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base_model: Qwen/Qwen3.5-4B
library_name: peft
tags:
- tinker
- peft
- lora
- opus-magnum
---
# opus-4b-dsl-mixed-step150-2026-04-29
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 150 / 300.
## Source training run
- wandb: [opus-4b-dsl-mixed-2026-04-29 (mqhz79iy)](https://wandb.ai/websim/opus-task/runs/mqhz79iy)
- tinker checkpoint: `tinker://addea7a1-fbe5-59b8-a467-5de3736c3404:train:0/sampler_weights/000150`
- distances: 1, 2, 3, 4
- task types: move, transmute, bond
- hard_task_fraction: 0.15 (cap on bond + d=4 share of train pool)
- learning rate: 1e-5
- group size: 8, groups per batch: 16
- renderer: qwen3_5_disable_thinking
## Usage
```python
from peft import PeftModel
from transformers import AutoModelForCausalLM, AutoTokenizer
base = "Qwen/Qwen3.5-4B"
adapter = "maxbittker/opus-4b-dsl-mixed-step150-2026-04-29"
tok = AutoTokenizer.from_pretrained(base)
model = AutoModelForCausalLM.from_pretrained(base, device_map="auto")
model = PeftModel.from_pretrained(model, adapter)
```
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