> ## Documentation Index
> Fetch the complete documentation index at: https://miles.radixark.com/docs/llms.txt
> Use this file to discover all available pages before exploring further.

# Thinking Machines

> Miles recipes for Thinking Machines Lab models — Inkling (975 B), a multimodal MoE with short convolution, relative attention, and a shared-expert sink.

Miles ships a native Megatron recipe for **Inkling**, Thinking Machines Lab's 975 B / 41 B-active multimodal mixture-of-experts model: local and global relative attention, the residual ShortConv, the shared-sink router and experts, and the image and audio encoders. The same backend drives both full-parameter and LoRA RL.

## Variants

| Model         | Active / Total | Layers | HF ID                                                                                   | Recipe                                                  |
| ------------- | -------------- | ------ | --------------------------------------------------------------------------------------- | ------------------------------------------------------- |
| Inkling       | 41 B / 975 B   | 66     | [thinkingmachines/Inkling](https://huggingface.co/thinkingmachines/Inkling)             | [Inkling](/docs/models/thinkingmachines/inkling)             |
| Inkling-Small | 12 B / 276 B   | 42     | [thinkingmachines/Inkling-Small](https://huggingface.co/thinkingmachines/Inkling-Small) | [Inkling-Small](/docs/models/thinkingmachines/inkling-small) |

## Fastest path to train

Inkling needs 16 nodes of 4× GB300 and the `radixark/miles:inkling` image:

```bash theme={null}
cd /root/miles
python scripts/run_inkling.py train \
   --model-name Inkling --train-mode full --task dapo_math \
   --num-nodes 16 --num-gpus-per-node 4
```

See the [Inkling](/docs/models/thinkingmachines/inkling) page for the architecture summary, HF → Megatron conversion, validated parallelism layouts, training attention backends, LoRA RL, and multimodal RL.