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A high-throughput and memory-efficient inference and serving engine for LLMs

Mirrored from https://github.com/tduncan-lum/Nvidia-Optimization-Stack---VLLM.git

Synced Nov 5, 02:15 UTC

[Bugfix] add support for 'num_attention_groups' in ModelArchConfigConvertorBase for Step3p5 (#39796)

Signed-off-by: realliujiaxu <realliujiaxu@163.com>

realliujiaxu committed Apr 16, 2026 at 13:48 UTC 7845379230c48c9103ab71682ee05977dfbbdfce
3 files changed +20
tests/config/base_model_arch_groundtruth.json
+17
@@ -356,6 +356,23 @@
356 "is_multimodal_model": false,
357 "dtype": "torch.float32"
358 },
359 + "stepfun-ai/Step-3.5-Flash": {
360 + "architectures": [
361 + "Step3p5ForCausalLM"
362 + ],
363 + "model_type": "step3p5",
364 + "text_model_type": "step3p5",
365 + "hidden_size": 4096,
366 + "total_num_hidden_layers": 45,
367 + "total_num_attention_heads": 64,
368 + "head_size": 128,
369 + "vocab_size": 128896,
370 + "total_num_kv_heads": 8,
371 + "num_experts": 288,
372 + "is_deepseek_mla": false,
373 + "is_multimodal_model": false,
374 + "dtype": "torch.bfloat16"
375 + },
376 "nvidia/NVIDIA-Nemotron-3-Nano-30B-A3B-BF16": {
377 "architectures": [
378 "NemotronHForCausalLM"
tests/config/test_model_arch_config.py
+1
@@ -16,6 +16,7 @@ BASE_TRUST_REMOTE_CODE_MODELS = {
16 "nvidia/Llama-3_3-Nemotron-Super-49B-v1",
17 "nvidia/NVIDIA-Nemotron-3-Nano-30B-A3B-BF16",
18 "XiaomiMiMo/MiMo-7B-RL",
19 + "stepfun-ai/Step-3.5-Flash",
20 # Excluded: Not available online right now
21 # "FreedomIntelligence/openPangu-Ultra-MoE-718B-V1.1",
22 "meituan-longcat/LongCat-Flash-Chat",
vllm/transformers_utils/model_arch_config_convertor.py
+2
@@ -77,6 +77,8 @@ class ModelArchConfigConvertorBase:
77 "num_key_value_heads",
78 # For ChatGLM:
79 "multi_query_group_num",
80 + # For Step3p5:
81 + "num_attention_groups",
82 ]
83 # For non-grouped-query attention models, the number of KV heads is
84 # equal to the number of attention heads.