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modules_to_save tensors
(embed_tokens, lm_head, and norm layers) that vLLM's LoRA runtime does not
support. This upload contains only the pure low-rank lora_A/lora_B weights
(448 pairs: 64 layers x q/k/v/o/gate/up/down projections), with
modules_to_save: null in adapter_config.json. No resize_token_embeddings()
call is needed to load this adapter.1python -m vllm.entrypoints.openai.api_server \
2 --model Qwen/Qwen3-32B \
3 --enable-lora \
4 --lora-modules prover=anjohn0077/NEXS-qwen3-32b-prover-lora \
5 --port 8000 \
6 --max-lora-rank 128 \
7 --gpu-memory-utilization 0.85lora_A/lora_B
keys, complete A/B pairs, correct Qwen3-32B shapes, no NaN/Inf) and loads and
generates through vLLM's LoRA runtime.