Qwen3.8-Flash-Next-E384-FP4-V2

A compact build of nvidia/Qwen3.8-Flash-Next-FP4-Local found by Neural Architecture Search (NAS) over the routed-expert configuration: it keeps 384 of the 512 routed experts in every decoder MoE layer and in the MTP layer.

Unchanged from the original:

• top-10 routing and the shared experts;
• attention, the n-gram PLE table, the vision tower and the tokenizer.

This is an unofficial derivative, not produced or endorsed by Alibaba or NVIDIA.

	original (512 experts)	E384
weights	102.14 GB = 95.12 GiB	85.66 GB = 79.78 GiB (−15.35 GiB, −16.1 %)
routed experts / layer (decoder + MTP)	512	384
active experts / token	10 + shared	10 + shared

Full merged checkpoint of the E384 FP4 model after GRPO-style reinforcement learning with a LoRA adapter. This is a complete model, not an adapter: all 21 weight shards, the tokenizer, configs and MTP files are included. It is an unofficial derivative and is not endorsed by Alibaba or NVIDIA.

Serving

Validated with vLLM 0.31 on one B200. The included vllm_patch/sitecustomize.py is needed for this checkpoint's FP4/CSF/PLE weight layout. From this directory:

export PYTHONPATH="$PWD/vllm_patch${PYTHONPATH:+:$PYTHONPATH}"
vllm serve . --served-model-name e384-rl --port 8000 \
  --tensor-parallel-size 1 --dtype bfloat16 --kv-cache-dtype fp8 \
  --quantization modelopt_mixed --mamba-cache-mode align \
  --enable-prefix-caching --enable-chunked-prefill \
  --max-model-len 72000 --max-num-seqs 128 --gpu-memory-utilization 0.90 \
  --reasoning-parser qwen3 --tool-call-parser qwen3_xml --enable-auto-tool-choice

serve_vllm.sh is the equivalent portable launcher. The model was benchmarked through this serving path, including the FP8 KV cache and the Qwen reasoning/tool parsers.

Training and checkpoint contents

• Three GRPO-style LoRA updates with verifiable rewards; the final round targets short-budget CVSS completion and structured findings emission.
• LoRA rank 32, alpha 64. The routed experts, router, shared expert and PLE are frozen.
• The GDN projection deltas are folded at full precision and those projections are stored as BF16; the corresponding entries are removed from quantization_config.quantized_layers. Other model tensors retain the base checkpoint format.
• The final adapter was merged into the weights before this repository was created. No adapter loading step is needed.

Full benchmark results

Same benchmark harness and serving settings for all rows. These are single runs; GPQA has only 198 questions and is correspondingly noisy. AIME 2026 uses 30 problems x 4 samples with xhigh reasoning and a 64k generation budget. Base numbers are the previously published reference numbers, not a rerun.

benchmark	base reference	this model
GPQA-Diamond (low, 32k)	77.3	75.76
MMLU (14,042)	86.9	87.37
C-Eval val (1,346)	89.0	89.97
AIME 2026 (xhigh, 64k)	93.3	95.0
HumanEval (164, greedy)	97.6	98.78
MMMU val (900)	79.4	78.22
tool calling (800)	—	77.88

Detailed per-task counts and truncation statistics are in benchmark_results.json.

AIME reasoning length

Compared with the preceding RL checkpoint on the same 120 AIME samples, total completion tokens fell 2.05% (19,897 to 19,490 per answer on average), while accuracy rose from 92.5 to 95.0. The median was essentially unchanged (16,528 to 16,675 tokens); the reduction came mainly from the long tail (P90 58,272 to 45,339) and fewer 65,536-token truncations (9 to 6). completion_tokens includes both the reasoning trace and the final answer.

Behavior notes and limitations

On the same training-prompt evaluation used during the final RL round, CVSS3 completion within a 2,200 token budget rose from 39% to 70%, CVSS4 from 18% to 60%, and structured-findings emission reward from 0.609 to 0.917. Math and guard rewards did not degrade in that evaluation. These are training-prompt measurements, not an independent behavior benchmark.

CVSS arithmetic is still not reliable: among completed answers, exact agreement with the reference implementation was about 4.5% for CVSS3 and 4.2% for CVSS4 in that evaluation. This checkpoint should not be described as having fully fixed CVSS scoring. See behavior_results.json for the detailed comparison.