* fix for qwen3-guard tokenizer
* Better qwen3guard check
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backend restructuring and housekeeping
Changes made:
- Moved all files from backend/backend/ → backend/core/ with nested subdirectories
- Created init.py for each submodule with proper exports
- Updated all imports in routes (routes/training.py, routes/models.py)
- Updated internal relative imports to use .. for parent references
- Deleted old backend/backend/ directory
- Moved shared modules (path_utils.py , model_config.py) to utils/ subfolder
* [transformers] [v5] remove unused hybridcache (#3910)
* remote unused hybridcache
* cleanup
* Fix top_k on trl GRPO
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* Add torch compile options for GRPOTrainer
* Update CUDA settings based on device capability
* Add triton persistent TMA matmul condition
* Fix syntax for triton.enable_persistent_tma_matmul
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* Update rl.py
* Update rl.py
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* Guard torch.compile on ROCm when triton_key missing
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* Update unsloth/import_fixes.py
Co-authored-by: gemini-code-assist[bot] <176961590+gemini-code-assist[bot]@users.noreply.github.com>
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* Tighten ROCm Triton import handling
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* add FastSentenceTransformer
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* Gemini code review suggestions
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* unsloth-zoo patch only fixed usage for XLMRobertaForMaskedLM, this is a fix for XLMRobertaModel
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* refactor do_lower_case
* add some comments
* force disable FP8 loading
* refactor pooling detection, add missing pooling types
* add save_pretrained_merged method which gets modules and config
* fix _save_pretrained_merged
* rename read_pooling_mode, load modules instead of hard-coding em
* comment
* revert save_pretrained_merged change
* propagate trust_remote_code properly
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* add super hacky mpnet patch from hell
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* refactor _load_modules, add for_inference to from_pretrained, add transformers 5 code for mpnet, add distilbert patches
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* add ModernBert
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* deberta-v2 support (provisional), fix remote_code
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* add generic add_pooling_layer logic
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* fix for missing config
* add push_to_hub_merged
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* edit messages, throw exception if no HF token
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* fix device_map mismatch
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* add comments, move import, other suggestions by Datta0
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* re-add adapter removal to save_pretrained_merged, but if saving to folder which had adapters before, leave them
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* add unsloth branding to save_pretrained_merged
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* propagate dtype to internal module when loading for inference
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* fix mpnet gradient checkpointing for torch >= 2.9
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* same thing for transformers 5, oops =)
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* Fix FastSentenceTransformer performance: 6x speedup via torch.compile + SDPA
The original implementation was 31% slower than naive SentenceTransformer due to
conflicting decorators from Unsloth's auto-compiler (@torch.compile on attention
modules but @torch.compiler.disable on sub-modules).
Changes:
- Add fast encoder path that bypasses Unsloth patching for encoder models
- Use native torch.compile with mode="reduce-overhead" for 6x speedup
- Auto-detect and enable SDPA for models that support it (BERT, RoBERTa, etc.)
- Change defaults: load_in_16bit=True, load_in_4bit=False (16-bit is optimal)
- Change default: use_gradient_checkpointing=False (conflicts with torch.compile)
- Add UNSLOTH_COMPILE_DISABLE=1 env var to fall back to old path if needed
Supported encoder types: mpnet, bert, distilbert, roberta, xlm-roberta, albert, electra
Benchmark results (BS=32, seq_len=128):
- Naive 16-bit LoRA: 13-50ms per iter
- Unsloth 16-bit LoRA: 2-9ms per iter (5.4x-6.7x faster)
- Memory usage: 61MB-1.3GB (even largest model fits easily)
Note: 4-bit + torch.compile has a PyTorch bug (pytorch/pytorch#90665).
4-bit is also 1.7-1.9x slower than 16-bit due to dequantization overhead,
so 16-bit is recommended for these small encoder models anyway.
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* Use Unsloth's prepare_model_for_kbit_training for consistency
Changed from peft.prepare_model_for_kbit_training to
unsloth.models._utils.prepare_model_for_kbit_training.
Unsloth's version provides:
- Float32 mixed precision upcasting for LoRA layers
- Better numerical stability
- Consistency with rest of Unsloth codebase
* Use relative imports and add float16 machine support
- Changed absolute import to relative: from ._utils import prepare_model_for_kbit_training
- Added SUPPORTS_BFLOAT16 import for proper dtype detection
- Handle devices that don't support bfloat16 by falling back to float16
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* add save_pretrained_torchao
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* Add auto-compile for torch.compile based on training step breakeven analysis
Changes:
- Change default compile_mode from "reduce-overhead" to "default" since CUDA
Graphs (used by reduce-overhead) is incompatible with PEFT/LoRA
- Add _estimate_compile_threshold() to calculate minimum steps needed for
torch.compile to be beneficial based on model parameter count
- Add _apply_torch_compile() helper with accelerate unwrap_model bug workaround
- Defer torch.compile application to trainer initialization time so we can
check max_steps against the breakeven threshold
- Patch SentenceTransformerTrainer to auto-apply compile when max_steps
exceeds the calculated threshold
Breakeven thresholds (with 1.2x safety margin):
- 22M params (MiniLM): ~1388 steps
- 110M params (mpnet): ~242 steps
- 335M params (snowflake): ~203 steps
This ensures torch.compile warmup cost is only paid when training is long
enough to benefit from the speedup.
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* do QAT preparation for fast path
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* fix double loading model, thanks Etherl
* do mpnet gradient checkpoint patch if gc is enabled
* remove distilbert patches from mpnet fix
* sanity check on model params, thanks Etherl
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* add save_pretrained_gguf, thanks Etherl
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* Refine compile threshold estimation for sentence transformers
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Co-authored-by: Daniel Han <danielhanchen@gmail.com>
Co-authored-by: Daniel Han <danielhanchen@users.noreply.github.com>
