")
model = PeftModel.from_pretrained(model, adapter)
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