GLM4.7 Flash

1. Model Introduction

GLM-4.7-Flash is a lightweight, high-speed MoE model in the GLM-4.7 series from Zhipu AI, designed for single-GPU-node deployment. Key highlights:

Compact MoE architecture: 30 B total / 3 B active, sparse activation for efficient inference.
MLA attention: Multi-head Latent Attention with q-LoRA rank 768 and kv-LoRA rank 512.
MTP head + EAGLE speculative: built-in --mtp-num-layers 1 and EAGLE rollout enabled by default.
R3 on by default: both miles launchers enable --use-rollout-routing-replay out of the box.

2. Supported Variants

Model	Active / Total	HF ID
GLM-4.7-Flash	3 B / 30 B	zai-org/GLM-4.7-Flash

3. Environment Setup

3.1 Download model + datasets

hf download zai-org/GLM-4.7-Flash --local-dir /root/shared/GLM-4.7-Flash
hf download --repo-type dataset zhuzilin/dapo-math-17k --local-dir /root/shared/dapo-math-17k
hf download --repo-type dataset zhuzilin/aime-2024     --local-dir /root/shared/aime-2024

The bash launcher hardcodes BASE_DIR=/root/shared. The Python launcher downloads zhuzilin/dapo-math-17k and zhuzilin/aime-2024 automatically.

3.2 HF → Megatron `torch_dist` conversion

cd /root/miles
source scripts/models/glm4.7-flash.sh
PYTHONPATH=/root/Megatron-LM torchrun --nproc-per-node 8 \
   tools/convert_hf_to_torch_dist.py \
   ${MODEL_ARGS[@]} \
   --hf-checkpoint /root/shared/GLM-4.7-Flash \
   --save          /root/shared/GLM-4.7-Flash_torch_dist

The Python launcher does the conversion automatically.

4. Launch

4.1 Quick start

cd /root/miles
bash scripts/run-glm4.7-flash.sh

# Python launcher (H200 only — `hardware` literal in the dataclass)
python scripts/run_glm47_flash.py

Defaults of the Python launcher (see ScriptArgs): model_org=zai-org, model_name=GLM-4.7-Flash, num_gpus_per_node=8, hardware=H200, data_dir=/root/datasets, model_dir=/root/models.

5. Recipe Configuration

5.1 Parallelism

TP	PP	CP	EP	expert-TP	`max_tokens_per_gpu`	GPUs
4	1	1	8	1	32768	8 (1 × 8)

--rollout-num-gpus-per-engine 4 (TP must divide 20 attention heads, so TP=4). The bash launcher’s SGLANG_ARGS keeps --sglang-enable-dp-attention / --sglang-dp-size commented out — the in-source comment notes that DP-attention requires tp_size % dp_size == 0.

5.2 Algorithm

GRPO with --eps-clip 0.2 --eps-clip-high 0.28 --use-kl-loss --kl-loss-coef 0.00.

5.3 Rollout & SGLang

SGLANG_ARGS=(
   --rollout-num-gpus-per-engine 4
   --sglang-mem-fraction-static 0.7

   # EAGLE speculative decoding (MTP)
   --sglang-speculative-algorithm EAGLE
   --sglang-speculative-num-steps 2
   --sglang-speculative-eagle-topk 1
   --sglang-speculative-num-draft-tokens 3

   # R3 — on by default in this script
   --use-rollout-routing-replay
)

5.4 Optimizer

CPU Adam on:

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

5.5 Notable quirks

Megatron-side DeepEP / flex dispatcher are commented out by default in this recipe.
R3 (--use-rollout-routing-replay) is enabled by default — atypical for the rest of the model lineup.

6. Pairs Well With

Rollout Routing Replay (R3) — already on by default.
Low Precision RL

​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