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Collecting environment information...
PyTorch version: 2.2.2+cu121
Is debug build: False
CUDA used to build PyTorch: 12.1
ROCM used to build PyTorch: N/A

OS: Debian GNU/Linux 12 (bookworm) (x86_64)
GCC version: (Debian 12.2.0-14) 12.2.0
Clang version: Could not collect
CMake version: Could not collect
Libc version: glibc-2.36

Python version: 3.11.9 (main, Apr 10 2024, 14:54:51) [GCC 12.2.0] (64-bit runtime)
Python platform: Linux-6.5.0-15-generic-x86_64-with-glibc2.36
Is CUDA available: True
CUDA runtime version: Could not collect
CUDA_MODULE_LOADING set to: LAZY
GPU models and configuration: 
GPU 0: NVIDIA H100 80GB HBM3
GPU 1: NVIDIA H100 80GB HBM3
GPU 2: NVIDIA H100 80GB HBM3
GPU 3: NVIDIA H100 80GB HBM3

Nvidia driver version: 550.54.14
cuDNN version: Could not collect
HIP runtime version: N/A
MIOpen runtime version: N/A
Is XNNPACK available: True

CPU:
Architecture:                       x86_64
CPU op-mode(s):                     32-bit, 64-bit
Address sizes:                      46 bits physical, 57 bits virtual
Byte Order:                         Little Endian
CPU(s):                             96
On-line CPU(s) list:                0-95
Vendor ID:                          GenuineIntel
Model name:                         Intel(R) Xeon(R) Gold 6442Y
CPU family:                         6
Model:                              143
Thread(s) per core:                 2
Core(s) per socket:                 24
Socket(s):                          2
Stepping:                           8
BogoMIPS:                           5200.00
Flags:                              fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush dts acpi mmx fxsr sse sse2 ss ht tm pbe syscall nx pdpe1gb rdtscp lm constant_tsc art arch_perfmon pebs bts rep_good nopl xtopology nonstop_tsc cpuid aperfmperf tsc_known_freq pni pclmulqdq dtes64 monitor ds_cpl vmx smx est tm2 ssse3 sdbg fma cx16 xtpr pdcm pcid dca sse4_1 sse4_2 x2apic movbe popcnt tsc_deadline_timer aes xsave avx f16c rdrand lahf_lm abm 3dnowprefetch cpuid_fault epb cat_l3 cat_l2 cdp_l3 invpcid_single cdp_l2 ssbd mba ibrs ibpb stibp ibrs_enhanced tpr_shadow flexpriority ept vpid ept_ad fsgsbase tsc_adjust bmi1 avx2 smep bmi2 erms invpcid cqm rdt_a avx512f avx512dq rdseed adx smap avx512ifma clflushopt clwb intel_pt avx512cd sha_ni avx512bw avx512vl xsaveopt xsavec xgetbv1 xsaves cqm_llc cqm_occup_llc cqm_mbm_total cqm_mbm_local split_lock_detect avx_vnni avx512_bf16 wbnoinvd dtherm ida arat pln pts hfi vnmi avx512vbmi umip pku ospke waitpkg avx512_vbmi2 gfni vaes vpclmulqdq avx512_vnni avx512_bitalg tme avx512_vpopcntdq la57 rdpid bus_lock_detect cldemote movdiri movdir64b enqcmd fsrm md_clear serialize tsxldtrk pconfig arch_lbr ibt amx_bf16 avx512_fp16 amx_tile amx_int8 flush_l1d arch_capabilities
Virtualization:                     VT-x
L1d cache:                          2.3 MiB (48 instances)
L1i cache:                          1.5 MiB (48 instances)
L2 cache:                           96 MiB (48 instances)
L3 cache:                           120 MiB (2 instances)
NUMA node(s):                       2
NUMA node0 CPU(s):                  0,2,4,6,8,10,12,14,16,18,20,22,24,26,28,30,32,34,36,38,40,42,44,46,48,50,52,54,56,58,60,62,64,66,68,70,72,74,76,78,80,82,84,86,88,90,92,94
NUMA node1 CPU(s):                  1,3,5,7,9,11,13,15,17,19,21,23,25,27,29,31,33,35,37,39,41,43,45,47,49,51,53,55,57,59,61,63,65,67,69,71,73,75,77,79,81,83,85,87,89,91,93,95
Vulnerability Gather data sampling: Not affected
Vulnerability Itlb multihit:        Not affected
Vulnerability L1tf:                 Not affected
Vulnerability Mds:                  Not affected
Vulnerability Meltdown:             Not affected
Vulnerability Mmio stale data:      Not affected
Vulnerability Retbleed:             Not affected
Vulnerability Spec rstack overflow: Not affected
Vulnerability Spec store bypass:    Mitigation; Speculative Store Bypass disabled via prctl
Vulnerability Spectre v1:           Mitigation; usercopy/swapgs barriers and __user pointer sanitization
Vulnerability Spectre v2:           Mitigation; Enhanced / Automatic IBRS, IBPB conditional, RSB filling, PBRSB-eIBRS SW sequence
Vulnerability Srbds:                Not affected
Vulnerability Tsx async abort:      Not affected

Versions of relevant libraries:
[pip3] numpy==1.26.4
[pip3] onnxruntime==1.17.3
[pip3] torch==2.2.2
[pip3] torchvision==0.17.2
[pip3] triton==2.2.0
[conda] Could not collectROCM Version: Could not collect
Neuron SDK Version: N/A
vLLM Version: N/A
vLLM Build Flags:
CUDA Archs: Not Set; ROCm: Disabled; Neuron: Disabled
GPU Topology:
GPU0	GPU1	GPU2	GPU3	CPU Affinity	NUMA Affinity	GPU NUMA ID
GPU0	 X 	NV6	NV6	NV6	0,2,4,6,8,10	0		N/A
GPU1	NV6	 X 	NV6	NV6	0,2,4,6,8,10	0		N/A
GPU2	NV6	NV6	 X 	NV6	1,3,5,7,9,11	1		N/A
GPU3	NV6	NV6	NV6	 X 	1,3,5,7,9,11	1		N/A

Legend:

  X    = Self
  SYS  = Connection traversing PCIe as well as the SMP interconnect between NUMA nodes (e.g., QPI/UPI)
  NODE = Connection traversing PCIe as well as the interconnect between PCIe Host Bridges within a NUMA node
  PHB  = Connection traversing PCIe as well as a PCIe Host Bridge (typically the CPU)
  PXB  = Connection traversing multiple PCIe bridges (without traversing the PCIe Host Bridge)
  PIX  = Connection traversing at most a single PCIe bridge
  NV#  = Connection traversing a bonded set of # NVLinks```

### 🐛 Describe the bug

I fine tuned the Mixtral 8x7b  model with lora ,  I included the layer **lm_head** in lora    so in my tensors has this "base_model.model.lm_head.base_layer.weight , in  code vllm/lora/utils.py" : it doesn't support or ignore it .

      def parse_fine_tuned_lora_name(name: str) -> Tuple[str, bool]:
          """Parse the name of lora weights.
      
          args:
              name: the name of the fine-tuned LoRA, e.g.
                  base_model.model.dense1.weight
          return:
              Tuple(module_name, is_lora_a):
                  module_name: the name of the module, e.g. model.dense1,
                  is_lora_a whether the tensor is lora_a or lora_b.
          """
          parts = name.split(".")
          assert parts[0] == "base_model"
          assert parts[1] == "model"
          if parts[-1] == "weight":
              assert parts[-2] == "lora_A" or parts[-2] == "lora_B"
              return ".".join(parts[2:-2]), parts[-2] == "lora_A"
          
          if parts[-1] == "lora_embedding_A" or parts[-1] == "lora_embedding_B":
              return ".".join(parts[2:-1]), parts[-1] == "lora_embedding_A"
          
          raise ValueError(f"{name} is unsupported format")
    
    

    
I am getting this error:
    Traceback (most recent call last):
  File "/workspace/vllm/lora/worker_manager.py", line 139, in _load_lora
    lora = self._lora_model_cls.from_local_checkpoint(
  File "/workspace/vllm/lora/models.py", line 227, in from_local_checkpoint
    return cls.from_lora_tensors(
  File "/workspace/vllm/lora/models.py", line 148, in from_lora_tensors
    module_name, is_lora_a = parse_fine_tuned_lora_name(tensor_name)
  File "/workspace/vllm/lora/utils.py", line 33, in parse_fine_tuned_lora_name
    assert parts[-2] == "lora_A" or parts[-2] == "lora_B"
AssertionError