trl/trainer/callbacks.py imports is_wandb_available from
accelerate.utils, not from transformers. The original fix in #4147
only patched the transformers version, so `from trl import GRPOTrainer`
still crashed via the callbacks.py -> accelerate -> wandb path.
Must patch both the source module (accelerate.utils.imports) AND the
re-export namespace (accelerate.utils) since Python's
`from accelerate.utils import X` reads from the latter, which holds
its own cached reference.
* Fix broken wandb import crashing unsloth startup
When wandb is installed but broken (e.g., wandb < 0.19.11 with
protobuf >= 6.0), the import chain unsloth -> trl -> transformers ->
is_wandb_available() -> import wandb crashes with:
ImportError: cannot import name 'Imports' from
'wandb.proto.wandb_telemetry_pb2'
This happens because transformers' is_wandb_available() has no
try/except around `import wandb`. The error propagates up and kills
`from unsloth import FastLanguageModel` even though wandb is optional.
Add disable_broken_wandb() following the same pattern as
disable_torchcodec_if_broken(). It proactively tries importing wandb
during early init, and if the import fails, patches
is_wandb_available() to return False and sets WANDB_DISABLED=true.
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* Fixup mapper issues and resolve properly
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* fix: update GGUF save paths to use ~/.unsloth/llama.cpp with Windows support
* fix: quote LLAMA_CPP_DEFAULT_DIR in fallback shell commands to handle paths with spaces
* refactor: deduplicate platform-specific build instructions in quantization error message
* chore: remove accidentally committed PR description file
* Fix import safety and f-string bugs in save.py
- H4: Add defensive try/except for LLAMA_CPP_DEFAULT_DIR and IS_WINDOWS imports
with fallback defaults, so save.py works even if zoo PR #526 is not merged yet
- H5: Fix Kaggle error path using plain "Error: {e}" instead of f"Error: {e}",
so the actual exception is shown to users
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* Fix lm_head lora save
* Fix _need_to_train_embeddings guard for lm_head LoRA targets
When lm_head is already in final_modules as a LoRA target, the
_need_to_train_embeddings block should not also add it to
modules_to_save. This prevents dual-wrapping (LoRA + modules_to_save
on the same module) which causes assertion failures downstream.
Check if embed_tokens/lm_head are already being trained as LoRA
targets before adding them to modules_to_save. Also prevents
duplicate entries with elif guards.
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Current arch.startswith("gfx1") incorrectly matches:
- RDNA1 (gfx10xx) and RDNA2 (gfx103x): not ROCm supported
- gfx1102 (RX 7600), gfx1103 (Phoenix APU): not in ROCm support matrix
- gfx1150/1151/1152 (RDNA3.5 APUs): not in ROCm support matrix
Replace with explicit whitelist aligned to the ROCm Linux support matrix:
https://rocm.docs.amd.com/projects/install-on-linux/en/latest/reference/system-requirements.html
gfx1100 - RDNA3 discrete (RX 7900 series, PRO W7900/W7800)
gfx1101 - RDNA3 discrete (RX 7800/7700 series, PRO W7700)
gfx1200 - RDNA4 discrete (RX 9060 series)
gfx1201 - RDNA4 discrete (RX 9070 series, AI PRO R9700)
Mirrors the existing is_cdna() pattern. Avoids silently applying
unverified Triton kernel tuning to unsupported hardware.
