Moonlight - Miles

1. Model Introduction

Moonlight is Moonshot AI’s compact MoE — 16 B total / 3 B active, trained with the Muon optimizer — and a useful single-node test target for MoE RL code changes before scaling to Kimi K2. Key highlights:

Compact MoE: 16 B total / 3 B active, 27 layers (1 dense + 26 MoE), 64 routed experts top-6 + 2 shared.
MLA attention: Multi-head Latent Attention with kv-LoRA rank 512.
Single-node footprint: full RL recipe fits on 1 × 8 H100.
Muon-trained base: pretrained with the Muon optimizer; weight decay matters at scale.

2. Supported Variants

Model	Active / Total	HF ID
Moonlight-16B-A3B	3 B / 16 B	moonshotai/Moonlight-16B-A3B

3. Environment Setup

3.1 Download model + datasets

hf download moonshotai/Moonlight-16B-A3B --local-dir /root/Moonlight-16B-A3B
hf download --repo-type dataset zhuzilin/dapo-math-17k --local-dir /root/dapo-math-17k
hf download --repo-type dataset zhuzilin/aime-2024     --local-dir /root/aime-2024

3.2 HF → Megatron `torch_dist` conversion

cd /root/miles
source scripts/models/moonlight.sh
PYTHONPATH=/root/Megatron-LM torchrun --nproc-per-node 8 \
   tools/convert_hf_to_torch_dist.py \
   ${MODEL_ARGS[@]} \
   --hf-checkpoint /root/Moonlight-16B-A3B \
   --save          /root/Moonlight-16B-A3B_torch_dist

4. Launch

4.1 Quick start

cd /root/miles
bash scripts/run-moonlight-16B-A3B.sh

5. Recipe Configuration

5.1 Parallelism

TP	PP	CP	EP	expert-TP	`max_tokens_per_gpu`	GPUs
4	1	1	8	1	8192	8 (1 × 8)

5.2 Algorithm

GRPO with --eps-clip 0.2 --eps-clip-high 0.28 --use-kl-loss --kl-loss-coef 0.00. R3 is not enabled.

ROLLOUT_ARGS=(
   --rm-type math
   --num-rollout 3000
   --rollout-batch-size 128
   --n-samples-per-prompt 8
   --rollout-max-response-len 4096
   --rollout-temperature 1

   --over-sampling-batch-size 256
   --dynamic-sampling-filter-path miles.rollout.filter_hub.dynamic_sampling_filters.check_reward_nonzero_std

   --num-steps-per-rollout 4
   --balance-data
)

5.3 Rollout & SGLang

SGLANG_ARGS=(
   --rollout-num-gpus-per-engine 8
   --sglang-mem-fraction-static 0.7
   --sglang-cuda-graph-bs 1 2 4 8 $(seq 16 8 256)
)

Megatron-side DeepEP is on: --moe-enable-deepep --moe-token-dispatcher-type flex.

5.4 Optimizer

CPU Adam on:

--optimizer-cpu-offload
--overlap-cpu-optimizer-d2h-h2d
--use-precision-aware-optimizer

5.5 Notable quirks

--attention-backend flash is commented out in this script (script comment: “need to comment this when using model with MLA”).

​1. Model Introduction

​2. Supported Variants

​3. Environment Setup

​3.1 Download model + datasets

​3.2 HF → Megatron torch_dist conversion

​4. Launch

​4.1 Quick start

​5. Recipe Configuration

​5.1 Parallelism

​5.2 Algorithm

​5.3 Rollout & SGLang

​5.4 Optimizer

​5.5 Notable quirks

​6. Pairs Well With