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GoodStartLabs/
opus-27b-py-step210-2026-05-02

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opus-27b-py-step210-2026-05-02

LoRA adapter trained with reinforcement learning (GRPO via Thinking Machines' Tinker SDK) on the Opus-Magnum puzzle-solving REPL benchmark, snapshotted at training step 210.

Training setup

Base model: Qwen/Qwen3.5-27B
Renderer: qwen3_5_disable_thinking
Representation: python (action language the agent emits)
Adapter: LoRA, rank 32
RL recipe: GRPO via Tinker
Hyperparameters:
- learning_rate = 1e-5
- group_size = 8, groups_per_batch = 16
- max_tokens = 1024, max_trajectory_tokens = 12000
- distances = 1,2,3,4
- max_steps_off_policy = None
- save_every = 5

Files

adapter_model.safetensors — Tinker raw LoRA adapter weights
adapter_config.json — adapter metadata (rank, alpha, target modules)
README.md — this file

Provenance

Tinker checkpoint:

tinker://c533f7ee-d0f6-567f-90ec-90ecc549733c:train:0/sampler_weights/000210

Converting to PEFT format

The files above are in Tinker's raw adapter format. To convert to PEFT format suitable for direct vLLM --lora-modules loading, run on a machine that can host the base model: