…3790)

* Fix is_contiguous() method call and remove duplicate imports

- Fix bug in rope_embedding.py where is_contiguous was used without
  parentheses, causing the method object (always truthy) to be evaluated
  instead of calling the method. This fixes issue unslothai#3781 where fast rope
  backpropagation was broken for zero strided/non-contiguous tensors.

- Remove duplicate `import torch` in rl.py (lines 20 and 25)
- Remove duplicate `import functools` and `import types` in vision.py

🤖 Generated with [Claude Code](https://claude.com/claude-code)

Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>

* Fix Boolean value of Tensor ambiguity error in mistral.py

Replace `or` operator with explicit `is None` check when getting
n_items from kwargs. The `or` operator fails when the value is a
Tensor because Python cannot determine the boolean value of a
multi-element tensor.

Fixes unslothai#3766

🤖 Generated with [Claude Code](https://claude.com/claude-code)

Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>

* Update rope_embedding.py

---------

Co-authored-by: yurekami <yurekami@users.noreply.github.com>
Co-authored-by: Claude Opus 4.5 <noreply@anthropic.com>
Co-authored-by: Daniel Han <danielhanchen@gmail.com>

…lothai#3794)

Add "corda" as an allowed value for the init_lora_weights parameter
in FastLanguageModel.get_peft_model() and FastBaseModel.get_peft_model().

This enables users to use CorDA (Correlation-aware Decomposed Adaptation)
initialization from PEFT, which provides an alternative LoRA initialization
strategy for improved finetuning performance.

Fixes unslothai#3693

Signed-off-by: majiayu000 <1835304752@qq.com>

for more information, see https://pre-commit.ci

…lothai#3811)

* Fix correctness bugs in rl.py, rl_replacements.py, and vision.py

1. rl_replacements.py (lines 864, 870): Fixed undefined `nanmin`/`nanmax`
   functions by using `.nan_to_num(nan=inf/-inf).min()/.max()` pattern.
   PyTorch doesn't have torch.nanmin/nanmax, so we replace NaN values
   before computing min/max.

2. vision.py (line 150): Fixed bug where code checked for "input" key
   but then accessed kwargs["input_ids"] instead of kwargs["input"].

3. vision.py (line 159): Fixed bug where literal string "key" was used
   instead of the variable `key` when accessing kwargs.

4. rl.py (lines 903, 905): Fixed non-existent `MathError` exception
   by replacing with `ValueError`.

* [pre-commit.ci] auto fixes from pre-commit.com hooks

for more information, see https://pre-commit.ci

---------

Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>

1. cohere.py:347-348 - Fixed wrong variable names in QK normalization.
   Used `Q`/`K` but variables were named `Qn`/`Kn`. This caused NameError
   when `use_qk_norm=True` (e.g., c4ai-command-r-plus models).

2. cohere.py:482 - Fixed wrong object reference in inference loop.
   Used `self.mlp` but should be `decoder_layer.mlp` since we're
   iterating through decoder layers. Caused AttributeError during inference.

3. falcon_h1.py:459,461 - Fixed wrong attribute names in inference path.
   Used `post_attention_layernorm` and `mlp` but Falcon H1 uses
   `pre_ff_layernorm` and `feed_forward`. Caused AttributeError during generation.

4. qwen3_moe.py:210 - Fixed wrong module path with incorrect capitalization.
   Used `transformers.models.Qwen3Moe` but should be `transformers.models.qwen3_moe`.
   Caused AttributeError when patching rotary embeddings.

5. qwen3_moe.py:239 - Fixed wrong model_patcher class.
   Used `FastQwen3Model` but should be `FastQwen3MoeModel` for MoE models.
   Caused incorrect patching for Qwen3 MoE models.

6. hf_hub.py:21-22 - Fixed floor division and missing return for billion values.
   Used `//` instead of `/` for millions, and had no return for values >= 1B.
   Caused incorrect formatting and None return for large numbers.

7. save.py:550 - Fixed self-assignment that did nothing.
   `sharded_ram_usage = sharded_ram_usage` should be `= max_shard_size`.
   Caused integer shard sizes to be ignored.

8. rl.py:562-567 - Fixed orphan string not included in length_check.
   The elif branch for max_seq_length validation was a standalone string
   expression, not concatenated to length_check. Caused silent skip of
   the max_seq_length > model_max_seq_length warning.

9. granite.py:49-52 - Fixed wrong model name and version in error message.
   Said "Gemma2" and "4.42.3" but should be "Granite" and "4.45.0".

…tmul

Fix 3D tensor support for bitsandbytes 8-bit matmul in forward pass

🤖 Generated with [Claude Code](https://claude.com/claude-code)

Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>

FIX: weight tying for LoRA embeddings and lm_head

Gemma3 models have a large vocabulary (262144 tokens) which causes
training loss to explode when using int8 embedding quantization.

This fix auto-detects Gemma3 models and switches from int8-int4
(phone-deployment) to int4 weight-only QAT for stable training.

…lity

Fix Gemma3 QAT training instability with int8-int4 scheme

When users load a model with fast_inference=False but then try to use
vLLM-style arguments with fast_generate, they previously got confusing
errors. This adds a wrapper that detects common mistakes and provides
helpful guidance:

- Using sampling_params: explains to use HF generate args instead
- Using lora_request: explains LoRA weights are already merged
- Passing text strings: shows how to tokenize input first

Changes:
- Add make_fast_generate_wrapper to _utils.py
- Apply wrapper in llama.py when fast_inference=False
- Apply wrapper in vision.py when fast_inference=False

for more information, see https://pre-commit.ci

…apper-helpful-errors

Add helpful error messages for fast_generate when fast_inference=False

Bug fixes

for more information, see https://pre-commit.ci

…curl

Make llama.cpp CURL dependency optional when building from source

…ai#4143)

* Fixup mapper issues and resolve properly

* [pre-commit.ci] auto fixes from pre-commit.com hooks

for more information, see https://pre-commit.ci

---------

Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>

* 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.

* [pre-commit.ci] auto fixes from pre-commit.com hooks

for more information, see https://pre-commit.ci

---------

Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>

…slothai#4148)

trl/trainer/callbacks.py imports is_wandb_available from
accelerate.utils, not from transformers. The original fix in unslothai#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.

* Refactor loss computation to include completion_mask

* [pre-commit.ci] auto fixes from pre-commit.com hooks

for more information, see https://pre-commit.ci

---------

Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>

)

* Fix gpt temporary patch for grpo to happen after compile

* [pre-commit.ci] auto fixes from pre-commit.com hooks

for more information, see https://pre-commit.ci

---------

Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>

* Refactor loss computation to include completion_mask

* [pre-commit.ci] auto fixes from pre-commit.com hooks

for more information, see https://pre-commit.ci

* Fixes for trl 0.28 and above

Remove sync/reload weights calls , remove vllm.LLM instantiation

* [pre-commit.ci] auto fixes from pre-commit.com hooks

for more information, see https://pre-commit.ci

* Refactor loss computation to include completion_mask

* [pre-commit.ci] auto fixes from pre-commit.com hooks

for more information, see https://pre-commit.ci

* Fixes for trl 0.28 and above

Remove sync/reload weights calls , remove vllm.LLM instantiation

* patch rpc in openenv for newer trl

* [pre-commit.ci] auto fixes from pre-commit.com hooks

for more information, see https://pre-commit.ci

---------

Co-authored-by: pluesclues <136766175+pluesclues@users.noreply.github.com>
Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>

updates:
- [github.com/astral-sh/ruff-pre-commit: v0.15.4 → v0.15.5](astral-sh/ruff-pre-commit@v0.15.4...v0.15.5)

Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>

for more information, see https://pre-commit.ci