File size: 1,520 Bytes
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license: other
base_model: moonshotai/Kimi-K2.6
library_name: tinker
tags:
- lora
- rl
- opus-magnum
- dsl
---
# opus-k26-dsl-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:** `dsl` (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://94a8b593-adf6-5f99-a986-4dd98d183887: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",
)
```
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