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=== [Fri May 22 15:03:42 UTC 2026] Phase 0: setup_env.sh ===
Retrieving notices: - \ | / - \ | done
Channels:
 - conda-forge
Platform: linux-64
Collecting package metadata (repodata.json): - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - done
Solving environment: | / done


==> WARNING: A newer version of conda exists. <==
    current version: 26.1.1
    latest version: 26.5.0

Please update conda by running

    $ conda update -n base -c conda-forge conda



## Package Plan ##

  environment location: /home/lakesenberg/miniforge3/envs/nemo

  added / updated specs:
    - python=3.11


The following packages will be downloaded:

    package                    |            build
    ---------------------------|-----------------
    ca-certificates-2026.5.20  |       hbd8a1cb_0         127 KB  conda-forge
    libexpat-2.8.1             |       hecca717_0          75 KB  conda-forge
    libgcc-15.2.0              |      he0feb66_19        1017 KB  conda-forge
    libgcc-ng-15.2.0           |      h69a702a_19          27 KB  conda-forge
    libgomp-15.2.0             |      he0feb66_19         590 KB  conda-forge
    libsqlite-3.53.1           |       h0c1763c_0         933 KB  conda-forge
    libuuid-2.42.1             |       h5347b49_0          39 KB  conda-forge
    ncurses-6.6                |       hdb14827_0         897 KB  conda-forge
    pip-26.1.1                 |     pyh8b19718_0         1.1 MB  conda-forge
    python-3.11.15             |hd63d673_0_cpython        29.5 MB  conda-forge
    ------------------------------------------------------------
                                           Total:        34.3 MB

The following NEW packages will be INSTALLED:

  _openmp_mutex      conda-forge/linux-64::_openmp_mutex-4.5-20_gnu 
  bzip2              conda-forge/linux-64::bzip2-1.0.8-hda65f42_9 
  ca-certificates    conda-forge/noarch::ca-certificates-2026.5.20-hbd8a1cb_0 
  ld_impl_linux-64   conda-forge/linux-64::ld_impl_linux-64-2.45.1-default_hbd61a6d_102 
  libexpat           conda-forge/linux-64::libexpat-2.8.1-hecca717_0 
  libffi             conda-forge/linux-64::libffi-3.5.2-h3435931_0 
  libgcc             conda-forge/linux-64::libgcc-15.2.0-he0feb66_19 
  libgcc-ng          conda-forge/linux-64::libgcc-ng-15.2.0-h69a702a_19 
  libgomp            conda-forge/linux-64::libgomp-15.2.0-he0feb66_19 
  liblzma            conda-forge/linux-64::liblzma-5.8.3-hb03c661_0 
  libnsl             conda-forge/linux-64::libnsl-2.0.1-hb9d3cd8_1 
  libsqlite          conda-forge/linux-64::libsqlite-3.53.1-h0c1763c_0 
  libuuid            conda-forge/linux-64::libuuid-2.42.1-h5347b49_0 
  libxcrypt          conda-forge/linux-64::libxcrypt-4.4.36-hd590300_1 
  libzlib            conda-forge/linux-64::libzlib-1.3.2-h25fd6f3_2 
  ncurses            conda-forge/linux-64::ncurses-6.6-hdb14827_0 
  openssl            conda-forge/linux-64::openssl-3.6.2-h35e630c_0 
  packaging          conda-forge/noarch::packaging-26.2-pyhc364b38_0 
  pip                conda-forge/noarch::pip-26.1.1-pyh8b19718_0 
  python             conda-forge/linux-64::python-3.11.15-hd63d673_0_cpython 
  readline           conda-forge/linux-64::readline-8.3-h853b02a_0 
  setuptools         conda-forge/noarch::setuptools-82.0.1-pyh332efcf_0 
  tk                 conda-forge/linux-64::tk-8.6.13-noxft_h366c992_103 
  tzdata             conda-forge/noarch::tzdata-2025c-hc9c84f9_1 
  wheel              conda-forge/noarch::wheel-0.47.0-pyhd8ed1ab_0 
  zstd               conda-forge/linux-64::zstd-1.5.7-hb78ec9c_6 



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libsqlite-3.53.1     | 933 KB    | ########## | 100% 
python-3.11.15       | 29.5 MB   | ####5      |  46% 





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python-3.11.15       | 29.5 MB   | #######3   |  73% 







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ncurses-6.6          | 897 KB    | ########## | 100% 




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                                                      done
Preparing transaction: \ | done
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#
# To activate this environment, use
#
#     $ conda activate nemo
#
# To deactivate an active environment, use
#
#     $ conda deactivate

Requirement already satisfied: pip in /home/lakesenberg/miniforge3/envs/nemo/lib/python3.11/site-packages (26.1.1)
Requirement already satisfied: wheel in /home/lakesenberg/miniforge3/envs/nemo/lib/python3.11/site-packages (0.47.0)
Requirement already satisfied: packaging>=24.0 in /home/lakesenberg/miniforge3/envs/nemo/lib/python3.11/site-packages (from wheel) (26.2)
Looking in indexes: https://download.pytorch.org/whl/cu124
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[?25hCollecting nvidia-cublas-cu12==12.4.5.8 (from torch>=2.4)
  Using cached nvidia_cublas_cu12-12.4.5.8-py3-none-manylinux2014_x86_64.whl (363.4 MB)
Collecting nvidia-cufft-cu12==11.2.1.3 (from torch>=2.4)
  Using cached nvidia_cufft_cu12-11.2.1.3-py3-none-manylinux2014_x86_64.whl (211.5 MB)
Collecting nvidia-curand-cu12==10.3.5.147 (from torch>=2.4)
  Using cached nvidia_curand_cu12-10.3.5.147-py3-none-manylinux2014_x86_64.whl (56.3 MB)
Collecting nvidia-cusolver-cu12==11.6.1.9 (from torch>=2.4)
  Using cached nvidia_cusolver_cu12-11.6.1.9-py3-none-manylinux2014_x86_64.whl (127.9 MB)
Collecting nvidia-cusparse-cu12==12.3.1.170 (from torch>=2.4)
  Using cached nvidia_cusparse_cu12-12.3.1.170-py3-none-manylinux2014_x86_64.whl (207.5 MB)
Collecting nvidia-cusparselt-cu12==0.6.2 (from torch>=2.4)
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Collecting nvidia-nccl-cu12==2.21.5 (from torch>=2.4)
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Collecting nvidia-nvtx-cu12==12.4.127 (from torch>=2.4)
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Collecting nvidia-nvjitlink-cu12==12.4.127 (from torch>=2.4)
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Collecting triton==3.2.0 (from torch>=2.4)
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Collecting sympy==1.13.1 (from torch>=2.4)
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Collecting mpmath<1.4,>=1.1.0 (from sympy==1.13.1->torch>=2.4)
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Collecting MarkupSafe>=2.0 (from jinja2->torch>=2.4)
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Installing collected packages: triton, nvidia-cusparselt-cu12, mpmath, typing-extensions, sympy, nvidia-nvtx-cu12, nvidia-nvjitlink-cu12, nvidia-nccl-cu12, nvidia-curand-cu12, nvidia-cufft-cu12, nvidia-cuda-runtime-cu12, nvidia-cuda-nvrtc-cu12, nvidia-cuda-cupti-cu12, nvidia-cublas-cu12, networkx, MarkupSafe, fsspec, filelock, nvidia-cusparse-cu12, nvidia-cudnn-cu12, jinja2, nvidia-cusolver-cu12, torch
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Installing collected packages: xxhash, urllib3, tqdm, six, shellingham, sentencepiece, safetensors, regex, pyyaml, pygments, pyarrow, psutil, propcache, numpy, multidict, mdurl, idna, hf-xet, h11, fsspec, frozenlist, dill, click, charset_normalizer, certifi, attrs, annotated-doc, aiohappyeyeballs, yarl, requests, python-dateutil, multiprocess, markdown-it-py, httpcore, anyio, aiosignal, rich, pandas, httpx, aiohttp, typer, huggingface_hub, tokenizers, datasets, accelerate, transformers, peft
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  Attempting uninstall: fsspec
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    Found existing installation: fsspec 2026.4.0
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    Uninstalling fsspec-2026.4.0:
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Successfully installed accelerate-1.13.0 aiohappyeyeballs-2.6.2 aiohttp-3.13.5 aiosignal-1.4.0 annotated-doc-0.0.4 anyio-4.13.0 attrs-26.1.0 certifi-2026.5.20 charset_normalizer-3.4.7 click-8.4.1 datasets-4.8.5 dill-0.4.1 frozenlist-1.8.0 fsspec-2026.2.0 h11-0.16.0 hf-xet-1.5.0 httpcore-1.0.9 httpx-0.28.1 huggingface_hub-1.16.1 idna-3.16 markdown-it-py-4.2.0 mdurl-0.1.2 multidict-6.7.1 multiprocess-0.70.19 numpy-2.4.6 pandas-3.0.3 peft-0.19.1 propcache-0.5.2 psutil-7.2.2 pyarrow-24.0.0 pygments-2.20.0 python-dateutil-2.9.0.post0 pyyaml-6.0.3 regex-2026.5.9 requests-2.34.2 rich-15.0.0 safetensors-0.7.0 sentencepiece-0.2.1 shellingham-1.5.4 six-1.17.0 tokenizers-0.22.2 tqdm-4.67.3 transformers-5.9.0 typer-0.25.1 urllib3-2.7.0 xxhash-3.7.0 yarl-1.24.2
Collecting flash-attn
  Downloading flash_attn-2.8.3.tar.gz (8.4 MB)
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[?25h  Preparing metadata (pyproject.toml): started
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Using cached einops-0.8.2-py3-none-any.whl (65 kB)
Building wheels for collected packages: flash-attn
  Building wheel for flash-attn (pyproject.toml): started
  Building wheel for flash-attn (pyproject.toml): finished with status 'done'
  Created wheel for flash-attn: filename=flash_attn-2.8.3-cp311-cp311-linux_x86_64.whl size=256022485 sha256=0abc62d04f28f140f4f76ab7cfd1d8ce24a69c6ab0cbace8d4ab99640b68dc0a
  Stored in directory: /home/lakesenberg/.cache/pip/wheels/42/31/1f/4b22dd7295b3cb064b8fa9038f6d58fb15c9571555b2d7c39c
Successfully built flash-attn
Installing collected packages: einops, flash-attn
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[?25h
Successfully installed einops-0.8.2 flash-attn-2.8.3
[info] downloading nvidia/NVIDIA-Nemotron-3-Nano-30B-A3B-BF16 -> /home/lakesenberg/models/nemotron-3-nano-30b-a3b-bf16
Warning: `huggingface-cli` is deprecated and no longer works. Use `hf` instead.

Hint: `hf` is already installed! Use it directly.

Hint: Examples:
  hf auth login
  hf download unsloth/gemma-4-31B-it-GGUF
  hf upload my-cool-model . .
  hf models ls --search "gemma"
  hf repos ls --format json
  hf jobs run python:3.12 python -c 'print("Hello!")'
  hf --help

EXIT_CODE=1

=== RESTART Fri May 22 15:20:38 UTC 2026 ===

=== [Fri May 22 15:20:38 UTC 2026] RESTART: Phase 0 setup + model download ===
Requirement already satisfied: pip in /home/lakesenberg/miniforge3/envs/nemo/lib/python3.11/site-packages (26.1.1)
Requirement already satisfied: wheel in /home/lakesenberg/miniforge3/envs/nemo/lib/python3.11/site-packages (0.47.0)
Requirement already satisfied: packaging>=24.0 in /home/lakesenberg/miniforge3/envs/nemo/lib/python3.11/site-packages (from wheel) (26.2)
Looking in indexes: https://download.pytorch.org/whl/cu124
Requirement already satisfied: torch>=2.4 in /home/lakesenberg/miniforge3/envs/nemo/lib/python3.11/site-packages (2.6.0+cu124)
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Requirement already satisfied: nvidia-cuda-runtime-cu12==12.4.127 in /home/lakesenberg/miniforge3/envs/nemo/lib/python3.11/site-packages (from torch>=2.4) (12.4.127)
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Requirement already satisfied: nvidia-cublas-cu12==12.4.5.8 in /home/lakesenberg/miniforge3/envs/nemo/lib/python3.11/site-packages (from torch>=2.4) (12.4.5.8)
Requirement already satisfied: nvidia-cufft-cu12==11.2.1.3 in /home/lakesenberg/miniforge3/envs/nemo/lib/python3.11/site-packages (from torch>=2.4) (11.2.1.3)
Requirement already satisfied: nvidia-curand-cu12==10.3.5.147 in /home/lakesenberg/miniforge3/envs/nemo/lib/python3.11/site-packages (from torch>=2.4) (10.3.5.147)
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Requirement already satisfied: nvidia-nccl-cu12==2.21.5 in /home/lakesenberg/miniforge3/envs/nemo/lib/python3.11/site-packages (from torch>=2.4) (2.21.5)
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WARNING: huggingface-hub 1.16.1 does not provide the extra 'cli'
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[info] skipping flash-attn (default). Set INSTALL_FLASH_ATTN=1 to try.
[info] downloading nvidia/NVIDIA-Nemotron-3-Nano-30B-A3B-BF16 -> /home/lakesenberg/models/nemotron-3-nano-30b-a3b-bf16
path=/home/lakesenberg/models/nemotron-3-nano-30b-a3b-bf16

=== Environment ready ===
  env       : nemo
  base model: /home/lakesenberg/models/nemotron-3-nano-30b-a3b-bf16

Next:  conda activate nemo && bash run_train.sh
=== [Fri May 22 15:23:06 UTC 2026] Phase 1: smoke test (20 steps) ===
[stage 1] generating 100 examples per task -> /home/lakesenberg/nemotron_lora_work/smoke_cot.jsonl
wrote 700 examples to /home/lakesenberg/nemotron_lora_work/smoke_cot.jsonl

--- sample ---
### Task: cryptarithm
### Problem:
Cryptarithm-style mapping.  Examples:
  14,51 -> 1451
  13,96 -> 1396
  11,68 -> 1168
Apply to: 42,37
### Reasoning:
<|cot_start|>Examples:
  14,51 -> 1451
  13,96 -> 1396
  11,68 -> 1168
Check both candidate forms:
  concat(14,51)=1451  rev_concat(14,51)=5114  target=1451  concat
  concat(13,96)=1396  rev_concat(13,96)=9613  target=1396  concat
  concat(11,68)=1168  rev_concat(11,68)=6811  target=1168  concat
Conclude operator: concat.
Apply to (42, 37):  answer = 4237
Final: \boxed{4237}

[stage 2] training LoRA on /home/lakesenberg/models/nemotron-3-nano-30b-a3b-bf16
[transformers] `torch_dtype` is deprecated! Use `dtype` instead!
=== TrainConfig ===
  base_model: /home/lakesenberg/models/nemotron-3-nano-30b-a3b-bf16
  data_path: /home/lakesenberg/nemotron_lora_work/smoke_cot.jsonl
  out_dir: /home/lakesenberg/nemotron_lora_work/smoke
  lora_rank: 32
  lora_alpha: 32
  lora_dropout: 0.0
  target_modules: ('q_proj', 'k_proj', 'v_proj', 'o_proj', 'gate_proj', 'up_proj', 'down_proj', 'x_proj', 'in_proj', 'out_proj', 'dt_proj', 'experts.w1', 'experts.w2')
  lr: 0.0001
  weight_decay: 0.0
  betas: (0.9, 0.95)
  max_grad_norm: 1.0
  warmup_steps: 50
  total_steps: 20
  grad_accum: 1
  batch_size: 1
  seq_len: 1024
  save_every: 500
  log_every: 10
  seed: 0
  bf16: True
  use_flash_attn: False
  gradient_checkpointing: True
  resume_adapter: None
Loading /home/lakesenberg/models/nemotron-3-nano-30b-a3b-bf16 ...
Traceback (most recent call last):
  File "/home/lakesenberg/.cache/huggingface/modules/transformers_modules/nemotron_hyphen_3_hyphen_nano_hyphen_30b_hyphen_a3b_hyphen_bf16/f06245fae7870619/modeling_nemotron_h.py", line 64, in <module>
    from mamba_ssm.ops.triton.layernorm_gated import rmsnorm_fn
ModuleNotFoundError: No module named 'mamba_ssm'

During handling of the above exception, another exception occurred:

Traceback (most recent call last):
  File "/home/lakesenberg/code/nemotron_lora_train/train_nemotron_lora_h200.py", line 315, in <module>
    train(cfg)
  File "/home/lakesenberg/code/nemotron_lora_train/train_nemotron_lora_h200.py", line 221, in train
    model = build_model(cfg)
            ^^^^^^^^^^^^^^^^
  File "/home/lakesenberg/code/nemotron_lora_train/train_nemotron_lora_h200.py", line 187, in build_model
    model = AutoModelForCausalLM.from_pretrained(cfg.base_model, **kwargs)
            ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/home/lakesenberg/miniforge3/envs/nemo/lib/python3.11/site-packages/transformers/models/auto/auto_factory.py", line 379, in from_pretrained
    model_class = get_class_from_dynamic_module(
                  ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/home/lakesenberg/miniforge3/envs/nemo/lib/python3.11/site-packages/transformers/dynamic_module_utils.py", line 627, in get_class_from_dynamic_module
    return get_class_in_module(class_name, final_module, force_reload=force_download)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/home/lakesenberg/miniforge3/envs/nemo/lib/python3.11/site-packages/transformers/dynamic_module_utils.py", line 309, in get_class_in_module
    module_spec.loader.exec_module(module)
  File "<frozen importlib._bootstrap_external>", line 940, in exec_module
  File "<frozen importlib._bootstrap>", line 241, in _call_with_frames_removed
  File "/home/lakesenberg/.cache/huggingface/modules/transformers_modules/nemotron_hyphen_3_hyphen_nano_hyphen_30b_hyphen_a3b_hyphen_bf16/f06245fae7870619/modeling_nemotron_h.py", line 66, in <module>
    raise ImportError("mamba-ssm is required by the Mamba model but cannot be imported")
ImportError: mamba-ssm is required by the Mamba model but cannot be imported
EXIT_CODE=1

=== RESUME2 Fri May 22 16:37:57 UTC 2026 ===
=== [Fri May 22 16:37:57 UTC 2026] RESUME: verify mamba-ssm ===
mamba_ssm OK
=== [Fri May 22 16:37:59 UTC 2026] Phase 1: smoke test (20 steps) ===
[stage 1] data file exists, skipping: /home/lakesenberg/nemotron_lora_work/smoke_cot.jsonl
[stage 2] training LoRA on /home/lakesenberg/models/nemotron-3-nano-30b-a3b-bf16
[transformers] `torch_dtype` is deprecated! Use `dtype` instead!
=== TrainConfig ===
  base_model: /home/lakesenberg/models/nemotron-3-nano-30b-a3b-bf16
  data_path: /home/lakesenberg/nemotron_lora_work/smoke_cot.jsonl
  out_dir: /home/lakesenberg/nemotron_lora_work/smoke
  lora_rank: 32
  lora_alpha: 32
  lora_dropout: 0.0
  target_modules: ('q_proj', 'k_proj', 'v_proj', 'o_proj', 'gate_proj', 'up_proj', 'down_proj', 'x_proj', 'in_proj', 'out_proj', 'dt_proj', 'experts.w1', 'experts.w2')
  lr: 0.0001
  weight_decay: 0.0
  betas: (0.9, 0.95)
  max_grad_norm: 1.0
  warmup_steps: 50
  total_steps: 20
  grad_accum: 1
  batch_size: 1
  seq_len: 1024
  save_every: 500
  log_every: 10
  seed: 0
  bf16: True
  use_flash_attn: False
  gradient_checkpointing: True
  resume_adapter: None
Loading /home/lakesenberg/models/nemotron-3-nano-30b-a3b-bf16 ...

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trainable params: 883,873,792 || all params: 32,461,811,136 || trainable%: 2.7228
[transformers] `use_return_dict` is deprecated! Use `return_dict` instead!
Traceback (most recent call last):
  File "/home/lakesenberg/code/nemotron_lora_train/train_nemotron_lora_h200.py", line 315, in <module>
    train(cfg)
  File "/home/lakesenberg/code/nemotron_lora_train/train_nemotron_lora_h200.py", line 254, in train
    out = model(input_ids=input_ids, attention_mask=attn_mask, use_cache=False)
          ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/home/lakesenberg/miniforge3/envs/nemo/lib/python3.11/site-packages/torch/nn/modules/module.py", line 1739, in _wrapped_call_impl
    return self._call_impl(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/home/lakesenberg/miniforge3/envs/nemo/lib/python3.11/site-packages/torch/nn/modules/module.py", line 1750, in _call_impl
    return forward_call(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/home/lakesenberg/miniforge3/envs/nemo/lib/python3.11/site-packages/peft/peft_model.py", line 1993, in forward
    return self.base_model(
           ^^^^^^^^^^^^^^^^
  File "/home/lakesenberg/miniforge3/envs/nemo/lib/python3.11/site-packages/torch/nn/modules/module.py", line 1739, in _wrapped_call_impl
    return self._call_impl(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/home/lakesenberg/miniforge3/envs/nemo/lib/python3.11/site-packages/torch/nn/modules/module.py", line 1750, in _call_impl
    return forward_call(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/home/lakesenberg/miniforge3/envs/nemo/lib/python3.11/site-packages/peft/tuners/tuners_utils.py", line 330, in forward
    return self.model.forward(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/home/lakesenberg/.cache/huggingface/modules/transformers_modules/nemotron_hyphen_3_hyphen_nano_hyphen_30b_hyphen_a3b_hyphen_bf16/f06245fae7870619/modeling_nemotron_h.py", line 1702, in forward
    nemotron_h_outputs = self.backbone(
                         ^^^^^^^^^^^^^^
  File "/home/lakesenberg/miniforge3/envs/nemo/lib/python3.11/site-packages/torch/nn/modules/module.py", line 1739, in _wrapped_call_impl
    return self._call_impl(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/home/lakesenberg/miniforge3/envs/nemo/lib/python3.11/site-packages/torch/nn/modules/module.py", line 1750, in _call_impl
    return forward_call(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/home/lakesenberg/.cache/huggingface/modules/transformers_modules/nemotron_hyphen_3_hyphen_nano_hyphen_30b_hyphen_a3b_hyphen_bf16/f06245fae7870619/modeling_nemotron_h.py", line 1489, in forward
    hidden_states = self._gradient_checkpointing_func(
                    ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/home/lakesenberg/miniforge3/envs/nemo/lib/python3.11/site-packages/torch/_compile.py", line 32, in inner
    return disable_fn(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/home/lakesenberg/miniforge3/envs/nemo/lib/python3.11/site-packages/torch/_dynamo/eval_frame.py", line 745, in _fn
    return fn(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^
  File "/home/lakesenberg/miniforge3/envs/nemo/lib/python3.11/site-packages/torch/utils/checkpoint.py", line 496, in checkpoint
    ret = function(*args, **kwargs)
          ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/home/lakesenberg/miniforge3/envs/nemo/lib/python3.11/site-packages/torch/nn/modules/module.py", line 1739, in _wrapped_call_impl
    return self._call_impl(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/home/lakesenberg/miniforge3/envs/nemo/lib/python3.11/site-packages/torch/nn/modules/module.py", line 1750, in _call_impl
    return forward_call(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/home/lakesenberg/.cache/huggingface/modules/transformers_modules/nemotron_hyphen_3_hyphen_nano_hyphen_30b_hyphen_a3b_hyphen_bf16/f06245fae7870619/modeling_nemotron_h.py", line 777, in forward
    hidden_states = self.mixer(
                    ^^^^^^^^^^^
  File "/home/lakesenberg/miniforge3/envs/nemo/lib/python3.11/site-packages/torch/nn/modules/module.py", line 1739, in _wrapped_call_impl
    return self._call_impl(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/home/lakesenberg/miniforge3/envs/nemo/lib/python3.11/site-packages/torch/nn/modules/module.py", line 1750, in _call_impl
    return forward_call(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/home/lakesenberg/.cache/huggingface/modules/transformers_modules/nemotron_hyphen_3_hyphen_nano_hyphen_30b_hyphen_a3b_hyphen_bf16/f06245fae7870619/modeling_nemotron_h.py", line 715, in forward
    return self.cuda_kernels_forward(hidden_states, cache_params, cache_position, attention_mask)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/home/lakesenberg/.cache/huggingface/modules/transformers_modules/nemotron_hyphen_3_hyphen_nano_hyphen_30b_hyphen_a3b_hyphen_bf16/f06245fae7870619/modeling_nemotron_h.py", line 429, in cuda_kernels_forward
    out = mamba_split_conv1d_scan_combined(
          ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/home/lakesenberg/miniforge3/envs/nemo/lib/python3.11/site-packages/mamba_ssm/ops/triton/ssd_combined.py", line 997, in mamba_split_conv1d_scan_combined
    return MambaSplitConv1dScanCombinedFn.apply(zxbcdt, conv1d_weight, conv1d_bias, dt_bias, A, D, chunk_size, initial_states, seq_idx, dt_limit, return_final_states, activation, rmsnorm_weight, rmsnorm_eps, outproj_weight, outproj_bias, headdim, ngroups, norm_before_gate)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/home/lakesenberg/miniforge3/envs/nemo/lib/python3.11/site-packages/torch/autograd/function.py", line 575, in apply
    return super().apply(*args, **kwargs)  # type: ignore[misc]
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/home/lakesenberg/miniforge3/envs/nemo/lib/python3.11/site-packages/torch/amp/autocast_mode.py", line 503, in decorate_fwd
    return fwd(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^
  File "/home/lakesenberg/miniforge3/envs/nemo/lib/python3.11/site-packages/mamba_ssm/ops/triton/ssd_combined.py", line 841, in forward
    causal_conv1d_fwd_function(rearrange(ensure_stride(xBC), "b s d -> b d s"),
TypeError: 'NoneType' object is not callable
EXIT_CODE=1
=== RESUME3 Fri May 22 16:59:38 UTC 2026 ===
=== [Fri May 22 16:59:38 UTC 2026] patch Nemotron model cache (force slow mamba path) ===
=== [Fri May 22 16:59:38 UTC 2026] RESUME: verify mamba-ssm ===
mamba_ssm OK
=== [Fri May 22 16:59:40 UTC 2026] Phase 1: smoke test (20 steps) ===
[stage 1] data file exists, skipping: /home/lakesenberg/nemotron_lora_work/smoke_cot.jsonl
[stage 2] training LoRA on /home/lakesenberg/models/nemotron-3-nano-30b-a3b-bf16
[transformers] `torch_dtype` is deprecated! Use `dtype` instead!
=== TrainConfig ===
  base_model: /home/lakesenberg/models/nemotron-3-nano-30b-a3b-bf16
  data_path: /home/lakesenberg/nemotron_lora_work/smoke_cot.jsonl
  out_dir: /home/lakesenberg/nemotron_lora_work/smoke
  lora_rank: 32
  lora_alpha: 32
  lora_dropout: 0.0
  target_modules: ('q_proj', 'k_proj', 'v_proj', 'o_proj', 'gate_proj', 'up_proj', 'down_proj', 'x_proj', 'in_proj', 'out_proj', 'dt_proj', 'experts.w1', 'experts.w2')
  lr: 0.0001
  weight_decay: 0.0
  betas: (0.9, 0.95)
  max_grad_norm: 1.0
  warmup_steps: 50
  total_steps: 20
  grad_accum: 1
  batch_size: 1
  seq_len: 1024
  save_every: 500
  log_every: 10
  seed: 0
  bf16: True
  use_flash_attn: False
  gradient_checkpointing: True
  resume_adapter: None
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trainable params: 883,873,792 || all params: 32,461,811,136 || trainable%: 2.7228
[transformers] `use_return_dict` is deprecated! Use `return_dict` instead!
Traceback (most recent call last):
  File "/home/lakesenberg/code/nemotron_lora_train/train_nemotron_lora_h200.py", line 315, in <module>
    train(cfg)
  File "/home/lakesenberg/code/nemotron_lora_train/train_nemotron_lora_h200.py", line 254, in train
    out = model(input_ids=input_ids, attention_mask=attn_mask, use_cache=False)
          ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/home/lakesenberg/miniforge3/envs/nemo/lib/python3.11/site-packages/torch/nn/modules/module.py", line 1739, in _wrapped_call_impl
    return self._call_impl(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/home/lakesenberg/miniforge3/envs/nemo/lib/python3.11/site-packages/torch/nn/modules/module.py", line 1750, in _call_impl
    return forward_call(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/home/lakesenberg/miniforge3/envs/nemo/lib/python3.11/site-packages/peft/peft_model.py", line 1993, in forward
    return self.base_model(
           ^^^^^^^^^^^^^^^^
  File "/home/lakesenberg/miniforge3/envs/nemo/lib/python3.11/site-packages/torch/nn/modules/module.py", line 1739, in _wrapped_call_impl
    return self._call_impl(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/home/lakesenberg/miniforge3/envs/nemo/lib/python3.11/site-packages/torch/nn/modules/module.py", line 1750, in _call_impl
    return forward_call(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/home/lakesenberg/miniforge3/envs/nemo/lib/python3.11/site-packages/peft/tuners/tuners_utils.py", line 330, in forward
    return self.model.forward(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/home/lakesenberg/.cache/huggingface/modules/transformers_modules/nemotron_hyphen_3_hyphen_nano_hyphen_30b_hyphen_a3b_hyphen_bf16/f06245fae7870619/modeling_nemotron_h.py", line 1702, in forward
    nemotron_h_outputs = self.backbone(
                         ^^^^^^^^^^^^^^
  File "/home/lakesenberg/miniforge3/envs/nemo/lib/python3.11/site-packages/torch/nn/modules/module.py", line 1739, in _wrapped_call_impl
    return self._call_impl(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/home/lakesenberg/miniforge3/envs/nemo/lib/python3.11/site-packages/torch/nn/modules/module.py", line 1750, in _call_impl
    return forward_call(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/home/lakesenberg/.cache/huggingface/modules/transformers_modules/nemotron_hyphen_3_hyphen_nano_hyphen_30b_hyphen_a3b_hyphen_bf16/f06245fae7870619/modeling_nemotron_h.py", line 1489, in forward
    hidden_states = self._gradient_checkpointing_func(
                    ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/home/lakesenberg/miniforge3/envs/nemo/lib/python3.11/site-packages/torch/_compile.py", line 32, in inner
    return disable_fn(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/home/lakesenberg/miniforge3/envs/nemo/lib/python3.11/site-packages/torch/_dynamo/eval_frame.py", line 745, in _fn
    return fn(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^
  File "/home/lakesenberg/miniforge3/envs/nemo/lib/python3.11/site-packages/torch/utils/checkpoint.py", line 496, in checkpoint
    ret = function(*args, **kwargs)
          ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/home/lakesenberg/miniforge3/envs/nemo/lib/python3.11/site-packages/torch/nn/modules/module.py", line 1739, in _wrapped_call_impl
    return self._call_impl(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/home/lakesenberg/miniforge3/envs/nemo/lib/python3.11/site-packages/torch/nn/modules/module.py", line 1750, in _call_impl
    return forward_call(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/home/lakesenberg/.cache/huggingface/modules/transformers_modules/nemotron_hyphen_3_hyphen_nano_hyphen_30b_hyphen_a3b_hyphen_bf16/f06245fae7870619/modeling_nemotron_h.py", line 777, in forward
    hidden_states = self.mixer(
                    ^^^^^^^^^^^
  File "/home/lakesenberg/miniforge3/envs/nemo/lib/python3.11/site-packages/torch/nn/modules/module.py", line 1739, in _wrapped_call_impl
    return self._call_impl(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/home/lakesenberg/miniforge3/envs/nemo/lib/python3.11/site-packages/torch/nn/modules/module.py", line 1750, in _call_impl
    return forward_call(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/home/lakesenberg/.cache/huggingface/modules/transformers_modules/nemotron_hyphen_3_hyphen_nano_hyphen_30b_hyphen_a3b_hyphen_bf16/f06245fae7870619/modeling_nemotron_h.py", line 715, in forward
    return self.cuda_kernels_forward(hidden_states, cache_params, cache_position, attention_mask)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/home/lakesenberg/.cache/huggingface/modules/transformers_modules/nemotron_hyphen_3_hyphen_nano_hyphen_30b_hyphen_a3b_hyphen_bf16/f06245fae7870619/modeling_nemotron_h.py", line 429, in cuda_kernels_forward
    out = mamba_split_conv1d_scan_combined(
          ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/home/lakesenberg/miniforge3/envs/nemo/lib/python3.11/site-packages/mamba_ssm/ops/triton/ssd_combined.py", line 997, in mamba_split_conv1d_scan_combined
    return MambaSplitConv1dScanCombinedFn.apply(zxbcdt, conv1d_weight, conv1d_bias, dt_bias, A, D, chunk_size, initial_states, seq_idx, dt_limit, return_final_states, activation, rmsnorm_weight, rmsnorm_eps, outproj_weight, outproj_bias, headdim, ngroups, norm_before_gate)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/home/lakesenberg/miniforge3/envs/nemo/lib/python3.11/site-packages/torch/autograd/function.py", line 575, in apply
    return super().apply(*args, **kwargs)  # type: ignore[misc]
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/home/lakesenberg/miniforge3/envs/nemo/lib/python3.11/site-packages/torch/amp/autocast_mode.py", line 503, in decorate_fwd
    return fwd(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^
  File "/home/lakesenberg/miniforge3/envs/nemo/lib/python3.11/site-packages/mamba_ssm/ops/triton/ssd_combined.py", line 841, in forward
    causal_conv1d_fwd_function(rearrange(ensure_stride(xBC), "b s d -> b d s"),
TypeError: 'NoneType' object is not callable
EXIT_CODE=1
=== RESUME4 Fri May 22 17:02:26 UTC 2026 ===
=== [Fri May 22 17:02:27 UTC 2026] patch Nemotron model cache (force slow mamba path) ===
=== [Fri May 22 17:02:27 UTC 2026] RESUME: verify mamba-ssm ===
mamba_ssm OK
=== [Fri May 22 17:02:28 UTC 2026] Phase 1: smoke test (20 steps) ===
[stage 1] data file exists, skipping: /home/lakesenberg/nemotron_lora_work/smoke_cot.jsonl
[stage 2] training LoRA on /home/lakesenberg/models/nemotron-3-nano-30b-a3b-bf16
[transformers] `torch_dtype` is deprecated! Use `dtype` instead!
=== TrainConfig ===
  base_model: /home/lakesenberg/models/nemotron-3-nano-30b-a3b-bf16
  data_path: /home/lakesenberg/nemotron_lora_work/smoke_cot.jsonl
  out_dir: /home/lakesenberg/nemotron_lora_work/smoke
  lora_rank: 32
  lora_alpha: 32
  lora_dropout: 0.0
  target_modules: ('q_proj', 'k_proj', 'v_proj', 'o_proj', 'gate_proj', 'up_proj', 'down_proj', 'x_proj', 'in_proj', 'out_proj', 'dt_proj', 'experts.w1', 'experts.w2')
  lr: 0.0001
  weight_decay: 0.0
  betas: (0.9, 0.95)
  max_grad_norm: 1.0
  warmup_steps: 50
  total_steps: 20
  grad_accum: 1
  batch_size: 1
  seq_len: 1024
  save_every: 500
  log_every: 10
  seed: 0
  bf16: True
  use_flash_attn: False
  gradient_checkpointing: True
  resume_adapter: None
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trainable params: 883,873,792 || all params: 32,461,811,136 || trainable%: 2.7228
[transformers] `use_return_dict` is deprecated! Use `return_dict` instead!
Traceback (most recent call last):
  File "/home/lakesenberg/code/nemotron_lora_train/train_nemotron_lora_h200.py", line 315, in <module>
    train(cfg)
  File "/home/lakesenberg/code/nemotron_lora_train/train_nemotron_lora_h200.py", line 254, in train
    out = model(input_ids=input_ids, attention_mask=attn_mask, use_cache=False)
          ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/home/lakesenberg/miniforge3/envs/nemo/lib/python3.11/site-packages/torch/nn/modules/module.py", line 1739, in _wrapped_call_impl
    return self._call_impl(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/home/lakesenberg/miniforge3/envs/nemo/lib/python3.11/site-packages/torch/nn/modules/module.py", line 1750, in _call_impl
    return forward_call(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/home/lakesenberg/miniforge3/envs/nemo/lib/python3.11/site-packages/peft/peft_model.py", line 1993, in forward
    return self.base_model(
           ^^^^^^^^^^^^^^^^
  File "/home/lakesenberg/miniforge3/envs/nemo/lib/python3.11/site-packages/torch/nn/modules/module.py", line 1739, in _wrapped_call_impl
    return self._call_impl(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/home/lakesenberg/miniforge3/envs/nemo/lib/python3.11/site-packages/torch/nn/modules/module.py", line 1750, in _call_impl
    return forward_call(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/home/lakesenberg/miniforge3/envs/nemo/lib/python3.11/site-packages/peft/tuners/tuners_utils.py", line 330, in forward
    return self.model.forward(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/home/lakesenberg/.cache/huggingface/modules/transformers_modules/nemotron_hyphen_3_hyphen_nano_hyphen_30b_hyphen_a3b_hyphen_bf16/f06245fae7870619/modeling_nemotron_h.py", line 1702, in forward
    nemotron_h_outputs = self.backbone(
                         ^^^^^^^^^^^^^^
  File "/home/lakesenberg/miniforge3/envs/nemo/lib/python3.11/site-packages/torch/nn/modules/module.py", line 1739, in _wrapped_call_impl
    return self._call_impl(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/home/lakesenberg/miniforge3/envs/nemo/lib/python3.11/site-packages/torch/nn/modules/module.py", line 1750, in _call_impl
    return forward_call(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/home/lakesenberg/.cache/huggingface/modules/transformers_modules/nemotron_hyphen_3_hyphen_nano_hyphen_30b_hyphen_a3b_hyphen_bf16/f06245fae7870619/modeling_nemotron_h.py", line 1489, in forward
    hidden_states = self._gradient_checkpointing_func(
                    ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/home/lakesenberg/miniforge3/envs/nemo/lib/python3.11/site-packages/torch/_compile.py", line 32, in inner
    return disable_fn(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/home/lakesenberg/miniforge3/envs/nemo/lib/python3.11/site-packages/torch/_dynamo/eval_frame.py", line 745, in _fn
    return fn(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^
  File "/home/lakesenberg/miniforge3/envs/nemo/lib/python3.11/site-packages/torch/utils/checkpoint.py", line 496, in checkpoint
    ret = function(*args, **kwargs)
          ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/home/lakesenberg/miniforge3/envs/nemo/lib/python3.11/site-packages/torch/nn/modules/module.py", line 1739, in _wrapped_call_impl
    return self._call_impl(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/home/lakesenberg/miniforge3/envs/nemo/lib/python3.11/site-packages/torch/nn/modules/module.py", line 1750, in _call_impl
    return forward_call(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/home/lakesenberg/.cache/huggingface/modules/transformers_modules/nemotron_hyphen_3_hyphen_nano_hyphen_30b_hyphen_a3b_hyphen_bf16/f06245fae7870619/modeling_nemotron_h.py", line 777, in forward
    hidden_states = self.mixer(
                    ^^^^^^^^^^^
  File "/home/lakesenberg/miniforge3/envs/nemo/lib/python3.11/site-packages/torch/nn/modules/module.py", line 1739, in _wrapped_call_impl
    return self._call_impl(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/home/lakesenberg/miniforge3/envs/nemo/lib/python3.11/site-packages/torch/nn/modules/module.py", line 1750, in _call_impl
    return forward_call(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/home/lakesenberg/.cache/huggingface/modules/transformers_modules/nemotron_hyphen_3_hyphen_nano_hyphen_30b_hyphen_a3b_hyphen_bf16/f06245fae7870619/modeling_nemotron_h.py", line 715, in forward
    return self.cuda_kernels_forward(hidden_states, cache_params, cache_position, attention_mask)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/home/lakesenberg/.cache/huggingface/modules/transformers_modules/nemotron_hyphen_3_hyphen_nano_hyphen_30b_hyphen_a3b_hyphen_bf16/f06245fae7870619/modeling_nemotron_h.py", line 429, in cuda_kernels_forward
    out = mamba_split_conv1d_scan_combined(
          ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/home/lakesenberg/miniforge3/envs/nemo/lib/python3.11/site-packages/mamba_ssm/ops/triton/ssd_combined.py", line 997, in mamba_split_conv1d_scan_combined
    return MambaSplitConv1dScanCombinedFn.apply(zxbcdt, conv1d_weight, conv1d_bias, dt_bias, A, D, chunk_size, initial_states, seq_idx, dt_limit, return_final_states, activation, rmsnorm_weight, rmsnorm_eps, outproj_weight, outproj_bias, headdim, ngroups, norm_before_gate)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/home/lakesenberg/miniforge3/envs/nemo/lib/python3.11/site-packages/torch/autograd/function.py", line 575, in apply
    return super().apply(*args, **kwargs)  # type: ignore[misc]
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/home/lakesenberg/miniforge3/envs/nemo/lib/python3.11/site-packages/torch/amp/autocast_mode.py", line 503, in decorate_fwd
    return fwd(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^
  File "/home/lakesenberg/miniforge3/envs/nemo/lib/python3.11/site-packages/mamba_ssm/ops/triton/ssd_combined.py", line 841, in forward
    causal_conv1d_fwd_function(rearrange(ensure_stride(xBC), "b s d -> b d s"),
TypeError: 'NoneType' object is not callable
EXIT_CODE=1
=== RESUME5 Fri May 22 17:06:20 UTC 2026 ===
=== [Fri May 22 17:06:20 UTC 2026] patch Nemotron model (force slow mamba path) ===
=== [Fri May 22 17:06:20 UTC 2026] RESUME: verify mamba-ssm ===
mamba_ssm OK
=== [Fri May 22 17:06:21 UTC 2026] Phase 1: smoke test (20 steps) ===
[stage 1] data file exists, skipping: /home/lakesenberg/nemotron_lora_work/smoke_cot.jsonl
[stage 2] training LoRA on /home/lakesenberg/models/nemotron-3-nano-30b-a3b-bf16
[transformers] `torch_dtype` is deprecated! Use `dtype` instead!
The fast path is not available because on of `(selective_state_update, causal_conv1d_fn, causal_conv1d_update)` is None. Falling back to the naive implementation. To install follow https://github.com/state-spaces/mamba/#installation and https://github.com/Dao-AILab/causal-conv1d
=== TrainConfig ===
  base_model: /home/lakesenberg/models/nemotron-3-nano-30b-a3b-bf16
  data_path: /home/lakesenberg/nemotron_lora_work/smoke_cot.jsonl
  out_dir: /home/lakesenberg/nemotron_lora_work/smoke
  lora_rank: 32
  lora_alpha: 32
  lora_dropout: 0.0
  target_modules: ('q_proj', 'k_proj', 'v_proj', 'o_proj', 'gate_proj', 'up_proj', 'down_proj', 'x_proj', 'in_proj', 'out_proj', 'dt_proj', 'experts.w1', 'experts.w2')
  lr: 0.0001
  weight_decay: 0.0
  betas: (0.9, 0.95)
  max_grad_norm: 1.0
  warmup_steps: 50
  total_steps: 20
  grad_accum: 1
  batch_size: 1
  seq_len: 1024
  save_every: 500
  log_every: 10
  seed: 0
  bf16: True
  use_flash_attn: False
  gradient_checkpointing: True
  resume_adapter: None
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trainable params: 883,873,792 || all params: 32,461,811,136 || trainable%: 2.7228
[transformers] `use_return_dict` is deprecated! Use `return_dict` instead!
step    10/20 loss=0.4303 ema=0.6710 lr=2.00e-05 mem=69.1/78.7 GiB
step    20/20 loss=0.4214 ema=0.7938 lr=4.00e-05 mem=69.0/80.9 GiB
saved checkpoint -> /home/lakesenberg/nemotron_lora_work/smoke/step-00020
DONE.  final adapter -> /home/lakesenberg/nemotron_lora_work/smoke/final

=== DONE ===
Final adapter: /home/lakesenberg/nemotron_lora_work/smoke/final

Next: upload that folder to Kaggle as a Model dataset,
then point CFG.tinker_adapter_path at it in the AA-SVD notebook.
=== [Fri May 22 17:09:11 UTC 2026] Phase 2: full training (2000 steps) ===
[stage 1] generating 4000 examples per task -> /home/lakesenberg/nemotron_lora_work/cot.jsonl
wrote 28000 examples to /home/lakesenberg/nemotron_lora_work/cot.jsonl

--- sample ---
### Task: numeral
### Problem:
Convert the Roman numeral LXXVI to an integer.
### Reasoning:
<|cot_start|>Reading symbols one by one:
  L = 50  ->  0 + 50 = 50
  X = 10  ->  50 + 10 = 60
  X = 10  ->  60 + 10 = 70
  V = 5  ->  70 + 5 = 75
  I = 1  ->  75 + 1 = 76
Final: \boxed{76}

[stage 2] training LoRA on /home/lakesenberg/models/nemotron-3-nano-30b-a3b-bf16
[transformers] `torch_dtype` is deprecated! Use `dtype` instead!
The fast path is not available because on of `(selective_state_update, causal_conv1d_fn, causal_conv1d_update)` is None. Falling back to the naive implementation. To install follow https://github.com/state-spaces/mamba/#installation and https://github.com/Dao-AILab/causal-conv1d
=== TrainConfig ===
  base_model: /home/lakesenberg/models/nemotron-3-nano-30b-a3b-bf16
  data_path: /home/lakesenberg/nemotron_lora_work/cot.jsonl
  out_dir: /home/lakesenberg/nemotron_lora_work/adapter
  lora_rank: 32
  lora_alpha: 32
  lora_dropout: 0.0
  target_modules: ('q_proj', 'k_proj', 'v_proj', 'o_proj', 'gate_proj', 'up_proj', 'down_proj', 'x_proj', 'in_proj', 'out_proj', 'dt_proj', 'experts.w1', 'experts.w2')
  lr: 0.0001
  weight_decay: 0.0
  betas: (0.9, 0.95)
  max_grad_norm: 1.0
  warmup_steps: 50
  total_steps: 2000
  grad_accum: 4
  batch_size: 1
  seq_len: 7680
  save_every: 500
  log_every: 10
  seed: 0
  bf16: True
  use_flash_attn: False
  gradient_checkpointing: True
  resume_adapter: None
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trainable params: 883,873,792 || all params: 32,461,811,136 || trainable%: 2.7228
[transformers] `use_return_dict` is deprecated! Use `return_dict` instead!

=== STOP 7680 / RESTART 4096 Fri May 22 19:00:50 UTC 2026 ===
=== [Fri May 22 19:00:51 UTC 2026] SEQ_LEN=4096 restart ===
=== [Fri May 22 19:00:51 UTC 2026] Phase 2: full training (2000 steps, seq=4096) ===
[stage 1] data file exists, skipping: /home/lakesenberg/nemotron_lora_work/cot.jsonl
[stage 2] training LoRA on /home/lakesenberg/models/nemotron-3-nano-30b-a3b-bf16
[transformers] `torch_dtype` is deprecated! Use `dtype` instead!
The fast path is not available because on of `(selective_state_update, causal_conv1d_fn, causal_conv1d_update)` is None. Falling back to the naive implementation. To install follow https://github.com/state-spaces/mamba/#installation and https://github.com/Dao-AILab/causal-conv1d
=== TrainConfig ===
  base_model: /home/lakesenberg/models/nemotron-3-nano-30b-a3b-bf16
  data_path: /home/lakesenberg/nemotron_lora_work/cot.jsonl
  out_dir: /home/lakesenberg/nemotron_lora_work/adapter
  lora_rank: 32
  lora_alpha: 32
  lora_dropout: 0.0
  target_modules: ('q_proj', 'k_proj', 'v_proj', 'o_proj', 'gate_proj', 'up_proj', 'down_proj', 'x_proj', 'in_proj', 'out_proj', 'dt_proj', 'experts.w1', 'experts.w2')
  lr: 0.0001
  weight_decay: 0.0
  betas: (0.9, 0.95)
  max_grad_norm: 1.0
  warmup_steps: 50
  total_steps: 2000
  grad_accum: 4
  batch_size: 1
  seq_len: 4096
  save_every: 500
  log_every: 10
  seed: 0
  bf16: True
  use_flash_attn: False
  gradient_checkpointing: True
  resume_adapter: None
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[transformers] `use_return_dict` is deprecated! Use `return_dict` instead!

=== STOP 4096 / RESTART 2048 Fri May 22 19:44:26 UTC 2026 ===
=== [Fri May 22 19:44:26 UTC 2026] SEQ_LEN=2048 restart ===
=== [Fri May 22 19:44:26 UTC 2026] Phase 2: full training (2000 steps, seq=2048) ===
[stage 1] data file exists, skipping: /home/lakesenberg/nemotron_lora_work/cot.jsonl
[stage 2] training LoRA on /home/lakesenberg/models/nemotron-3-nano-30b-a3b-bf16
[transformers] `torch_dtype` is deprecated! Use `dtype` instead!
The fast path is not available because on of `(selective_state_update, causal_conv1d_fn, causal_conv1d_update)` is None. Falling back to the naive implementation. To install follow https://github.com/state-spaces/mamba/#installation and https://github.com/Dao-AILab/causal-conv1d
=== TrainConfig ===
  base_model: /home/lakesenberg/models/nemotron-3-nano-30b-a3b-bf16
  data_path: /home/lakesenberg/nemotron_lora_work/cot.jsonl
  out_dir: /home/lakesenberg/nemotron_lora_work/adapter
  lora_rank: 32
  lora_alpha: 32
  lora_dropout: 0.0
  target_modules: ('q_proj', 'k_proj', 'v_proj', 'o_proj', 'gate_proj', 'up_proj', 'down_proj', 'x_proj', 'in_proj', 'out_proj', 'dt_proj', 'experts.w1', 'experts.w2')
  lr: 0.0001
  weight_decay: 0.0
  betas: (0.9, 0.95)
  max_grad_norm: 1.0
  warmup_steps: 50
  total_steps: 2000
  grad_accum: 4
  batch_size: 1
  seq_len: 2048
  save_every: 500
  log_every: 10
  seed: 0
  bf16: True
  use_flash_attn: False
  gradient_checkpointing: True
  resume_adapter: None
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trainable params: 883,873,792 || all params: 32,461,811,136 || trainable%: 2.7228
[transformers] `use_return_dict` is deprecated! Use `return_dict` instead!
step    10/2000 loss=0.4620 ema=0.5800 lr=2.00e-05 mem=69.0/86.8 GiB
step    20/2000 loss=0.6800 ema=0.6877 lr=4.00e-05 mem=68.9/86.8 GiB
step    30/2000 loss=0.6103 ema=0.5642 lr=6.00e-05 mem=68.8/86.8 GiB
step    40/2000 loss=0.1662 ema=0.4054 lr=8.00e-05 mem=68.9/86.8 GiB
step    50/2000 loss=0.0279 ema=0.2234 lr=1.00e-04 mem=68.8/86.8 GiB
step    60/2000 loss=0.0005 ema=0.1067 lr=1.00e-04 mem=68.9/86.8 GiB
step    70/2000 loss=0.0146 ema=0.0605 lr=1.00e-04 mem=69.0/86.8 GiB
step    80/2000 loss=0.0240 ema=0.0324 lr=9.99e-05 mem=68.9/86.8 GiB
step    90/2000 loss=0.0007 ema=0.0192 lr=9.99e-05 mem=68.8/86.8 GiB
step   100/2000 loss=0.0049 ema=0.0158 lr=9.98e-05 mem=68.9/86.8 GiB
step   110/2000 loss=0.0445 ema=0.0155 lr=9.98e-05 mem=69.1/86.8 GiB
step   120/2000 loss=0.0108 ema=0.0163 lr=9.97e-05 mem=69.1/86.8 GiB
step   130/2000 loss=0.0096 ema=0.0134 lr=9.96e-05 mem=68.8/86.8 GiB
step   140/2000 loss=0.0099 ema=0.0130 lr=9.95e-05 mem=69.0/86.8 GiB
step   150/2000 loss=0.0003 ema=0.0135 lr=9.94e-05 mem=68.9/86.8 GiB
step   160/2000 loss=0.0023 ema=0.0084 lr=9.92e-05 mem=68.9/86.8 GiB
step   170/2000 loss=0.0071 ema=0.0081 lr=9.91e-05 mem=68.8/86.8 GiB
step   180/2000 loss=0.0004 ema=0.0071 lr=9.89e-05 mem=68.8/86.8 GiB
step   190/2000 loss=0.0080 ema=0.0055 lr=9.87e-05 mem=69.1/86.8 GiB
step   200/2000 loss=0.0001 ema=0.0046 lr=9.85e-05 mem=68.8/86.8 GiB
step   210/2000 loss=0.0128 ema=0.0086 lr=9.83e-05 mem=69.1/86.8 GiB
step   220/2000 loss=0.0001 ema=0.0086 lr=9.81e-05 mem=68.8/86.8 GiB
step   230/2000 loss=0.0049 ema=0.0056 lr=9.79e-05 mem=68.8/86.8 GiB
step   240/2000 loss=0.0008 ema=0.0105 lr=9.77e-05 mem=68.9/86.8 GiB
step   250/2000 loss=0.0066 ema=0.0074 lr=9.74e-05 mem=69.0/86.8 GiB
step   260/2000 loss=0.0011 ema=0.0055 lr=9.72e-05 mem=68.8/86.8 GiB
step   270/2000 loss=0.0009 ema=0.0055 lr=9.69e-05 mem=69.0/86.8 GiB
step   280/2000 loss=0.0005 ema=0.0046 lr=9.66e-05 mem=69.0/86.8 GiB
step   290/2000 loss=0.0018 ema=0.0043 lr=9.63e-05 mem=69.1/86.8 GiB
step   300/2000 loss=0.0001 ema=0.0036 lr=9.60e-05 mem=68.8/86.8 GiB
step   310/2000 loss=0.0023 ema=0.0054 lr=9.57e-05 mem=69.5/86.8 GiB
step   320/2000 loss=0.0036 ema=0.0047 lr=9.53e-05 mem=68.8/86.8 GiB
step   330/2000 loss=0.0046 ema=0.0035 lr=9.50e-05 mem=69.0/86.8 GiB
step   340/2000 loss=0.0000 ema=0.0034 lr=9.46e-05 mem=68.8/86.8 GiB
step   350/2000 loss=0.0043 ema=0.0049 lr=9.43e-05 mem=69.0/86.8 GiB
step   360/2000 loss=0.0071 ema=0.0078 lr=9.39e-05 mem=68.8/86.8 GiB
step   370/2000 loss=0.0000 ema=0.0046 lr=9.35e-05 mem=68.9/86.8 GiB
step   380/2000 loss=0.0024 ema=0.0034 lr=9.31e-05 mem=68.8/86.8 GiB
step   390/2000 loss=0.0004 ema=0.0027 lr=9.27e-05 mem=69.1/86.8 GiB
step   400/2000 loss=0.0024 ema=0.0020 lr=9.23e-05 mem=69.0/86.8 GiB
step   410/2000 loss=0.0012 ema=0.0020 lr=9.18e-05 mem=69.6/86.8 GiB
step   420/2000 loss=0.0029 ema=0.0027 lr=9.14e-05 mem=68.8/86.8 GiB
step   430/2000 loss=0.0002 ema=0.0030 lr=9.09e-05 mem=69.0/86.8 GiB
step   440/2000 loss=0.0000 ema=0.0020 lr=9.05e-05 mem=68.9/86.8 GiB
step   450/2000 loss=0.0000 ema=0.0041 lr=9.00e-05 mem=68.9/86.8 GiB
step   460/2000 loss=0.0039 ema=0.0045 lr=8.95e-05 mem=69.0/86.8 GiB
step   470/2000 loss=0.0001 ema=0.0053 lr=8.90e-05 mem=68.9/86.8 GiB
step   480/2000 loss=0.0004 ema=0.0041 lr=8.85e-05 mem=68.9/86.8 GiB
step   490/2000 loss=0.0005 ema=0.0134 lr=8.80e-05 mem=68.8/86.8 GiB
step   500/2000 loss=0.0038 ema=0.0072 lr=8.74e-05 mem=69.1/86.8 GiB
saved checkpoint -> /home/lakesenberg/nemotron_lora_work/adapter/step-00500
step   510/2000 loss=0.0114 ema=0.0061 lr=8.69e-05 mem=69.0/86.8 GiB
step   520/2000 loss=0.0041 ema=0.0037 lr=8.63e-05 mem=69.1/86.8 GiB
step   530/2000 loss=0.0031 ema=0.0021 lr=8.58e-05 mem=69.5/86.8 GiB
step   540/2000 loss=0.0002 ema=0.0017 lr=8.52e-05 mem=69.1/86.8 GiB
step   550/2000 loss=0.0071 ema=0.0023 lr=8.46e-05 mem=69.5/86.8 GiB
step   560/2000 loss=0.0042 ema=0.0025 lr=8.41e-05 mem=69.1/86.8 GiB
step   570/2000 loss=0.0047 ema=0.0021 lr=8.35e-05 mem=69.1/86.8 GiB
step   580/2000 loss=0.0015 ema=0.0019 lr=8.29e-05 mem=69.5/86.8 GiB
step   590/2000 loss=0.0033 ema=0.0018 lr=8.22e-05 mem=69.1/86.8 GiB
step   600/2000 loss=0.0103 ema=0.0033 lr=8.16e-05 mem=69.1/86.8 GiB
step   610/2000 loss=0.0000 ema=0.0021 lr=8.10e-05 mem=68.9/86.8 GiB
step   620/2000 loss=0.0027 ema=0.0019 lr=8.04e-05 mem=69.0/86.8 GiB
step   630/2000 loss=0.0000 ema=0.0032 lr=7.97e-05 mem=68.8/86.8 GiB
step   640/2000 loss=0.0004 ema=0.0027 lr=7.91e-05 mem=68.8/86.8 GiB
step   650/2000 loss=0.0001 ema=0.0017 lr=7.84e-05 mem=69.1/86.8 GiB
step   660/2000 loss=0.0000 ema=0.0079 lr=7.77e-05 mem=68.8/86.8 GiB
step   670/2000 loss=0.0019 ema=0.0051 lr=7.71e-05 mem=68.8/86.8 GiB
step   680/2000 loss=0.0001 ema=0.0028 lr=7.64e-05 mem=68.8/86.8 GiB
step   690/2000 loss=0.0000 ema=0.0015 lr=7.57e-05 mem=69.0/86.8 GiB
step   700/2000 loss=0.0000 ema=0.0016 lr=7.50e-05 mem=69.1/86.8 GiB
step   710/2000 loss=0.0028 ema=0.0017 lr=7.43e-05 mem=68.8/86.8 GiB
step   720/2000 loss=0.0036 ema=0.0015 lr=7.36e-05 mem=68.8/86.8 GiB
step   730/2000 loss=0.0015 ema=0.0015 lr=7.29e-05 mem=68.9/86.8 GiB
step   740/2000 loss=0.0055 ema=0.0020 lr=7.22e-05 mem=68.9/86.8 GiB
step   750/2000 loss=0.0002 ema=0.0034 lr=7.14e-05 mem=68.8/86.8 GiB
step   760/2000 loss=0.0033 ema=0.0022 lr=7.07e-05 mem=68.9/86.8 GiB
step   770/2000 loss=0.0000 ema=0.0020 lr=7.00e-05 mem=68.9/86.8 GiB
step   780/2000 loss=0.0000 ema=0.0013 lr=6.92e-05 mem=68.8/86.8 GiB
step   790/2000 loss=0.0019 ema=0.0012 lr=6.85e-05 mem=69.5/86.8 GiB
step   800/2000 loss=0.0030 ema=0.0019 lr=6.77e-05 mem=68.9/86.8 GiB
step   810/2000 loss=0.0000 ema=0.0017 lr=6.70e-05 mem=68.8/86.8 GiB
step   820/2000 loss=0.0000 ema=0.0012 lr=6.62e-05 mem=68.8/86.8 GiB
step   830/2000 loss=0.0000 ema=0.0014 lr=6.55e-05 mem=69.1/86.8 GiB
step   840/2000 loss=0.0015 ema=0.0021 lr=6.47e-05 mem=68.9/86.8 GiB
step   850/2000 loss=0.0000 ema=0.0015 lr=6.39e-05 mem=68.8/86.8 GiB
step   860/2000 loss=0.0000 ema=0.0011 lr=6.31e-05 mem=68.9/86.8 GiB
step   870/2000 loss=0.0046 ema=0.0017 lr=6.24e-05 mem=69.0/86.8 GiB
step   880/2000 loss=0.0027 ema=0.0018 lr=6.16e-05 mem=68.8/86.8 GiB
step   890/2000 loss=0.0048 ema=0.0012 lr=6.08e-05 mem=69.1/86.8 GiB
step   900/2000 loss=0.0018 ema=0.0012 lr=6.00e-05 mem=68.8/86.8 GiB
step   910/2000 loss=0.0010 ema=0.0031 lr=5.92e-05 mem=69.6/86.8 GiB
step   920/2000 loss=0.0019 ema=0.0023 lr=5.84e-05 mem=69.5/86.8 GiB
step   930/2000 loss=0.0026 ema=0.0034 lr=5.76e-05 mem=69.1/86.8 GiB
step   940/2000 loss=0.0023 ema=0.0020 lr=5.68e-05 mem=69.0/86.8 GiB
step   950/2000 loss=0.0018 ema=0.0016 lr=5.60e-05 mem=69.1/86.8 GiB
step   960/2000 loss=0.0072 ema=0.0020 lr=5.52e-05 mem=69.5/86.8 GiB
step   970/2000 loss=0.0001 ema=0.0010 lr=5.44e-05 mem=68.9/86.8 GiB
step   980/2000 loss=0.0000 ema=0.0016 lr=5.36e-05 mem=68.8/86.8 GiB
step   990/2000 loss=0.0003 ema=0.0013 lr=5.28e-05 mem=69.0/86.8 GiB
step  1000/2000 loss=0.0000 ema=0.0007 lr=5.20e-05 mem=69.0/86.8 GiB
saved checkpoint -> /home/lakesenberg/nemotron_lora_work/adapter/step-01000
step  1010/2000 loss=0.0001 ema=0.0012 lr=5.12e-05 mem=68.9/86.8 GiB
step  1020/2000 loss=0.0098 ema=0.0023 lr=5.04e-05 mem=69.0/86.8 GiB
step  1030/2000 loss=0.0000 ema=0.0015 lr=4.96e-05 mem=68.9/86.8 GiB
step  1040/2000 loss=0.0000 ema=0.0011 lr=4.88e-05 mem=68.8/86.8 GiB
step  1050/2000 loss=0.0013 ema=0.0007 lr=4.80e-05 mem=69.5/86.8 GiB
step  1060/2000 loss=0.0000 ema=0.0005 lr=4.72e-05 mem=68.9/86.8 GiB
step  1070/2000 loss=0.0000 ema=0.0009 lr=4.64e-05 mem=68.8/86.8 GiB
step  1080/2000 loss=0.0000 ema=0.0012 lr=4.56e-05 mem=68.8/86.8 GiB
step  1090/2000 loss=0.0022 ema=0.0014 lr=4.48e-05 mem=68.8/86.8 GiB
step  1100/2000 loss=0.0022 ema=0.0008 lr=4.40e-05 mem=69.0/86.8 GiB
step  1110/2000 loss=0.0035 ema=0.0014 lr=4.32e-05 mem=69.5/86.8 GiB
step  1120/2000 loss=0.0000 ema=0.0009 lr=4.24e-05 mem=68.9/86.8 GiB
step  1130/2000 loss=0.0000 ema=0.0012 lr=4.16e-05 mem=69.1/86.8 GiB
step  1140/2000 loss=0.0026 ema=0.0010 lr=4.08e-05 mem=69.1/87.9 GiB
step  1150/2000 loss=0.0000 ema=0.0009 lr=4.00e-05 mem=68.9/87.9 GiB
step  1160/2000 loss=0.0019 ema=0.0010 lr=3.92e-05 mem=69.5/87.9 GiB
step  1170/2000 loss=0.0000 ema=0.0013 lr=3.84e-05 mem=68.8/87.9 GiB
step  1180/2000 loss=0.0000 ema=0.0014 lr=3.76e-05 mem=68.9/87.9 GiB
step  1190/2000 loss=0.0032 ema=0.0019 lr=3.69e-05 mem=69.1/87.9 GiB
step  1200/2000 loss=0.0000 ema=0.0013 lr=3.61e-05 mem=68.9/87.9 GiB
step  1210/2000 loss=0.0000 ema=0.0007 lr=3.53e-05 mem=68.8/87.9 GiB
step  1220/2000 loss=0.0000 ema=0.0006 lr=3.45e-05 mem=68.9/87.9 GiB
step  1230/2000 loss=0.0000 ema=0.0004 lr=3.38e-05 mem=68.9/87.9 GiB
step  1240/2000 loss=0.0000 ema=0.0006 lr=3.30e-05 mem=68.9/87.9 GiB
step  1250/2000 loss=0.0002 ema=0.0008 lr=3.23e-05 mem=69.1/87.9 GiB
step  1260/2000 loss=0.0000 ema=0.0005 lr=3.15e-05 mem=68.8/87.9 GiB
step  1270/2000 loss=0.0000 ema=0.0041 lr=3.08e-05 mem=68.8/87.9 GiB
step  1280/2000 loss=0.0000 ema=0.0017 lr=3.00e-05 mem=69.1/87.9 GiB
step  1290/2000 loss=0.0003 ema=0.0013 lr=2.93e-05 mem=69.0/87.9 GiB
step  1300/2000 loss=0.0045 ema=0.0015 lr=2.86e-05 mem=69.1/87.9 GiB
step  1310/2000 loss=0.0000 ema=0.0013 lr=2.78e-05 mem=68.8/87.9 GiB
step  1320/2000 loss=0.0000 ema=0.0012 lr=2.71e-05 mem=69.1/87.9 GiB
step  1330/2000 loss=0.0005 ema=0.0009 lr=2.64e-05 mem=69.1/87.9 GiB
step  1340/2000 loss=0.0000 ema=0.0005 lr=2.57e-05 mem=68.9/87.9 GiB
step  1350/2000 loss=0.0011 ema=0.0005 lr=2.50e-05 mem=68.8/87.9 GiB
step  1360/2000 loss=0.0008 ema=0.0008 lr=2.43e-05 mem=69.1/87.9 GiB
step  1370/2000 loss=0.0015 ema=0.0007 lr=2.36e-05 mem=69.5/87.9 GiB
step  1380/2000 loss=0.0018 ema=0.0010 lr=2.29e-05 mem=69.5/87.9 GiB
step  1390/2000 loss=0.0009 ema=0.0005 lr=2.23e-05 mem=68.9/87.9 GiB
step  1400/2000 loss=0.0000 ema=0.0007 lr=2.16e-05 mem=68.9/87.9 GiB
step  1410/2000 loss=0.0000 ema=0.0009 lr=2.09e-05 mem=68.9/87.9 GiB
step  1420/2000 loss=0.0016 ema=0.0011 lr=2.03e-05 mem=69.5/87.9 GiB
step  1430/2000 loss=0.0000 ema=0.0007 lr=1.96e-05 mem=68.9/87.9 GiB
step  1440/2000 loss=0.0007 ema=0.0008 lr=1.90e-05 mem=69.5/87.9 GiB
step  1450/2000 loss=0.0026 ema=0.0009 lr=1.84e-05 mem=69.5/87.9 GiB
step  1460/2000 loss=0.0001 ema=0.0010 lr=1.78e-05 mem=68.9/87.9 GiB
step  1470/2000 loss=0.0025 ema=0.0012 lr=1.71e-05 mem=69.1/87.9 GiB
step  1480/2000 loss=0.0000 ema=0.0006 lr=1.65e-05 mem=68.9/87.9 GiB
step  1490/2000 loss=0.0034 ema=0.0011 lr=1.59e-05 mem=69.0/87.9 GiB
step  1500/2000 loss=0.0005 ema=0.0008 lr=1.54e-05 mem=69.0/87.9 GiB
saved checkpoint -> /home/lakesenberg/nemotron_lora_work/adapter/step-01500
step  1510/2000 loss=0.0000 ema=0.0006 lr=1.48e-05 mem=69.1/87.9 GiB
step  1520/2000 loss=0.0010 ema=0.0004 lr=1.42e-05 mem=69.5/87.9 GiB
step  1530/2000 loss=0.0000 ema=0.0004 lr=1.37e-05 mem=68.8/87.9 GiB
step  1540/2000 loss=0.0001 ema=0.0009 lr=1.31e-05 mem=69.1/87.9 GiB
step  1550/2000 loss=0.0012 ema=0.0008 lr=1.26e-05 mem=68.9/87.9 GiB
step  1560/2000 loss=0.0000 ema=0.0005 lr=1.20e-05 mem=68.8/87.9 GiB
step  1570/2000 loss=0.0014 ema=0.0004 lr=1.15e-05 mem=69.1/87.9 GiB
step  1580/2000 loss=0.0000 ema=0.0008 lr=1.10e-05 mem=68.8/87.9 GiB
step  1590/2000 loss=0.0000 ema=0.0007 lr=1.05e-05 mem=69.0/87.9 GiB
step  1600/2000 loss=0.0043 ema=0.0012 lr=1.00e-05 mem=69.5/87.9 GiB
step  1610/2000 loss=0.0000 ema=0.0007 lr=9.55e-06 mem=68.8/87.9 GiB
step  1620/2000 loss=0.0002 ema=0.0007 lr=9.08e-06 mem=69.0/87.9 GiB
step  1630/2000 loss=0.0000 ema=0.0006 lr=8.62e-06 mem=69.1/87.9 GiB
step  1640/2000 loss=0.0004 ema=0.0008 lr=8.18e-06 mem=69.0/87.9 GiB
step  1650/2000 loss=0.0013 ema=0.0006 lr=7.74e-06 mem=69.5/87.9 GiB
step  1660/2000 loss=0.0171 ema=0.0031 lr=7.32e-06 mem=69.1/87.9 GiB
step  1670/2000 loss=0.0015 ema=0.0014 lr=6.90e-06 mem=69.5/87.9 GiB
step  1680/2000 loss=0.0015 ema=0.0008 lr=6.50e-06 mem=68.8/87.9 GiB
step  1690/2000 loss=0.0001 ema=0.0009 lr=6.11e-06 mem=68.8/87.9 GiB
step  1700/2000 loss=0.0000 ema=0.0005 lr=5.73e-06 mem=69.0/87.9 GiB
step  1710/2000 loss=0.0000 ema=0.0005 lr=5.36e-06 mem=68.8/87.9 GiB
step  1720/2000 loss=0.0001 ema=0.0005 lr=5.00e-06 mem=69.1/87.9 GiB
step  1730/2000 loss=0.0000 ema=0.0005 lr=4.66e-06 mem=69.1/87.9 GiB
step  1740/2000 loss=0.0000 ema=0.0008 lr=4.32e-06 mem=68.8/87.9 GiB
step  1750/2000 loss=0.0000 ema=0.0005 lr=4.00e-06 mem=68.9/87.9 GiB
step  1760/2000 loss=0.0009 ema=0.0006 lr=3.69e-06 mem=69.6/87.9 GiB
step  1770/2000 loss=0.0021 ema=0.0007 lr=3.39e-06 mem=69.5/87.9 GiB
step  1780/2000 loss=0.0000 ema=0.0003 lr=3.11e-06 mem=68.8/87.9 GiB
step  1790/2000 loss=0.0000 ema=0.0006 lr=2.83e-06 mem=68.8/87.9 GiB
step  1800/2000 loss=0.0013 ema=0.0004 lr=2.57e-06 mem=68.9/87.9 GiB
step  1810/2000 loss=0.0000 ema=0.0005 lr=2.32e-06 mem=68.9/87.9 GiB
step  1820/2000 loss=0.0030 ema=0.0006 lr=2.09e-06 mem=69.1/87.9 GiB
step  1830/2000 loss=0.0016 ema=0.0008 lr=1.86e-06 mem=69.5/87.9 GiB
step  1840/2000 loss=0.0000 ema=0.0005 lr=1.65e-06 mem=68.8/87.9 GiB
step  1850/2000 loss=0.0020 ema=0.0006 lr=1.45e-06 mem=69.0/87.9 GiB
step  1860/2000 loss=0.0000 ema=0.0005 lr=1.27e-06 mem=68.9/87.9 GiB
step  1870/2000 loss=0.0000 ema=0.0005 lr=1.09e-06 mem=68.9/87.9 GiB
step  1880/2000 loss=0.0000 ema=0.0004 lr=9.31e-07 mem=68.9/87.9 GiB
step  1890/2000 loss=0.0000 ema=0.0003 lr=7.83e-07 mem=68.9/87.9 GiB
step  1900/2000 loss=0.0001 ema=0.0008 lr=6.47e-07 mem=69.1/87.9 GiB
step  1910/2000 loss=0.0000 ema=0.0008 lr=5.25e-07 mem=68.9/87.9 GiB
step  1920/2000 loss=0.0000 ema=0.0006 lr=4.15e-07 mem=68.9/87.9 GiB
step  1930/2000 loss=0.0008 ema=0.0004 lr=3.18e-07 mem=69.5/87.9 GiB
step  1940/2000 loss=0.0000 ema=0.0006 lr=2.33e-07 mem=68.8/87.9 GiB
step  1950/2000 loss=0.0000 ema=0.0008 lr=1.62e-07 mem=69.0/87.9 GiB
step  1960/2000 loss=0.0015 ema=0.0006 lr=1.04e-07 mem=69.5/87.9 GiB
step  1970/2000 loss=0.0015 ema=0.0011 lr=5.84e-08 mem=69.0/87.9 GiB
step  1980/2000 loss=0.0002 ema=0.0008 lr=2.60e-08 mem=69.0/87.9 GiB
step  1990/2000 loss=0.0000 ema=0.0006 lr=6.49e-09 mem=69.1/87.9 GiB
step  2000/2000 loss=0.0000 ema=0.0003 lr=0.00e+00 mem=68.8/87.9 GiB
saved checkpoint -> /home/lakesenberg/nemotron_lora_work/adapter/step-02000
DONE.  final adapter -> /home/lakesenberg/nemotron_lora_work/adapter/final

=== DONE ===
Final adapter: /home/lakesenberg/nemotron_lora_work/adapter/final

Next: upload that folder to Kaggle as a Model dataset,
then point CFG.tinker_adapter_path at it in the AA-SVD notebook.
=== [Sat May 23 09:28:53 UTC 2026] Phase 3: smoke eval ===
[transformers] `torch_dtype` is deprecated! Use `dtype` instead!
The fast path is not available because on of `(selective_state_update, causal_conv1d_fn, causal_conv1d_update)` is None. Falling back to the naive implementation. To install follow https://github.com/state-spaces/mamba/#installation and https://github.com/Dao-AILab/causal-conv1d
=== EvalConfig ===
  base_model: /home/lakesenberg/models/nemotron-3-nano-30b-a3b-bf16
  adapter: /home/lakesenberg/nemotron_lora_work/adapter/final
  out_dir: /home/lakesenberg/nemotron_lora_work/eval_smoke
  n_per_task: 3
  max_new_tokens: 256
  batch_size: 2
  seed: 0
  tasks: ('numeral', 'unit', 'gravity', 'cipher', 'bit', 'equation', 'cryptarithm')
  use_flash_attn: False
  bf16: True
  repetition_threshold: 0.3
[load] base = /home/lakesenberg/models/nemotron-3-nano-30b-a3b-bf16

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[load] adapter = /home/lakesenberg/nemotron_lora_work/adapter/final
Traceback (most recent call last):
  File "/home/lakesenberg/code/nemotron_lora_train/evaluate_adapter.py", line 365, in <module>
    summary = evaluate(cfg)
              ^^^^^^^^^^^^^
  File "/home/lakesenberg/code/nemotron_lora_train/evaluate_adapter.py", line 230, in evaluate
    model, tokenizer = load_model(cfg)
                       ^^^^^^^^^^^^^^^
  File "/home/lakesenberg/code/nemotron_lora_train/evaluate_adapter.py", line 183, in load_model
    model = PeftModel.from_pretrained(model, cfg.adapter)
            ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/home/lakesenberg/miniforge3/envs/nemo/lib/python3.11/site-packages/peft/peft_model.py", line 582, in from_pretrained
    load_result = model.load_adapter(
                  ^^^^^^^^^^^^^^^^^^^
  File "/home/lakesenberg/miniforge3/envs/nemo/lib/python3.11/site-packages/peft/peft_model.py", line 1408, in load_adapter
    load_result = set_peft_model_state_dict(
                  ^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/home/lakesenberg/miniforge3/envs/nemo/lib/python3.11/site-packages/peft/utils/save_and_load.py", line 644, in set_peft_model_state_dict
    state_dict = convert_peft_adapter_state_dict_for_transformers(
                 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/home/lakesenberg/miniforge3/envs/nemo/lib/python3.11/site-packages/peft/utils/transformers_weight_conversion.py", line 509, in convert_peft_adapter_state_dict_for_transformers
    peft_weight_conversions = build_peft_weight_mapping(
                              ^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/home/lakesenberg/miniforge3/envs/nemo/lib/python3.11/site-packages/peft/utils/transformers_weight_conversion.py", line 310, in build_peft_weight_mapping
    new_conversion = orig_conversion.__class__(
                     ^^^^^^^^^^^^^^^^^^^^^^^^^^
TypeError: WeightConverter.__init__() got an unexpected keyword argument 'distributed_operation'
EXIT_CODE=1