To Reproduce Steps to reproduce the behavior:

1. On 1.4-rc0 image in GCP ( 8xH100)  ( registry.stage.redhat.io/rhelai1/bootc-gcp-nvidia-rhel9:1.4-1738349195 ) download the models:

2. ilab model download --repository docker://registry.stage.redhat.io/rhelai1/granite-8b-lab-v1 --release 1.4
   ilab model download --repository docker://registry.stage.redhat.io/rhelai1/skills-adapter-v3 --release 1.4
   ilab model download --repository docker://registry.stage.redhat.io/rhelai1/knowledge-adapter-v3 --release 1.4
   ilab model download --repository docker://registry.stage.redhat.io/rhelai1/mixtral-8x7b-instruct-v0-1 --release 1.4
   ilab model download --repository docker://registry.stage.redhat.io/rhelai1/prometheus-8x7b-v2-0 --release 1.4
   ilab model download --repository docker://registry.stage.redhat.io/rhelai1/granite-8b-starter-v1 --release 1.4

1. Run SDG
2. After about 75 minutes ( ran command like: `time ilab data generate` ) SDG stops with this error:

3. INFO 2025-02-03 11:07:04,715 instructlab.sdg.datamixing:43: Rebalancing dataset to have 10395 samples ...
   Map (num_proc=8): 100%|##########| 10395/10395 [00:08<00:00, 1294.75 examples/s]
   INFO 2025-02-03 11:07:24,745 instructlab.model.backends.vllm:494: Waiting for GPU VRAM reclamation...
   failed to generate data with exception: struct fields don't match or are in the wrong order: Input fields: struct<content: string, role: string> output fields: struct<role: string, content: 
   string>

1. The dataset contents:

2. [cloud-user@ecosystem-qe-2 2025-02-03_095244]$ pwd
   /var/home/cloud-user/.local/share/instructlab/datasets/2025-02-03_095244
   [cloud-user@ecosystem-qe-2 2025-02-03_095244]$ ls -lsa
   total 9048
      4 drwxr-xr-x. 5 cloud-user cloud-user    4096 Feb  3 11:06 .
      0 drwxr-xr-x. 4 cloud-user cloud-user      50 Feb  3 10:00 ..
      4 drwxr-xr-x. 2 cloud-user cloud-user    4096 Feb  3 11:05 generated_2025-02-03T09_55_05
      4 -rw-r--r--. 1 cloud-user cloud-user     471 Feb  3 11:06 knowledge_recipe_2025-02-03T09_55_05.yaml
   4324 -rw-r--r--. 1 cloud-user cloud-user 4425768 Feb  3 11:06 messages_2025-02-03T09_55_05.jsonl
      4 drwxr-xr-x. 2 cloud-user cloud-user    4096 Feb  3 11:06 node_datasets_2025-02-03T09_55_05
      4 drwxr-xr-x. 3 cloud-user cloud-user    4096 Feb  3 09:55 preprocessed_2025-02-03T09_55_05
      4 -rw-r--r--. 1 cloud-user cloud-user     911 Feb  3 11:06 skills_recipe_2025-02-03T09_55_05.yaml
    932 -rw-r--r--. 1 cloud-user cloud-user  950538 Feb  3 09:55 test_2025-02-03T09_55_05.jsonl
   3768 -rw-r--r--. 1 cloud-user cloud-user 3856014 Feb  3 11:06 train_2025-02-03T09_55_05.jsonl

Expected behavior

• SDG to complete successfully 

Screenshots

• Attached Image

Device Info (please complete the following information):

• Hardware Specs: GCP a3-highgpu-8g
• OS Version: RHEL AI 1.4 rc0
• InstructLab Version: ilab, version 0.23.1
• Provide the output of these two commands:
• sudo bootc status --format json | jq .status.booted.image.image.image to print the name and tag of the bootc image, should look like 
• registry.stage.redhat.io/rhelai1/bootc-intel-rhel9:1.3-1732894187
•  
•  

  [cloud-user@ecosystem-qe-2 2025-02-03_095244]$ ilab system info                                                                 
  ggml_cuda_init: GGML_CUDA_FORCE_MMQ:    no                                                                                   
  ggml_cuda_init: GGML_CUDA_FORCE_CUBLAS: no                                                                                   
  ggml_cuda_init: found 8 CUDA devices:                                                                                         
    Device 0: NVIDIA H100 80GB HBM3, compute capability 9.0, VMM: yes                                                           
    Device 1: NVIDIA H100 80GB HBM3, compute capability 9.0, VMM: yes                                                           
    Device 2: NVIDIA H100 80GB HBM3, compute capability 9.0, VMM: yes                                                           
    Device 3: NVIDIA H100 80GB HBM3, compute capability 9.0, VMM: yes                                                           
    Device 4: NVIDIA H100 80GB HBM3, compute capability 9.0, VMM: yes                                                           
    Device 5: NVIDIA H100 80GB HBM3, compute capability 9.0, VMM: yes                                                           
    Device 6: NVIDIA H100 80GB HBM3, compute capability 9.0, VMM: yes                                                           
    Device 7: NVIDIA H100 80GB HBM3, compute capability 9.0, VMM: yes                                                           
  Platform:                                                                                                                     
    sys.version: 3.11.7 (main, Jan  8 2025, 00:00:00) [GCC 11.4.1 20231218 (Red Hat 11.4.1-3)]                                 
    sys.platform: linux                                                                                                         
    os.name: posix                                                                                                             
    platform.release: 5.14.0-427.50.1.el9_4.x86_64                                                                             
    platform.machine: x86_64                                                                                                   
    platform.node: ecosystem-qe-2                                                  
    platform.python_version: 3.11.7                                                                                             
    os-release.ID: rhel                                                                                                          
    os-release.VERSION_ID: 9.4                                                                                                 
    os-release.PRETTY_NAME: Red Hat Enterprise Linux 9.4 (Plow)                                                                 
    memory.total: 1842.58 GB                                                                                                   
    memory.available: 1831.53 GB                                                                                               
    memory.used: 2.89 GB                                                                                                       
  InstructLab:
    instructlab.version: 0.23.1
    instructlab-dolomite.version: 0.2.0
    instructlab-eval.version: 0.5.1
    instructlab-quantize.version: 0.1.0
    instructlab-schema.version: 0.4.2
    instructlab-sdg.version: 0.7.0
    instructlab-training.version: 0.7.0 Torch:
    torch.version: 2.5.1
    torch.backends.cpu.capability: AVX512
    torch.version.cuda: 12.4
    torch.version.hip: None
    torch.cuda.available: True
    torch.backends.cuda.is_built: True
    torch.backends.mps.is_built: False
    torch.backends.mps.is_available: False
    torch.cuda.bf16: True
    torch.cuda.current.device: 0
    torch.cuda.0.name: NVIDIA H100 80GB HBM3
    torch.cuda.0.free: 78.6 GB
    torch.cuda.0.total: 79.1 GB
    torch.cuda.0.capability: 9.0 (see https://developer.nvidia.com/cuda-gpus#compute)
    torch.cuda.1.name: NVIDIA H100 80GB HBM3
    torch.cuda.1.free: 78.6 GB
    torch.cuda.1.total: 79.1 GB
    torch.cuda.1.capability: 9.0 (see https://developer.nvidia.com/cuda-gpus#compute)
    torch.cuda.2.name: NVIDIA H100 80GB HBM3
    torch.cuda.2.free: 78.6 GB
    torch.cuda.2.total: 79.1 GB
    torch.cuda.2.capability: 9.0 (see https://developer.nvidia.com/cuda-gpus#compute)
    torch.cuda.3.name: NVIDIA H100 80GB HBM3
    torch.cuda.3.free: 78.6 GB
    torch.cuda.3.total: 79.1 GB
    torch.cuda.3.capability: 9.0 (see https://developer.nvidia.com/cuda-gpus#compute)
    torch.cuda.4.name: NVIDIA H100 80GB HBM3
    torch.cuda.4.free: 78.6 GB
    torch.cuda.4.total: 79.1 GB
    torch.cuda.4.capability: 9.0 (see https://developer.nvidia.com/cuda-gpus#compute)
    torch.cuda.5.name: NVIDIA H100 80GB HBM3
    torch.cuda.5.free: 78.6 GB
    torch.cuda.5.total: 79.1 GB
    torch.cuda.5.capability: 9.0 (see https://developer.nvidia.com/cuda-gpus#compute)
    torch.cuda.6.name: NVIDIA H100 80GB HBM3
    torch.cuda.6.free: 78.6 GB
    torch.cuda.6.total: 79.1 GB
    torch.cuda.6.capability: 9.0 (see https://developer.nvidia.com/cuda-gpus#compute)
  
    torch.cuda.7.name: NVIDIA H100 80GB HBM3
    torch.cuda.7.free: 78.6 GB
    torch.cuda.7.total: 79.1 GB
    torch.cuda.7.capability: 9.0 (see https://developer.nvidia.com/cuda-gpus#compute)
  llama_cpp_python:
    llama_cpp_python.version: 0.3.2
    llama_cpp_python.supports_gpu_offload: True
  

Bug impact

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Known workaround

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Additional context

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