ModulesToSaveWrapper was removed from peft.tuners.tuners_utils in PEFT
0.16.0. The class has been available in peft.utils.other since at least
PEFT 0.7.1, which is the minimum version Unsloth requires.
This fixes the ImportError when using PEFT >= 0.16.0.
- Fix Kaggle misclassification by prioritizing filesystem markers over env vars
- Preserve telemetry pings when statistics is explicitly provided
- Replace bare except with except Exception
- Minor cleanup based on automated review feedback
Fixed Codex regression: keep snapshot_download pings for explicit statistics values; detection only runs when statistics is None. Also replaced bare except.
Problem: Kaggle notebook environments can expose both KAGGLE_* and COLAB_* environment keys. _get_statistics currently checks COLAB_ before KAGGLE_, causing Kaggle sessions to be labeled colab/colabpro.
Prefer filesystem markers (e.g. /kaggle/working, /content + /opt/colab) before env-key heuristics, then fall back to the existing env-key checks. This avoids misclassification when providers leak overlapping env vars.
Kaggle test notebook: https://www.kaggle.com/code/hnxnq07/kaggle-stats-gathering-test
- Fix test file: use return_tokenized instead of return_tensors
- Fix test file: use text_dataset instead of undefined dataset variable
- Move parameter validation to constructor (fail fast on invalid params)
- Add labels field in tokenized output for causal LM training
- Add empty file handling with clear error message
- Add tests for constructor validation and labels field
This PR fixes the "Arch conditional MMA instruction used without targeting
appropriate compute capability. Aborting." errors that occur when using
FBGEMM on Blackwell GPUs (B200/B100, SM100).
Changes:
- Add stderr filters in import_fixes.py for CUTLASS/FBGEMM MMA errors
- Add warning filters for various deprecation messages
- Update check_fbgemm_gpu_version() to disable FBGEMM instead of raising
an error when old versions are detected
- Update test_has_fbgemm() in fp8.py to catch broader CUTLASS/CUDA errors
and gracefully fall back to Triton kernels
- Update loader_utils.py to disable FBGEMM instead of raising ValueError
for old fbgemm_gpu versions
The key behavior change is that FBGEMM errors no longer crash the script.
Instead, FBGEMM is disabled and Triton kernels are used automatically.
This allows Unsloth to work on SM100 GPUs where CUTLASS SM90 kernels fail,
and also gracefully handles old FBGEMM versions.
The GitHub issue check had issues:
1. Network latency on import
2. Issue being closed does not mean the fix is in the installed vLLM version
Now skip the PDL workaround if vLLM version > 0.13.2, which is when
the upstream fix is expected to be included.
