---
license: other
base_model: moonshotai/Kimi-K2.6
library_name: tinker
tags:
  - lora
  - rl
  - opus-magnum
  - python
---

# opus-k26-py-step150-2026-05-02

LoRA adapter trained with reinforcement learning (GRPO via Thinking Machines'
Tinker SDK) on the Opus-Magnum puzzle-solving REPL benchmark, snapshotted at
training step **150**.

## Training setup

- **Base model:** `moonshotai/Kimi-K2.6`
- **Renderer:** `kimi_k25`
- **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://0aedf8c7-c9ad-57de-b8d5-d451fd058fde:train:0/sampler_weights/000150
```

## 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:

```python
from tinker_cookbook.weights import build_lora_adapter

build_lora_adapter(
    base_model="moonshotai/Kimi-K2.6",
    adapter_path="./tinker_adapter",   # this repo's contents
    output_path="./peft_adapter",
)
```