Instructions to use GoodStartLabs/opus-4b-dsl-step180-2026-04-29 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use GoodStartLabs/opus-4b-dsl-step180-2026-04-29 with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen3.5-4B") model = PeftModel.from_pretrained(base_model, "GoodStartLabs/opus-4b-dsl-step180-2026-04-29") - Notebooks
- Google Colab
- Kaggle
opus-4b-dsl-step180-2026-04-29
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 180 / 300.
Source training run
- wandb: opus-4b-dsl-2026-04-29 (onya5lq7)
- tinker checkpoint:
tinker://883a6f46-abad-5e6e-b740-9c0051523362:train:0/sampler_weights/000180 - distances: 1, 2, 3
- task types: move, transmute (no bond)
- 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-step180-2026-04-29"
tok = AutoTokenizer.from_pretrained(base)
model = AutoModelForCausalLM.from_pretrained(base, device_map="auto")
model = PeftModel.from_pretrained(model, adapter)
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