From a86363eca972118e2c6c4bb91c42810851fc72d6 Mon Sep 17 00:00:00 2001 From: oKatanaaa Date: Thu, 11 Dec 2025 03:21:02 +0000 Subject: [PATCH 01/24] fix: weights tying --- unsloth/models/llama.py | 48 ++++++++++++++++++++++++++++++++++++++++ unsloth/models/vision.py | 1 + 2 files changed, 49 insertions(+) diff --git a/unsloth/models/llama.py b/unsloth/models/llama.py index 4c9337ccf9..d38018ee1b 100644 --- a/unsloth/models/llama.py +++ b/unsloth/models/llama.py @@ -2601,6 +2601,7 @@ class FastLlamaModel: loftq_config = {}, temporary_location = "_unsloth_temporary_saved_buffers", qat_scheme = None, + ensure_weight_tying = False, **kwargs, ): if os.environ.get("UNSLOTH_USE_NEW_MODEL", "0") == "1": @@ -2630,6 +2631,7 @@ class FastLlamaModel: init_lora_weights = init_lora_weights, loftq_config = loftq_config, temporary_location = temporary_location, + ensure_weight_tying = ensure_weight_tying, **kwargs, ) if os.environ.get("UNSLOTH_ENABLE_FULL_FINETUNING", "0") == "1": @@ -2953,6 +2955,7 @@ class FastLlamaModel: loftq_config = loftq_config, use_rslora = use_rslora, modules_to_save = modules_to_save, + ensure_weight_tying = ensure_weight_tying, **kwargs, ) if not SUPPORTS_LOFTQ: @@ -3002,6 +3005,51 @@ class FastLlamaModel: model = FastLlamaModel.patch_peft_model(model, use_gradient_checkpointing) + if ensure_weight_tying: + try: + input_embeddings = model.get_input_embeddings() + output_embeddings = model.get_output_embeddings() + + if input_embeddings is not None and output_embeddings is not None: + def _retie_parameter(target_module, source_module): + if not hasattr(source_module, "weight"): + return + weight = source_module.weight + # Remove existing registration to avoid "attribute already exists" + if "weight" in getattr(target_module, "_parameters", {}): + target_module._parameters.pop("weight") + if hasattr(target_module, "weight"): + try: + delattr(target_module, "weight") + except Exception: + pass + target_module.register_parameter("weight", weight) + + # Tie trainable copies created by ModulesToSaveWrapper first (these are used in forward) + if hasattr(input_embeddings, "modules_to_save") and hasattr( + output_embeddings, "modules_to_save" + ): + if hasattr(input_embeddings.modules_to_save, "default") and hasattr( + output_embeddings.modules_to_save, "default" + ): + _retie_parameter( + output_embeddings.modules_to_save.default, + input_embeddings.modules_to_save.default, + ) + + # Tie original_module references as well if present + if hasattr(input_embeddings, "original_module") and hasattr( + output_embeddings, "original_module" + ): + _retie_parameter( + output_embeddings.original_module, + input_embeddings.original_module, + ) + except Exception as e: + logger.warning_once( + f"Unsloth: Failed to ensure weight tying between embeddings and lm_head: {e}" + ) + if train_embed_tokens: print("Unsloth: Training embed_tokens in mixed precision to save VRAM") assert hasattr(model.get_input_embeddings(), "modules_to_save") diff --git a/unsloth/models/vision.py b/unsloth/models/vision.py index ed19f587cf..9f847f2837 100644 --- a/unsloth/models/vision.py +++ b/unsloth/models/vision.py @@ -930,6 +930,7 @@ class FastBaseModel: task_type = TaskType.CAUSAL_LM, temporary_location = "_unsloth_temporary_saved_buffers", qat_scheme = None, + ensure_weight_tying = False, **kwargs, ): if os.environ.get("UNSLOTH_ENABLE_FULL_FINETUNING", "0") == "1": From 1837de275165b5307b057036c420f2778c6d1343 Mon Sep 17 00:00:00 2001 From: "pre-commit-ci[bot]" <66853113+pre-commit-ci[bot]@users.noreply.github.com> Date: Thu, 11 Dec 2025 03:31:41 +0000 Subject: [PATCH 02/24] [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci --- unsloth/models/llama.py | 7 ++++--- 1 file changed, 4 insertions(+), 3 deletions(-) diff --git a/unsloth/models/llama.py b/unsloth/models/llama.py index d38018ee1b..e0d8cbcf25 100644 --- a/unsloth/models/llama.py +++ b/unsloth/models/llama.py @@ -3011,6 +3011,7 @@ class FastLlamaModel: output_embeddings = model.get_output_embeddings() if input_embeddings is not None and output_embeddings is not None: + def _retie_parameter(target_module, source_module): if not hasattr(source_module, "weight"): return @@ -3029,9 +3030,9 @@ class FastLlamaModel: if hasattr(input_embeddings, "modules_to_save") and hasattr( output_embeddings, "modules_to_save" ): - if hasattr(input_embeddings.modules_to_save, "default") and hasattr( - output_embeddings.modules_to_save, "default" - ): + if hasattr( + input_embeddings.modules_to_save, "default" + ) and hasattr(output_embeddings.modules_to_save, "default"): _retie_parameter( output_embeddings.modules_to_save.default, input_embeddings.modules_to_save.default, From 7403104b0c05c0794bd8f74342624a22c930a535 Mon Sep 17 00:00:00 2001 From: oKatanaaa Date: Sat, 13 Dec 2025 00:02:48 +0000 Subject: [PATCH 03/24] fix: add a log instead of silent exception --- unsloth/models/llama.py | 7 +++++-- 1 file changed, 5 insertions(+), 2 deletions(-) diff --git a/unsloth/models/llama.py b/unsloth/models/llama.py index e0d8cbcf25..6e47907166 100644 --- a/unsloth/models/llama.py +++ b/unsloth/models/llama.py @@ -3022,8 +3022,11 @@ class FastLlamaModel: if hasattr(target_module, "weight"): try: delattr(target_module, "weight") - except Exception: - pass + except Exception as exc: + logger.warning_once( + f"Unsloth: Could not delete existing weight attr during retie on " + f"{type(target_module).__name__}: {exc}" + ) target_module.register_parameter("weight", weight) # Tie trainable copies created by ModulesToSaveWrapper first (these are used in forward) From 08f1716a70ae932f8299421d020fa44fa2de6f2e Mon Sep 17 00:00:00 2001 From: Strahinja Stamenkovic Date: Fri, 26 Dec 2025 03:43:59 +0100 Subject: [PATCH 04/24] Add missing import of inspect (#3778) * Add missing import of inspect * Update device_type.py --- unsloth/device_type.py | 1 + 1 file changed, 1 insertion(+) diff --git a/unsloth/device_type.py b/unsloth/device_type.py index 68038de679..0f924bfdfd 100644 --- a/unsloth/device_type.py +++ b/unsloth/device_type.py @@ -24,6 +24,7 @@ __all__ = [ import torch import functools +import inspect from unsloth_zoo.utils import Version From 181b76420efde3a0a0e0a4e5f6dd598523027a2e Mon Sep 17 00:00:00 2001 From: Fizza Mukhtar Date: Thu, 25 Dec 2025 18:46:13 -0800 Subject: [PATCH 05/24] Clarify NotImplementedError for fast_inference with full_finetuning (#3768) * Improve error message for fast_inference and full_finetuning * Refine error message string formatting * Update unsloth/models/vision.py --------- Co-authored-by: Daniel Han --- unsloth/models/vision.py | 8 ++++++-- 1 file changed, 6 insertions(+), 2 deletions(-) diff --git a/unsloth/models/vision.py b/unsloth/models/vision.py index b78b190bcb..e1cf8f6f82 100644 --- a/unsloth/models/vision.py +++ b/unsloth/models/vision.py @@ -718,9 +718,13 @@ class FastBaseModel: if full_finetuning: max_lora_rank = max(get_lora_supported_ranks()) raise NotImplementedError( - f"Unsloth: `fast_inference = True` does not yet support `full_finetuning = True`.\n" - f"Use LoRA rank `r = {max_lora_rank}` as the closest replacement for full finetuning with Unsloth for RL." + "Unsloth: `fast_inference=True` cannot be used together with `full_finetuning=True`.\n" + "Reason: fast_inference is optimized for inference-only workflows and " + "does not currently support full fine-tuning.\n" + "Workaround: disable fast_inference, or use parameter-efficient fine-tuning " + f"(e.g. LoRA with rank r={max_lora_rank})." ) + model_config.model_name = model_name if fast_inference: From b314dca22dc5bedc80235d2c712d2cbfed2add89 Mon Sep 17 00:00:00 2001 From: Michael Han <107991372+shimmyshimmer@users.noreply.github.com> Date: Sat, 27 Dec 2025 00:49:19 -0800 Subject: [PATCH 06/24] Update README for new unsloth.ai/docs.md --- README.md | 17 +++++++++-------- 1 file changed, 9 insertions(+), 8 deletions(-) diff --git a/README.md b/README.md index 43c09381fc..7cd9d0bba4 100644 --- a/README.md +++ b/README.md @@ -8,7 +8,7 @@ - + ### Train gpt-oss, DeepSeek, Gemma, Qwen & Llama 2x faster with 70% less VRAM! @@ -18,7 +18,7 @@ ## ✨ Train for Free -Notebooks are beginner friendly. Read our [guide](https://docs.unsloth.ai/get-started/fine-tuning-guide). Add dataset, run, then export your trained model to GGUF, llama.cpp, Ollama, vLLM, SGLang or Hugging Face. +Notebooks are beginner friendly. Read our [guide](https://docs.unsloth.ai/get-started/fine-tuning-guide). Add dataset, run, then deploy your trained model. | Model | Free Notebooks | Performance | Memory use | |-----------|---------|--------|----------| @@ -34,9 +34,9 @@ Notebooks are beginner friendly. Read our [guide](https://docs.unsloth.ai/get-st | **Llama 3.2 Conversational** | [▶️ Start for free](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Llama3.2_(1B_and_3B)-Conversational.ipynb) | 2x faster | 70% less | | **Orpheus-TTS (3B)** | [▶️ Start for free](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Orpheus_(3B)-TTS.ipynb) | 1.5x faster | 50% less | -- See all our notebooks for: [Kaggle](https://github.com/unslothai/notebooks?tab=readme-ov-file#-kaggle-notebooks), [GRPO](https://docs.unsloth.ai/get-started/unsloth-notebooks#grpo-reasoning-rl-notebooks), **[TTS](https://docs.unsloth.ai/get-started/unsloth-notebooks#text-to-speech-tts-notebooks)** & [Vision](https://docs.unsloth.ai/get-started/unsloth-notebooks#vision-multimodal-notebooks) -- See [all our models](https://docs.unsloth.ai/get-started/all-our-models) and [all our notebooks](https://docs.unsloth.ai/get-started/unsloth-notebooks) -- See detailed documentation for Unsloth [here](https://docs.unsloth.ai/) +- See all our notebooks for: [Kaggle](https://github.com/unslothai/notebooks?tab=readme-ov-file#-kaggle-notebooks), [GRPO](https://unsloth.ai/docs/get-started/unsloth-notebooks#grpo-reasoning-rl-notebooks), [TTS](https://unsloth.ai/docs/get-started/unsloth-notebooks#text-to-speech-tts-notebooks) & [Vision](https://unsloth.ai/docs/get-started/unsloth-notebooks#vision-multimodal-notebooks) +- See [all our models](https://unsloth.ai/docs/get-started/unsloth-model-catalog) and [all our notebooks](https://unsloth.ai/docs/get-started/unsloth-notebooks) +- See detailed documentation for Unsloth [here](https://unsloth.ai/docs) ## ⚡ Quickstart ### Linux or WSL @@ -46,9 +46,9 @@ pip install unsloth ### Windows For Windows, `pip install unsloth` works only if you have Pytorch installed. Read our [Windows Guide](https://docs.unsloth.ai/get-started/installing-+-updating/windows-installation). ### Docker -Use our official [Unsloth Docker image](https://hub.docker.com/r/unsloth/unsloth) ```unsloth/unsloth``` container. Read our [Docker Guide](https://docs.unsloth.ai/get-started/install-and-update/docker). +Use our official [Unsloth Docker image](https://hub.docker.com/r/unsloth/unsloth) ```unsloth/unsloth``` container. Read our [Docker Guide](https://unsloth.ai/docs/get-started/install-and-update/docker). ### Blackwell & DGX Spark -For RTX 50x, B200, 6000 GPUs: `pip install unsloth`. Read our [Blackwell Guide](https://docs.unsloth.ai/basics/training-llms-with-blackwell-rtx-50-series-and-unsloth) and [DGX Spark Guide](https://docs.unsloth.ai/new/fine-tuning-llms-with-nvidia-dgx-spark-and-unsloth) for more details. +For RTX 50x, B200, 6000 GPUs: `pip install unsloth`. Read our [Blackwell Guide](https://unsloth.ai/docs/basics/fine-tuning-llms-with-blackwell-rtx-50-series-and-unsloth) and [DGX Spark Guide](https://unsloth.ai/docs/basics/fine-tuning-llms-with-nvidia-dgx-spark-and-unsloth) for more details. ## 🦥 Unsloth News - New RoPE & MLP **Triton Kernels** & **Padding Free + Packing**: 3x faster training & 30% less VRAM. [Blog](https://docs.unsloth.ai/new/3x-faster-training-packing) @@ -98,6 +98,7 @@ For RTX 50x, B200, 6000 GPUs: `pip install unsloth`. Read our [Blackwell Guide]( - Supports **all models** including [TTS](https://docs.unsloth.ai/basics/text-to-speech-tts-fine-tuning), multimodal, [BERT](https://docs.unsloth.ai/get-started/unsloth-notebooks#other-important-notebooks) and more! Any model that works in transformers, works in Unsloth. - The most efficient library for [Reinforcement Learning (RL)](https://docs.unsloth.ai/get-started/reinforcement-learning-rl-guide), using 80% less VRAM. Supports GRPO, GSPO, DrGRPO, DAPO etc. - **0% loss in accuracy** - no approximation methods - all exact. +- Export and [deploy your model](https://unsloth.ai/docs/basics/inference-and-deployment) to GGUF, llama.cpp, vLLM, SGLang and Hugging Face. - Supports NVIDIA (since 2018), [AMD](https://docs.unsloth.ai/get-started/install-and-update/amd) and Intel GPUs. Minimum CUDA Capability 7.0 (V100, T4, Titan V, RTX 20, 30, 40x, A100, H100, L40 etc) - Works on **Linux**, WSL and **Windows** - All kernels written in [OpenAI's Triton](https://openai.com/index/triton/) language. Manual backprop engine. @@ -283,7 +284,7 @@ docker run -d -e JUPYTER_PASSWORD="mypassword" \ Access Jupyter Lab at `http://localhost:8888` and start fine-tuning! ## 📜 Documentation -- Go to our official [Documentation](https://docs.unsloth.ai) for [running models](https://docs.unsloth.ai/basics/running-and-saving-models), [saving to GGUF](https://docs.unsloth.ai/basics/running-and-saving-models/saving-to-gguf), [checkpointing](https://docs.unsloth.ai/basics/finetuning-from-last-checkpoint), [evaluation](https://docs.unsloth.ai/get-started/fine-tuning-llms-guide#evaluation) and more! +- Go to our official [Documentation](https://unsloth.ai/docs) for [running models](https://docs.unsloth.ai/basics/running-and-saving-models), [saving to GGUF](https://docs.unsloth.ai/basics/running-and-saving-models/saving-to-gguf), [checkpointing](https://docs.unsloth.ai/basics/finetuning-from-last-checkpoint), [evaluation](https://docs.unsloth.ai/get-started/fine-tuning-llms-guide#evaluation) and more! - Read our Guides for: [Fine-tuning](https://docs.unsloth.ai/get-started/fine-tuning-llms-guide), [Reinforcement Learning](https://docs.unsloth.ai/get-started/reinforcement-learning-rl-guide), [Text-to-Speech (TTS)](https://docs.unsloth.ai/basics/text-to-speech-tts-fine-tuning), [Vision](https://docs.unsloth.ai/basics/vision-fine-tuning) and [any model](https://docs.unsloth.ai/models/tutorials-how-to-fine-tune-and-run-llms). - We support Huggingface's transformers, TRL, Trainer, Seq2SeqTrainer and Pytorch code. From 58235ee1927d2dd448762ff9fd1010e991435370 Mon Sep 17 00:00:00 2001 From: Daniel Han Date: Sun, 28 Dec 2025 19:57:43 -0800 Subject: [PATCH 07/24] Update FUNDING.yml (#3792) --- .github/FUNDING.yml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.github/FUNDING.yml b/.github/FUNDING.yml index 4ebb6df3d0..ae5dade42d 100644 --- a/.github/FUNDING.yml +++ b/.github/FUNDING.yml @@ -3,7 +3,7 @@ github: unslothai patreon: # Replace with a single Patreon username open_collective: # Replace with a single Open Collective username -ko_fi: unsloth +ko_fi: # unsloth tidelift: # Replace with a single Tidelift platform-name/package-name e.g., npm/babel community_bridge: # Replace with a single Community Bridge project-name e.g., cloud-foundry liberapay: # Replace with a single Liberapay username From c0c21a1e227823dac76e1cfc08856ac3def1015b Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Alk=C4=B1n=20=C3=9Cnl=C3=BC?= Date: Mon, 29 Dec 2025 08:18:02 +0300 Subject: [PATCH 08/24] fix(trainer): import psutil to prevent NameError in _prepare_dataset (#3780) * fix(trainer): import psutil to prevent NameError in _prepare_dataset Fixes #3777 * Update rl.py --------- Co-authored-by: Daniel Han --- unsloth/models/rl.py | 1 + unsloth/tokenizer_utils.py | 1 + unsloth/trainer.py | 1 + 3 files changed, 3 insertions(+) diff --git a/unsloth/models/rl.py b/unsloth/models/rl.py index 4ea36519d9..003a0e7f1b 100644 --- a/unsloth/models/rl.py +++ b/unsloth/models/rl.py @@ -227,6 +227,7 @@ import numpy as np from contextlib import nullcontext from torch.nn import functional as F import inspect +import psutil from transformers import DataCollatorForSeq2Seq, DataCollatorForLanguageModeling as TransformersDataCollatorForLanguageModeling from transformers.training_args import ParallelMode diff --git a/unsloth/tokenizer_utils.py b/unsloth/tokenizer_utils.py index 99651643a8..0136e3498e 100644 --- a/unsloth/tokenizer_utils.py +++ b/unsloth/tokenizer_utils.py @@ -25,6 +25,7 @@ import collections import numpy as np import gc import subprocess +import psutil from unsloth_zoo.tokenizer_utils import ( mean_of_trained_tokens, diff --git a/unsloth/trainer.py b/unsloth/trainer.py index c0b2dd03b6..0d98cff305 100644 --- a/unsloth/trainer.py +++ b/unsloth/trainer.py @@ -14,6 +14,7 @@ import logging import os +import psutil import warnings from dataclasses import dataclass, field from typing import Optional From ab815692a9c80d9737e3b0d67927363e6da3b527 Mon Sep 17 00:00:00 2001 From: Francesco Bertolotti Date: Mon, 29 Dec 2025 06:21:48 +0100 Subject: [PATCH 09/24] fastrope fix for zero strided tensors (#3782) Co-authored-by: Francesco Bertolotti --- unsloth/kernels/rope_embedding.py | 24 ++++++++++++------------ 1 file changed, 12 insertions(+), 12 deletions(-) diff --git a/unsloth/kernels/rope_embedding.py b/unsloth/kernels/rope_embedding.py index a032e0f7fc..fcc9cb923b 100644 --- a/unsloth/kernels/rope_embedding.py +++ b/unsloth/kernels/rope_embedding.py @@ -312,8 +312,8 @@ class Fast_RoPE_Embedding_QK(torch.autograd.Function): _, n_heads_K, _, _ = K.shape # Inplace rotary embedding is generally fine - Q_out = Q.clone() if not Q.is_contiguous else Q - K_out = K.clone() if not K.is_contiguous else K + Q_out = Q.clone() if not Q.is_contiguous() else Q + K_out = K.clone() if not K.is_contiguous() else K if has_indices: # TRL's rotary indices are always in int32, so casting is just for safety @@ -383,21 +383,21 @@ class Fast_RoPE_Embedding_QK(torch.autograd.Function): else ctx.cos.new_empty(1, dtype = torch.int32) ) + # Inplace rotary embedding is generally fine + dQ_out = dQ.clone() if not dQ.is_contiguous() else dQ + dK_out = dK.clone() if not dK.is_contiguous() else dK + Q_batch_stride, Q_head_stride, Q_seq_stride = ( - dQ.stride(0), - dQ.stride(1), - dQ.stride(2), + dQ_out.stride(0), + dQ_out.stride(1), + dQ_out.stride(2), ) K_batch_stride, K_head_stride, K_seq_stride = ( - dK.stride(0), - dK.stride(1), - dK.stride(2), + dK_out.stride(0), + dK_out.stride(1), + dK_out.stride(2), ) - # Inplace rotary embedding is generally fine - dQ_out = dQ.clone() if not dQ.is_contiguous else dQ - dK_out = dK.clone() if not dK.is_contiguous else dK - with torch_gpu_device(dQ.device): _rope_embedding_QK[(batch * ctx.seq_len, ctx.n_heads_Q)]( dQ_out, From c8b0bada94f55ab93848dbff28dcdec22b7cec31 Mon Sep 17 00:00:00 2001 From: Fizza Mukhtar Date: Sun, 28 Dec 2025 21:23:51 -0800 Subject: [PATCH 10/24] Fix crash when trl.experimental.openenv is unavailable (#3787) * Guard optional trl.experimental.openenv usage in RL patches * Simplify optional trl.openenv import handling * [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> --- unsloth/models/rl_replacements.py | 4 ++++ 1 file changed, 4 insertions(+) diff --git a/unsloth/models/rl_replacements.py b/unsloth/models/rl_replacements.py index 7d4d520c1f..3dfeea6871 100644 --- a/unsloth/models/rl_replacements.py +++ b/unsloth/models/rl_replacements.py @@ -949,11 +949,15 @@ def openenv_vllm_reload_weights(): return if Version(importlib_version("trl")) < Version("0.26.0"): return + try: import trl.experimental.openenv.utils as openenv_utils import trl.experimental.openenv as openenv except ImportError as e: logger.info(f"Unsloth: Failed to import trl openenv: {e}") + logger.info( + "Unsloth: trl.experimental.openenv not available — skipping RL openenv patches." + ) return src = inspect.getsource(openenv_utils.generate_rollout_completions) From fe82f5f3663eb75e106fa17f6bc65141265fd5cf Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?=E3=82=86=E3=82=8A?= Date: Mon, 29 Dec 2025 13:30:55 +0800 Subject: [PATCH 11/24] Fix Boolean value of Tensor ambiguity error in mistral.py (#3790) MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit * 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 #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 * 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 #3766 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude Opus 4.5 * Update rope_embedding.py --------- Co-authored-by: yurekami Co-authored-by: Claude Opus 4.5 Co-authored-by: Daniel Han --- unsloth/models/mistral.py | 12 +++++++----- unsloth/models/rl.py | 1 - unsloth/models/vision.py | 2 -- 3 files changed, 7 insertions(+), 8 deletions(-) diff --git a/unsloth/models/mistral.py b/unsloth/models/mistral.py index 0eed45c5cd..5e893d2b6f 100644 --- a/unsloth/models/mistral.py +++ b/unsloth/models/mistral.py @@ -307,9 +307,9 @@ def MistralForCausalLM_fast_forward( RETURN_LOGITS = False if not RETURN_LOGITS and labels is not None: - n_items = kwargs.get("num_items_in_batch", None) or kwargs.get( - "n_items", None - ) + n_items = kwargs.get("num_items_in_batch", None) + if n_items is None: + n_items = kwargs.get("n_items", None) logit_softcapping = getattr(self.config, "final_logit_softcapping", 0) # loss = fused_linear_cross_entropy( @@ -363,11 +363,13 @@ def MistralForCausalLM_fast_forward( shift_labels, kwargs.get("packed_seq_lengths"), ) + n_items = kwargs.get("num_items_in_batch", None) + if n_items is None: + n_items = kwargs.get("n_items", None) loss = fast_cross_entropy_loss( logits = shift_logits, labels = shift_labels, - n_items = kwargs.get("num_items_in_batch", None) - or kwargs.get("n_items", None), + n_items = n_items, ) if not return_dict: diff --git a/unsloth/models/rl.py b/unsloth/models/rl.py index 003a0e7f1b..03f2c44701 100644 --- a/unsloth/models/rl.py +++ b/unsloth/models/rl.py @@ -22,7 +22,6 @@ from typing import Any, Callable, Dict, List, Literal, Optional, Tuple, Union import inspect import os import re -import torch from unsloth_zoo.compiler import create_new_function from unsloth_zoo.log import logger from unsloth_zoo.logging_utils import PatchRLStatistics diff --git a/unsloth/models/vision.py b/unsloth/models/vision.py index e1cf8f6f82..36cfbf0b17 100644 --- a/unsloth/models/vision.py +++ b/unsloth/models/vision.py @@ -68,11 +68,9 @@ import functools import os import gc import math -import functools from typing import Optional, Tuple, List, Union import re, inspect, sys import contextlib -import types try: from huggingface_hub.utils import get_token From 9fedb1c11df2c4b7d1096962d2cf16d74372c80a Mon Sep 17 00:00:00 2001 From: lif <1835304752@qq.com> Date: Mon, 29 Dec 2025 15:17:58 +0800 Subject: [PATCH 12/24] fix: add support for init_lora_weights="corda" in get_peft_model (#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 #3693 Signed-off-by: majiayu000 <1835304752@qq.com> --- unsloth/models/_utils.py | 3 ++- unsloth/models/llama.py | 3 ++- 2 files changed, 4 insertions(+), 2 deletions(-) diff --git a/unsloth/models/_utils.py b/unsloth/models/_utils.py index abc8380562..ccb547f58e 100644 --- a/unsloth/models/_utils.py +++ b/unsloth/models/_utils.py @@ -1981,9 +1981,10 @@ def validate_loftq_config(loftq_config, lora_dropout, bias, init_lora_weights, m type(init_lora_weights) is bool or init_lora_weights == "gaussian" or init_lora_weights == "loftq" + or init_lora_weights == "corda" ): raise ValueError( - 'Unsloth: `init_lora_weights` must be either [True, False, "gaussian", "loftq"].' + 'Unsloth: `init_lora_weights` must be either [True, False, "gaussian", "loftq", "corda"].' ) if init_lora_weights == "loftq": diff --git a/unsloth/models/llama.py b/unsloth/models/llama.py index 1d7695b9aa..762445b5e8 100644 --- a/unsloth/models/llama.py +++ b/unsloth/models/llama.py @@ -2779,9 +2779,10 @@ class FastLlamaModel: type(init_lora_weights) is bool or init_lora_weights == "gaussian" or init_lora_weights == "loftq" + or init_lora_weights == "corda" ): raise ValueError( - 'Unsloth: `init_lora_weights` must be either [True, False, "gaussian", "loftq"].' + 'Unsloth: `init_lora_weights` must be either [True, False, "gaussian", "loftq", "corda"].' ) if init_lora_weights == "loftq": From c452eb13f54dc572bb0b12c63ae47746543c4654 Mon Sep 17 00:00:00 2001 From: Fizza-Mukhtar Date: Tue, 30 Dec 2025 07:08:10 -0800 Subject: [PATCH 13/24] Fix 3D tensor support for bitsandbytes 8-bit matmul in forward pass --- unsloth/kernels/fast_lora.py | 13 +++++++++++++ 1 file changed, 13 insertions(+) diff --git a/unsloth/kernels/fast_lora.py b/unsloth/kernels/fast_lora.py index 60d0c318c3..16b679d005 100644 --- a/unsloth/kernels/fast_lora.py +++ b/unsloth/kernels/fast_lora.py @@ -379,9 +379,22 @@ class LoRA_QKV(torch.autograd.Function): ): dtype = X.dtype + # bitsandbytes 8-bit matmul expects 2D inputs. + # TorchInductor/AOTAutograd fails on 3D tensors during backward, + # so we explicitly flatten the sequence dimension. + orig_shape = X.shape + if X.dim() == 3: + X = X.view(-1, X.shape[-1]) + Q = matmul_lora(X, QW, QW_quant, QA, QB, QS) K = matmul_lora(X, KW, KW_quant, KA, KB, KS) V = matmul_lora(X, VW, VW_quant, VA, VB, VS) + + # Restore original shape after matmul + if len(orig_shape) == 3: + Q = Q.view(orig_shape[0], orig_shape[1], -1) + K = K.view(orig_shape[0], orig_shape[1], -1) + V = V.view(orig_shape[0], orig_shape[1], -1) ctx.custom_saved_tensors = ( QW, From f2e87251c721482d05bf8dd21452e4eb5c20ba02 Mon Sep 17 00:00:00 2001 From: Fizza-Mukhtar Date: Tue, 30 Dec 2025 07:56:01 -0800 Subject: [PATCH 14/24] Fix 3D tensor support for bitsandbytes 8-bit matmul in forward pass --- unsloth/kernels/fast_lora.py | 10 +++++----- 1 file changed, 5 insertions(+), 5 deletions(-) diff --git a/unsloth/kernels/fast_lora.py b/unsloth/kernels/fast_lora.py index 16b679d005..fbb18c3a15 100644 --- a/unsloth/kernels/fast_lora.py +++ b/unsloth/kernels/fast_lora.py @@ -383,12 +383,12 @@ class LoRA_QKV(torch.autograd.Function): # TorchInductor/AOTAutograd fails on 3D tensors during backward, # so we explicitly flatten the sequence dimension. orig_shape = X.shape + X_for_matmul = X if X.dim() == 3: - X = X.view(-1, X.shape[-1]) - - Q = matmul_lora(X, QW, QW_quant, QA, QB, QS) - K = matmul_lora(X, KW, KW_quant, KA, KB, KS) - V = matmul_lora(X, VW, VW_quant, VA, VB, VS) + X_for_matmul = X.view(-1, X.shape[-1]) + Q = matmul_lora(X_for_matmul, QW, QW_quant, QA, QB, QS) + K = matmul_lora(X_for_matmul, KW, KW_quant, KA, KB, KS) + V = matmul_lora(X_for_matmul, VW, VW_quant, VA, VB, VS) # Restore original shape after matmul if len(orig_shape) == 3: From e43e67cb18e3f9842ca34416db8f42b21f7154f2 Mon Sep 17 00:00:00 2001 From: "pre-commit-ci[bot]" <66853113+pre-commit-ci[bot]@users.noreply.github.com> Date: Tue, 30 Dec 2025 15:58:40 +0000 Subject: [PATCH 15/24] [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci --- unsloth/kernels/fast_lora.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/unsloth/kernels/fast_lora.py b/unsloth/kernels/fast_lora.py index fbb18c3a15..f1c0e298d9 100644 --- a/unsloth/kernels/fast_lora.py +++ b/unsloth/kernels/fast_lora.py @@ -389,7 +389,7 @@ class LoRA_QKV(torch.autograd.Function): Q = matmul_lora(X_for_matmul, QW, QW_quant, QA, QB, QS) K = matmul_lora(X_for_matmul, KW, KW_quant, KA, KB, KS) V = matmul_lora(X_for_matmul, VW, VW_quant, VA, VB, VS) - + # Restore original shape after matmul if len(orig_shape) == 3: Q = Q.view(orig_shape[0], orig_shape[1], -1) From b21b4e6252a8ee2381952d26d21fe023ad14c0d9 Mon Sep 17 00:00:00 2001 From: Michael Han <107991372+shimmyshimmer@users.noreply.github.com> Date: Tue, 30 Dec 2025 15:14:27 -0800 Subject: [PATCH 16/24] Refresh of Unsloth README.md with https://unsloth.ai/docs --- README.md | 115 +++++++++++++++++++++++++----------------------------- 1 file changed, 53 insertions(+), 62 deletions(-) diff --git a/README.md b/README.md index 7cd9d0bba4..ae1fccfbba 100644 --- a/README.md +++ b/README.md @@ -1,6 +1,6 @@
- + unsloth logo @@ -18,7 +18,7 @@ ## ✨ Train for Free -Notebooks are beginner friendly. Read our [guide](https://docs.unsloth.ai/get-started/fine-tuning-guide). Add dataset, run, then deploy your trained model. +Notebooks are beginner friendly. Read our [guide](https://unsloth.ai/docs/get-started/fine-tuning-llms-guide). Add dataset, run, then deploy your trained model. | Model | Free Notebooks | Performance | Memory use | |-----------|---------|--------|----------| @@ -44,33 +44,35 @@ Notebooks are beginner friendly. Read our [guide](https://docs.unsloth.ai/get-st pip install unsloth ``` ### Windows -For Windows, `pip install unsloth` works only if you have Pytorch installed. Read our [Windows Guide](https://docs.unsloth.ai/get-started/installing-+-updating/windows-installation). +For Windows, `pip install unsloth` works only if you have Pytorch installed. Read our [Windows Guide](https://unsloth.ai/docs/get-started/install-and-update/windows-installation). + ### Docker Use our official [Unsloth Docker image](https://hub.docker.com/r/unsloth/unsloth) ```unsloth/unsloth``` container. Read our [Docker Guide](https://unsloth.ai/docs/get-started/install-and-update/docker). + ### Blackwell & DGX Spark For RTX 50x, B200, 6000 GPUs: `pip install unsloth`. Read our [Blackwell Guide](https://unsloth.ai/docs/basics/fine-tuning-llms-with-blackwell-rtx-50-series-and-unsloth) and [DGX Spark Guide](https://unsloth.ai/docs/basics/fine-tuning-llms-with-nvidia-dgx-spark-and-unsloth) for more details. ## 🦥 Unsloth News -- New RoPE & MLP **Triton Kernels** & **Padding Free + Packing**: 3x faster training & 30% less VRAM. [Blog](https://docs.unsloth.ai/new/3x-faster-training-packing) -- **Ministral 3** by Mistral: Run Ministral 3 or fine-tune with vision/RL sodoku notebooks. [Guide](https://docs.unsloth.ai/new/ministral-3) • [Notebooks](https://docs.unsloth.ai/new/ministral-3#fine-tuningb) -- **500K Context**: Training a 20B model with >500K context is now possible on an 80GB GPU. [Blog](https://docs.unsloth.ai/new/500k-context-length-fine-tuning) -- **FP8 Reinforcement Learning**: You can now do FP8 GRPO on consumer GPUs. [Blog](https://docs.unsloth.ai/new/fp8-reinforcement-learning) • [Notebook](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Qwen3_8B_FP8_GRPO.ipynb) -- **DeepSeek-OCR**: Fine-tune to improve language understanding by 89%. [Guide](https://docs.unsloth.ai/new/deepseek-ocr-run-and-fine-tune) • [Notebook](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Deepseek_OCR_(3B).ipynb) -- **Docker**: Use Unsloth with no setup & environment issues with our new image. [Guide](https://docs.unsloth.ai/new/how-to-train-llms-with-unsloth-and-docker) • [Docker image](https://hub.docker.com/r/unsloth/unsloth) -- **gpt-oss RL**: Introducing the fastest possible inference for gpt-oss RL! [Read blog](https://docs.unsloth.ai/new/gpt-oss-reinforcement-learning) -- **Vision RL**: You can now train VLMs with GRPO or GSPO in Unsloth! [Read guide](https://docs.unsloth.ai/new/vision-reinforcement-learning-vlm-rl) -- **gpt-oss** by OpenAI: Read our [Unsloth Flex Attention](https://docs.unsloth.ai/new/long-context-gpt-oss-training) blog and [gpt-oss Guide](https://docs.unsloth.ai/basics/gpt-oss). 20B works on 14GB VRAM. 120B on 65GB. +- New RoPE & MLP **Triton Kernels** & **Padding Free + Packing**: 3x faster training & 30% less VRAM. [Blog](https://unsloth.ai/docs/new/3x-faster-training-packing) +- **New Mistral**: Run Ministral 3 or Devstral 2 and fine-tune with vision/RL sodoku notebooks. [Guide](https://unsloth.ai/docs/models/ministral-3) • [Notebooks](https://unsloth.ai/docs/models/ministral-3#fine-tuning-ministral-3) +- **500K Context**: Training a 20B model with >500K context is now possible on an 80GB GPU. [Blog](https://unsloth.ai/docs/new/500k-context-length-fine-tuning) +- **FP8 Reinforcement Learning**: You can now do FP8 GRPO on consumer GPUs. [Blog](https://unsloth.ai/docs/new/fp8-reinforcement-learning) • [Notebook](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Qwen3_8B_FP8_GRPO.ipynb) +- **DeepSeek-OCR**: Fine-tune to improve language understanding by 89%. [Guide](https://unsloth.ai/docs/models/deepseek-ocr-how-to-run-and-fine-tune) • [Notebook](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Deepseek_OCR_(3B).ipynb) +- **Docker**: Use Unsloth with no setup & environment issues with our new image. [Guide](https://unsloth.ai/docs/new/how-to-fine-tune-llms-with-unsloth-and-docker) • [Docker image](https://hub.docker.com/r/unsloth/unsloth) +- **gpt-oss RL**: Introducing the fastest possible inference for gpt-oss RL! [Read blog](https://unsloth.ai/docs/models/gpt-oss-how-to-run-and-fine-tune/gpt-oss-reinforcement-learning) +- **Vision RL**: You can now train VLMs with GRPO or GSPO in Unsloth! [Read guide](https://unsloth.ai/docs/get-started/reinforcement-learning-rl-guide/vision-reinforcement-learning-vlm-rl) +- **gpt-oss** by OpenAI: Read our [Unsloth Flex Attention](https://unsloth.ai/docs/models/gpt-oss-how-to-run-and-fine-tune/long-context-gpt-oss-training) blog and [gpt-oss Guide](https://unsloth.ai/docs/models/gpt-oss-how-to-run-and-fine-tune). 20B works on 14GB VRAM. 120B on 65GB.
Click for more news -- **Quantization-Aware Training**: We collabed with Pytorch, recovering ~70% accuracy. [Read blog](https://docs.unsloth.ai/new/quantization-aware-training-qat) -- **Memory-efficient RL**: We're introducing even better RL. Our new kernels & algos allows faster RL with 50% less VRAM & 10× more context. [Read blog](https://docs.unsloth.ai/new/memory-efficient-rl) -- **Gemma 3n** by Google: [Read Blog](https://docs.unsloth.ai/basics/gemma-3n-how-to-run-and-fine-tune). We [uploaded GGUFs, 4-bit models](https://huggingface.co/collections/unsloth/gemma-3n-685d3874830e49e1c93f9339). -- **[Text-to-Speech (TTS)](https://docs.unsloth.ai/basics/text-to-speech-tts-fine-tuning)** is now supported, including `sesame/csm-1b` and STT `openai/whisper-large-v3`. -- **[Qwen3](https://docs.unsloth.ai/basics/qwen3-how-to-run-and-fine-tune)** is now supported. Qwen3-30B-A3B fits on 17.5GB VRAM. -- Introducing **[Dynamic 2.0](https://docs.unsloth.ai/basics/unsloth-dynamic-2.0-ggufs)** quants that set new benchmarks on 5-shot MMLU & Aider Polyglot. -- [**EVERYTHING** is now supported](https://unsloth.ai/blog/gemma3#everything) - all models (TTS, BERT, Mamba), FFT, etc. [MultiGPU](https://docs.unsloth.ai/basics/multi-gpu-training-with-unsloth) coming soon. Enable FFT with `full_finetuning = True`, 8-bit with `load_in_8bit = True`. +- **Quantization-Aware Training**: We collabed with Pytorch, recovering ~70% accuracy. [Read blog](https://unsloth.ai/docs/basics/quantization-aware-training-qat) +- **Memory-efficient RL**: We're introducing even better RL. Our new kernels & algos allows faster RL with 50% less VRAM & 10× more context. [Read blog](https://unsloth.ai/docs/get-started/reinforcement-learning-rl-guide/memory-efficient-rl) +- **Gemma 3n** by Google: [Read Blog](https://unsloth.ai/docs/models/gemma-3-how-to-run-and-fine-tune/gemma-3n-how-to-run-and-fine-tune). We [uploaded GGUFs, 4-bit models](https://huggingface.co/collections/unsloth/gemma-3n-685d3874830e49e1c93f9339). +- **[Text-to-Speech (TTS)](https://unsloth.ai/docs/basics/text-to-speech-tts-fine-tuning)** is now supported, including `sesame/csm-1b` and STT `openai/whisper-large-v3`. +- **[Qwen3](https://unsloth.ai/docs/models/qwen3-how-to-run-and-fine-tune)** is now supported. Qwen3-30B-A3B fits on 17.5GB VRAM. +- Introducing **[Dynamic 2.0](https://unsloth.ai/docs/basics/unsloth-dynamic-2.0-ggufs)** quants that set new benchmarks on 5-shot MMLU & Aider Polyglot. +- [**EVERYTHING** is now supported](https://unsloth.ai/blog/gemma3#everything) - all models (TTS, BERT, Mamba), FFT, etc. [MultiGPU](https://unsloth.ai/docs/basics/multi-gpu-training-with-unsloth) coming soon. Enable FFT with `full_finetuning = True`, 8-bit with `load_in_8bit = True`. - 📣 [DeepSeek-R1](https://unsloth.ai/blog/deepseek-r1) - run or fine-tune them [with our guide](https://unsloth.ai/blog/deepseek-r1). All model uploads: [here](https://huggingface.co/collections/unsloth/deepseek-r1-all-versions-678e1c48f5d2fce87892ace5). - 📣 Introducing Long-context [Reasoning (GRPO)](https://unsloth.ai/blog/grpo) in Unsloth. Train your own reasoning model with just 5GB VRAM. Transform Llama, Phi, Mistral etc. into reasoning LLMs! - 📣 Introducing Unsloth [Dynamic 4-bit Quantization](https://unsloth.ai/blog/dynamic-4bit)! We dynamically opt not to quantize certain parameters and this greatly increases accuracy while only using <10% more VRAM than BnB 4-bit. See our collection on [Hugging Face here.](https://huggingface.co/collections/unsloth/unsloth-4-bit-dynamic-quants-67503bb873f89e15276c44e7) @@ -84,28 +86,29 @@ For RTX 50x, B200, 6000 GPUs: `pip install unsloth`. Read our [Blackwell Guide](
## 🔗 Links and Resources -| Type | Links | -| ------------------------------- | --------------------------------------- | -|   **r/unsloth Reddit** | [Join Reddit community](https://reddit.com/r/unsloth)| -| 📚 **Documentation & Wiki** | [Read Our Docs](https://docs.unsloth.ai) | -|   **Twitter (aka X)** | [Follow us on X](https://twitter.com/unslothai)| -| 💾 **Installation** | [Pip & Docker Install](https://docs.unsloth.ai/get-started/installing-+-updating)| -| 🔮 **Our Models** | [Unsloth Catalog](https://docs.unsloth.ai/get-started/all-our-models)| -| ✍️ **Blog** | [Read our Blogs](https://unsloth.ai/blog)| +| Type | Links | +| ----------------------------------------------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------ | +|   **r/unsloth Reddit** | [Join Reddit community](https://reddit.com/r/unsloth) | +| 📚 **Documentation & Wiki** | [Read Our Docs](https://unsloth.ai/docs) | +|   **Twitter (aka X)** | [Follow us on X](https://twitter.com/unslothai) | +| 💾 **Installation** | [Pip & Docker Install](https://unsloth.ai/docs/get-started/install-and-update) | +| 🔮 **Our Models** | [Unsloth Catalog](https://unsloth.ai/docs/get-started/unsloth-model-catalog) | +| ✍️ **Blog** | [Read our Blogs](https://unsloth.ai/blog) | ## ⭐ Key Features -- Supports **full-finetuning**, pretraining, 4b-bit, 16-bit and **FP8** training -- Supports **all models** including [TTS](https://docs.unsloth.ai/basics/text-to-speech-tts-fine-tuning), multimodal, [BERT](https://docs.unsloth.ai/get-started/unsloth-notebooks#other-important-notebooks) and more! Any model that works in transformers, works in Unsloth. -- The most efficient library for [Reinforcement Learning (RL)](https://docs.unsloth.ai/get-started/reinforcement-learning-rl-guide), using 80% less VRAM. Supports GRPO, GSPO, DrGRPO, DAPO etc. -- **0% loss in accuracy** - no approximation methods - all exact. -- Export and [deploy your model](https://unsloth.ai/docs/basics/inference-and-deployment) to GGUF, llama.cpp, vLLM, SGLang and Hugging Face. -- Supports NVIDIA (since 2018), [AMD](https://docs.unsloth.ai/get-started/install-and-update/amd) and Intel GPUs. Minimum CUDA Capability 7.0 (V100, T4, Titan V, RTX 20, 30, 40x, A100, H100, L40 etc) -- Works on **Linux**, WSL and **Windows** -- All kernels written in [OpenAI's Triton](https://openai.com/index/triton/) language. Manual backprop engine. -- If you trained a model with 🦥Unsloth, you can use this cool sticker!   + +* Supports **full-finetuning**, pretraining, 4b-bit, 16-bit and **FP8** training +* Supports **all models** including [TTS](https://unsloth.ai/docs/basics/text-to-speech-tts-fine-tuning), multimodal, [BERT](https://unsloth.ai/docs/get-started/unsloth-notebooks#other-important-notebooks) and more! Any model that works in transformers, works in Unsloth. +* The most efficient library for [Reinforcement Learning (RL)](https://unsloth.ai/docs/get-started/reinforcement-learning-rl-guide), using 80% less VRAM. Supports GRPO, GSPO, DrGRPO, DAPO etc. +* **0% loss in accuracy** - no approximation methods - all exact. +* Export and [deploy your model](https://unsloth.ai/docs/basics/inference-and-deployment) to GGUF, llama.cpp, vLLM, SGLang and Hugging Face. +* Supports NVIDIA (since 2018), [AMD](https://unsloth.ai/docs/get-started/install-and-update/amd) and Intel GPUs. Minimum CUDA Capability 7.0 (V100, T4, Titan V, RTX 20, 30, 40x, A100, H100, L40 etc) +* Works on **Linux**, WSL and **Windows** +* All kernels written in OpenAI's Triton language. Manual backprop engine. +* If you trained a model with 🦥Unsloth, you can use this cool sticker!   ## 💾 Install Unsloth -You can also see our docs for more detailed installation and updating instructions [here](https://docs.unsloth.ai/get-started/installing-+-updating). +You can also see our docs for more detailed installation and updating instructions [here](https://unsloth.ai/docs/get-started/install-and-update). Unsloth supports Python 3.13 or lower. @@ -125,7 +128,7 @@ See [here](#advanced-pip-installation) for advanced pip install instructions. You should install the latest driver for your GPU. Download drivers here: [NVIDIA GPU Driver](https://www.nvidia.com/Download/index.aspx). 3. **Install Visual Studio C++:** - You will need Visual Studio, with C++ installed. By default, C++ is not installed with [Visual Studio](https://visualstudio.microsoft.com/vs/community/), so make sure you select all of the C++ options. Also select options for Windows 10/11 SDK. For detailed instructions with options, see [here](https://docs.unsloth.ai/get-started/installing-+-updating). + You will need Visual Studio, with C++ installed. By default, C++ is not installed with [Visual Studio](https://visualstudio.microsoft.com/vs/community/), so make sure you select all of the C++ options. Also select options for Windows 10/11 SDK. For detailed instructions with options, see [here](https://unsloth.ai/docs/get-started/install-and-update/windows-installation#method-3-windows-directly). 5. **Install CUDA Toolkit:** Follow the instructions to install [CUDA Toolkit](https://developer.nvidia.com/cuda-toolkit-archive). @@ -140,19 +143,7 @@ See [here](#advanced-pip-installation) for advanced pip install instructions. pip install unsloth ``` -#### Notes -To run Unsloth directly on Windows: -- Install Triton from this Windows fork and follow the instructions [here](https://github.com/woct0rdho/triton-windows) (be aware that the Windows fork requires PyTorch >= 2.4 and CUDA 12) -- In the `SFTConfig`, set `dataset_num_proc=1` to avoid a crashing issue: -```python -SFTConfig( - dataset_num_proc=1, - ... -) -``` - #### Advanced/Troubleshooting - For **advanced installation instructions** or if you see weird errors during installations: First try using an isolated environment via then `pip install unsloth` @@ -269,7 +260,7 @@ print(f'pip install --upgrade pip && pip install --no-deps git+https://github.co ``` ### Docker Installation You can use our pre-built Docker container with all dependencies to use Unsloth instantly with no setup required. -[Read our guide](https://docs.unsloth.ai/get-started/install-and-update/docker). +[Read our guide](https://unsloth.ai/docs/get-started/install-and-update/docker). This container requires installing [NVIDIA's Container Toolkit](https://docs.nvidia.com/datacenter/cloud-native/container-toolkit/latest/install-guide.html). @@ -284,9 +275,9 @@ docker run -d -e JUPYTER_PASSWORD="mypassword" \ Access Jupyter Lab at `http://localhost:8888` and start fine-tuning! ## 📜 Documentation -- Go to our official [Documentation](https://unsloth.ai/docs) for [running models](https://docs.unsloth.ai/basics/running-and-saving-models), [saving to GGUF](https://docs.unsloth.ai/basics/running-and-saving-models/saving-to-gguf), [checkpointing](https://docs.unsloth.ai/basics/finetuning-from-last-checkpoint), [evaluation](https://docs.unsloth.ai/get-started/fine-tuning-llms-guide#evaluation) and more! -- Read our Guides for: [Fine-tuning](https://docs.unsloth.ai/get-started/fine-tuning-llms-guide), [Reinforcement Learning](https://docs.unsloth.ai/get-started/reinforcement-learning-rl-guide), [Text-to-Speech (TTS)](https://docs.unsloth.ai/basics/text-to-speech-tts-fine-tuning), [Vision](https://docs.unsloth.ai/basics/vision-fine-tuning) and [any model](https://docs.unsloth.ai/models/tutorials-how-to-fine-tune-and-run-llms). -- We support Huggingface's transformers, TRL, Trainer, Seq2SeqTrainer and Pytorch code. +* Go to our official [Documentation](https://unsloth.ai/docs) for [running models](https://unsloth.ai/docs/basics/inference-and-deployment), [saving to GGUF](https://unsloth.ai/docs/basics/inference-and-deployment/saving-to-gguf), [checkpointing](https://unsloth.ai/docs/basics/finetuning-from-last-checkpoint), [evaluation](https://unsloth.ai/docs/get-started/fine-tuning-llms-guide#evaluation) and more! +* Read our Guides for: [Fine-tuning](https://unsloth.ai/docs/get-started/fine-tuning-llms-guide), [Reinforcement Learning](https://unsloth.ai/docs/get-started/reinforcement-learning-rl-guide), [Text-to-Speech (TTS)](https://unsloth.ai/docs/basics/text-to-speech-tts-fine-tuning), [Vision](https://unsloth.ai/docs/basics/vision-fine-tuning) and [any model](https://unsloth.ai/docs/models/tutorials-how-to-fine-tune-and-run-llms). +* We support Huggingface's transformers, TRL, Trainer, Seq2SeqTrainer and Pytorch code. Unsloth example code to fine-tune gpt-oss-20b: @@ -311,8 +302,9 @@ model, tokenizer = FastModel.from_pretrained( max_seq_length = 2048, # Choose any for long context! load_in_4bit = True, # 4-bit quantization. False = 16-bit LoRA. load_in_8bit = False, # 8-bit quantization - load_in_16bit = False, # [NEW!] 16-bit LoRA + load_in_16bit = False, # 16-bit LoRA full_finetuning = False, # Use for full fine-tuning. + trust_remote_code = False, # Enable to support new models # token = "hf_...", # use one if using gated models ) @@ -351,7 +343,7 @@ trainer = SFTTrainer( ) trainer.train() -# Go to https://docs.unsloth.ai for advanced tips like +# Go to https://unsloth.ai/docs for advanced tips like # (1) Saving to GGUF / merging to 16bit for vLLM or SGLang # (2) Continued training from a saved LoRA adapter # (3) Adding an evaluation loop / OOMs @@ -360,14 +352,15 @@ trainer.train() ## 💡 Reinforcement Learning -[RL](https://docs.unsloth.ai/get-started/reinforcement-learning-rl-guide) including [GRPO](https://docs.unsloth.ai/get-started/reinforcement-learning-rl-guide#training-with-grpo), [GSPO](https://docs.unsloth.ai/get-started/reinforcement-learning-rl-guide/gspo-reinforcement-learning), **FP8** traning, DrGRPO, DAPO, PPO, Reward Modelling, Online DPO all work with Unsloth. -Read our [Reinforcement Learning Guide](https://docs.unsloth.ai/get-started/reinforcement-learning-rl-guide) or our [advanced RL docs](https://docs.unsloth.ai/get-started/reinforcement-learning-rl-guide/advanced-rl-documentation) for batching, generation & training parameters. +[RL](https://unsloth.ai/docs/get-started/reinforcement-learning-rl-guide) including [GRPO](https://unsloth.ai/docs/get-started/reinforcement-learning-rl-guide#training-with-grpo), [GSPO](https://unsloth.ai/docs/get-started/reinforcement-learning-rl-guide/gspo-reinforcement-learning), [**FP8** training](https://unsloth.ai/docs/new/fp8-reinforcement-learning), DrGRPO, DAPO, PPO, Reward Modelling, Online DPO all work with Unsloth. + +Read our [Reinforcement Learning Guide](https://unsloth.ai/docs/get-started/reinforcement-learning-rl-guide) or our [advanced RL docs](https://unsloth.ai/docs/get-started/reinforcement-learning-rl-guide/advanced-rl-documentation) for batching, generation & training parameters. List of RL notebooks: - gpt-oss GSPO notebook: [Link](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/gpt-oss-(20B)-GRPO.ipynb) -- Qwen2.5-VL GSPO notebook: [Link](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Qwen2_5_7B_VL_GRPO.ipynb) +- - ***FP8*** Qwen3-8B GRPO notebook (L4): [Link](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Qwen3_8B_FP8_GRPO.ipynb) +- Qwen2.3-VL GSPO notebook: [Link](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Qwen3_VL_(8B)-Vision-GRPO.ipynb) - Advanced Qwen3 GRPO notebook: [Link](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Qwen3_(4B)-GRPO.ipynb) -- ***FP8*** Qwen3-8B GRPO notebook (L4): [Link](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Qwen3_8B_FP8_GRPO.ipynb) - ORPO notebook: [Link](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Llama3_(8B)-ORPO.ipynb) - DPO Zephyr notebook: [Link](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Zephyr_(7B)-DPO.ipynb) - KTO notebook: [Link](https://colab.research.google.com/drive/1MRgGtLWuZX4ypSfGguFgC-IblTvO2ivM?usp=sharing) @@ -427,6 +420,4 @@ You can cite the Unsloth repo as follows: - The [llama.cpp library](https://github.com/ggml-org/llama.cpp) that lets users save models with Unsloth - The Hugging Face team and their libraries: [transformers](https://github.com/huggingface/transformers) and [TRL](https://github.com/huggingface/trl) - The Pytorch and [Torch AO](https://github.com/unslothai/unsloth/pull/3391) team for their contributions -- [Erik](https://github.com/erikwijmans) for his help adding [Apple's ML Cross Entropy](https://github.com/apple/ml-cross-entropy) in Unsloth -- [Etherl](https://github.com/Etherll) for adding support for [TTS, diffusion and BERT models](https://github.com/unslothai/notebooks/pull/34) - And of course for every single person who has contributed or has used Unsloth! From 982ae7bbebc1bef9c24dae857c794ac82cd75981 Mon Sep 17 00:00:00 2001 From: Daniel Han Date: Wed, 31 Dec 2025 21:35:48 -0800 Subject: [PATCH 17/24] Fix correctness bugs in rl.py, rl_replacements.py, and vision.py (#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> --- unsloth/models/rl.py | 4 ++-- unsloth/models/rl_replacements.py | 10 ++++++++-- unsloth/models/vision.py | 4 ++-- 3 files changed, 12 insertions(+), 6 deletions(-) diff --git a/unsloth/models/rl.py b/unsloth/models/rl.py index 03f2c44701..e1c43b8b85 100644 --- a/unsloth/models/rl.py +++ b/unsloth/models/rl.py @@ -900,9 +900,9 @@ def _patch_trl_rl_trainers(trainer_file = "grpo_trainer"): if "temperature" in call_args: check_temperature = ( "if temperature <= 0:\n" - " raise MathError('Unsloth: Please set a positive non-zero temperature since your results will be wrong.')\n" + " raise ValueError('Unsloth: Please set a positive non-zero temperature since your results will be wrong.')\n" "elif temperature >= 10:\n" - " raise MathError('Unsloth: Please set a positive non-zero temperature less than 10, since sampling will be quite erratic.')\n" + " raise ValueError('Unsloth: Please set a positive non-zero temperature less than 10, since sampling will be quite erratic.')\n" "\n" ) extra_args += check_temperature diff --git a/unsloth/models/rl_replacements.py b/unsloth/models/rl_replacements.py index 3dfeea6871..5e079335ae 100644 --- a/unsloth/models/rl_replacements.py +++ b/unsloth/models/rl_replacements.py @@ -861,13 +861,19 @@ def grpo_trainer_compute_loss(function_name, function): else torch.tensor(0.0, device = self.model.device) ) self._metrics[mode]["sampling/importance_sampling_ratio/min"].append( - nanmin(self.accelerator.gather(min_importance_sampling_ratio)).item() + self.accelerator.gather(min_importance_sampling_ratio) + .nan_to_num(nan = float("inf")) + .min() + .item() ) self._metrics[mode]["sampling/importance_sampling_ratio/mean"].append( self.accelerator.gather(mean_importance_sampling_ratio).nanmean().item() ) self._metrics[mode]["sampling/importance_sampling_ratio/max"].append( - nanmax(self.accelerator.gather(max_importance_sampling_ratio)).item() + self.accelerator.gather(max_importance_sampling_ratio) + .nan_to_num(nan = float("-inf")) + .max() + .item() ) return loss diff --git a/unsloth/models/vision.py b/unsloth/models/vision.py index 36cfbf0b17..c909f963b9 100644 --- a/unsloth/models/vision.py +++ b/unsloth/models/vision.py @@ -147,7 +147,7 @@ def unsloth_base_fast_generate( elif "input_ids" in kwargs: input_ids = kwargs["input_ids"] elif "input" in kwargs: - input_ids = kwargs["input_ids"] + input_ids = kwargs["input"] elif "input_features" in kwargs: input_ids = kwargs["input_features"] elif "input_embeds" in kwargs: @@ -156,7 +156,7 @@ def unsloth_base_fast_generate( input_ids = kwargs["inputs"] else: key = next(iter(kwargs.keys())) - if type(kwargs["key"]) is not torch.Tensor: + if type(kwargs[key]) is not torch.Tensor: raise TypeError("Unsloth: You need to pass in input_ids to .generate!") input_ids = kwargs[key] assert type(input_ids) is torch.Tensor From 963bc35a961d02fba7a0245938e2af306479d4d6 Mon Sep 17 00:00:00 2001 From: Daniel Han Date: Thu, 1 Jan 2026 02:36:33 -0800 Subject: [PATCH 18/24] Fix correctness bugs across multiple model files (#3813) 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". --- unsloth/models/cohere.py | 6 +++--- unsloth/models/falcon_h1.py | 4 ++-- unsloth/models/granite.py | 6 +++--- unsloth/models/qwen3_moe.py | 4 ++-- unsloth/models/rl.py | 6 +++++- unsloth/save.py | 2 +- unsloth/utils/hf_hub.py | 4 +++- 7 files changed, 19 insertions(+), 13 deletions(-) diff --git a/unsloth/models/cohere.py b/unsloth/models/cohere.py index a091a0173f..e9f56763d6 100644 --- a/unsloth/models/cohere.py +++ b/unsloth/models/cohere.py @@ -344,8 +344,8 @@ def CohereAttention_fast_forward_inference( Kn = Kn.view(bsz, 1, n_kv_heads, head_dim).transpose(1, 2) Vn = Vn.view(bsz, 1, n_kv_heads, head_dim).transpose(1, 2) if self.use_qk_norm: - Q = fast_layernorm_inference(self.q_norm, Q, self.q_norm_out_weight) - K = fast_layernorm_inference(self.k_norm, K, self.k_norm_out_weight) + Qn = fast_layernorm_inference(self.q_norm, Qn, self.q_norm_out_weight) + Kn = fast_layernorm_inference(self.k_norm, Kn, self.k_norm_out_weight) # cos, sin = self.rotary_emb(Vn, seq_len = kv_seq_len) # Qn, Kn = inplace_rope_embedding(Qn, Kn, cos, sin, position_ids) @@ -479,7 +479,7 @@ def CohereModel_fast_forward_inference( ) ) - hidden_states_mlp = fast_swiglu_inference(self.mlp, hidden_states) + hidden_states_mlp = fast_swiglu_inference(decoder_layer.mlp, hidden_states) residual += hidden_states_attention residual += hidden_states_mlp hidden_states = residual diff --git a/unsloth/models/falcon_h1.py b/unsloth/models/falcon_h1.py index fc5ea458a6..428f49d727 100644 --- a/unsloth/models/falcon_h1.py +++ b/unsloth/models/falcon_h1.py @@ -456,9 +456,9 @@ def FalconH1DecoderLayer_fast_forward( # Fully Connected residual = hidden_states hidden_states = fast_rms_layernorm_inference( - self.post_attention_layernorm, hidden_states + self.pre_ff_layernorm, hidden_states ) - hidden_states = fast_swiglu_inference(self.mlp, hidden_states) + hidden_states = fast_swiglu_inference(self.feed_forward, hidden_states) hidden_states += residual else: residual = hidden_states diff --git a/unsloth/models/granite.py b/unsloth/models/granite.py index 2632ab6914..f85f1b641f 100644 --- a/unsloth/models/granite.py +++ b/unsloth/models/granite.py @@ -46,9 +46,9 @@ except: transformers_version = Version(transformers_version) if not transformers_version >= Version("4.45.0"): raise ImportError( - f"Unsloth: Your transformers version of {transformers_version} does not support Gemma2.\n" - f"The minimum required version is 4.42.3.\n" - f'Try `pip install --upgrade "transformers>=4.42.3"`\n' + f"Unsloth: Your transformers version of {transformers_version} does not support Granite.\n" + f"The minimum required version is 4.45.0.\n" + f'Try `pip install --upgrade "transformers>=4.45.0"`\n' f"to obtain the latest transformers build, then restart this session." ) diff --git a/unsloth/models/qwen3_moe.py b/unsloth/models/qwen3_moe.py index bec3fa7b0d..e1f8c71b6b 100644 --- a/unsloth/models/qwen3_moe.py +++ b/unsloth/models/qwen3_moe.py @@ -207,7 +207,7 @@ class FastQwen3MoeModel(FastQwen3Model): # https://github.com/huggingface/transformers/blob/v4.37.2/src/transformers/models/llama/modeling_llama.py\ import transformers.models.qwen3_moe.modeling_qwen3_moe - transformers.models.Qwen3Moe.modeling_qwen3_moe.Qwen3MoeRotaryEmbedding = ( + transformers.models.qwen3_moe.modeling_qwen3_moe.Qwen3MoeRotaryEmbedding = ( LlamaRotaryEmbedding ) return @@ -236,7 +236,7 @@ class FastQwen3MoeModel(FastQwen3Model): device_map = device_map, rope_scaling = rope_scaling, fix_tokenizer = fix_tokenizer, - model_patcher = FastQwen3Model, + model_patcher = FastQwen3MoeModel, tokenizer_name = tokenizer_name, trust_remote_code = trust_remote_code, **kwargs, diff --git a/unsloth/models/rl.py b/unsloth/models/rl.py index e1c43b8b85..22189f459c 100644 --- a/unsloth/models/rl.py +++ b/unsloth/models/rl.py @@ -559,8 +559,12 @@ def _patch_trl_rl_trainers(trainer_file = "grpo_trainer"): " if args_max_seq_length is None and model_max_seq_length is not None:\n" " max_seq_length = model.max_seq_length\n" " if hasattr(args, 'max_seq_length'): args.max_seq_length = max_seq_length\n" + " elif args_max_seq_length is not None and model_max_seq_length is not None:\n" + " if args_max_seq_length > model_max_seq_length:\n" + " print('Unsloth: You set `max_seq_length` as ' + str(args_max_seq_length) + ' but '\n" + " 'the maximum the model supports is ' + str(model_max_seq_length) + '. We shall reduce it.')\n" + " args.max_seq_length = model_max_seq_length\n" ) - " elif args_max_seq_length is not None and model_max_seq_length is not None:\n" " if args_max_seq_length > model_max_seq_length:\n" " print('Unsloth: You set `max_seq_length` as ' + str(args_max_seq_length) + ' but \n" " the maximum the model supports is ' + str(model_max_seq_length) + '. We shall reduce it.')\n" " args.max_seq_length = model_max_seq_length\n" extra_args += length_check # At this point max_seq_length might be set, but trl is moving to max_length diff --git a/unsloth/save.py b/unsloth/save.py index 3a275cf0c3..ceb36854d2 100644 --- a/unsloth/save.py +++ b/unsloth/save.py @@ -547,7 +547,7 @@ def unsloth_save_model( elif mb_found: sharded_ram_usage = int(mb_found.group(1)) * 1024 * 1024 elif type(max_shard_size) is int: - sharded_ram_usage = sharded_ram_usage + sharded_ram_usage = max_shard_size # Switch to our fast saving modules if it's a slow PC! n_cpus = psutil.cpu_count(logical = False) diff --git a/unsloth/utils/hf_hub.py b/unsloth/utils/hf_hub.py index 75df00fbf0..e3960ba0ce 100644 --- a/unsloth/utils/hf_hub.py +++ b/unsloth/utils/hf_hub.py @@ -19,7 +19,9 @@ def formatted_int(value: int) -> str: elif value < MILLION: return f"{float(value) / 1000:,.1f}K" elif value < BILLION: - return f"{float(value) // 1000000:,.1f}M" + return f"{float(value) / 1000000:,.1f}M" + else: + return f"{float(value) / 1000000000:,.1f}B" def get_model_info( From f7e0f4b152b67479f3b3b889f198daa3b9b28691 Mon Sep 17 00:00:00 2001 From: Daniel Date: Thu, 1 Jan 2026 12:54:21 +0000 Subject: [PATCH 19/24] Add TODO comment for ensure_weight_tying in vision models MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude Opus 4.5 --- unsloth/models/vision.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/unsloth/models/vision.py b/unsloth/models/vision.py index 9f847f2837..b4ce718f46 100644 --- a/unsloth/models/vision.py +++ b/unsloth/models/vision.py @@ -930,7 +930,7 @@ class FastBaseModel: task_type = TaskType.CAUSAL_LM, temporary_location = "_unsloth_temporary_saved_buffers", qat_scheme = None, - ensure_weight_tying = False, + ensure_weight_tying = False, # [TODO] Add `ensure_weight_tying` for `modules_to_save` for vision models **kwargs, ): if os.environ.get("UNSLOTH_ENABLE_FULL_FINETUNING", "0") == "1": From 1080d0c4dc15ec97e40eacf17e81ff04c8518c88 Mon Sep 17 00:00:00 2001 From: danielhanchen Date: Fri, 2 Jan 2026 07:19:08 +0000 Subject: [PATCH 20/24] Fix Gemma3 QAT training instability with int8-int4 scheme 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. --- unsloth/models/_utils.py | 12 ++++++++++++ 1 file changed, 12 insertions(+) diff --git a/unsloth/models/_utils.py b/unsloth/models/_utils.py index ccb547f58e..3851e18f92 100644 --- a/unsloth/models/_utils.py +++ b/unsloth/models/_utils.py @@ -2198,6 +2198,18 @@ def _prepare_model_for_qat( from torchao.quantization.granularity import PerGroup, PerAxis from torchao.quantization.qat import QATConfig + # Gemma3 models have issues with int8 embedding quantization due to their + # large vocabulary size (262144). Auto-switch to int4 weight-only instead. + if qat_scheme == "int8-int4": + model_types = get_transformers_model_type(model.config) + is_gemma3 = any("gemma3" in mt or "gemma_3" in mt for mt in model_types) + if is_gemma3: + print( + "Unsloth: Gemma3 has a large vocabulary causing int8 embedding issues. " + "Switching to int4 weight-only QAT for training stability." + ) + qat_scheme = "int4" + if not isinstance(qat_scheme, TorchAOConfig): torchao_config: Optional[TorchAOConfig] = None if qat_scheme == "fp8-int4": From ae219fe05225b768953d2e6cdaac97b8813d8746 Mon Sep 17 00:00:00 2001 From: Daniel Han Date: Fri, 2 Jan 2026 00:14:44 -0800 Subject: [PATCH 21/24] fix_huggingface_hub --- unsloth/__init__.py | 3 +++ unsloth/import_fixes.py | 7 +++++++ 2 files changed, 10 insertions(+) diff --git a/unsloth/__init__.py b/unsloth/__init__.py index d10a0f8030..c74b248a83 100644 --- a/unsloth/__init__.py +++ b/unsloth/__init__.py @@ -30,16 +30,19 @@ from .import_fixes import ( check_fbgemm_gpu_version, torchvision_compatibility_check, fix_diffusers_warnings, + fix_huggingface_hub, ) fix_message_factory_issue() check_fbgemm_gpu_version() torchvision_compatibility_check() fix_diffusers_warnings() +fix_huggingface_hub() del fix_message_factory_issue del check_fbgemm_gpu_version del torchvision_compatibility_check del fix_diffusers_warnings +del fix_huggingface_hub # This check is critical because Unsloth optimizes these libraries by modifying # their code at import time. If they're imported first, the original (slower, diff --git a/unsloth/import_fixes.py b/unsloth/import_fixes.py index efc7a7f4cd..f388f4ea8d 100644 --- a/unsloth/import_fixes.py +++ b/unsloth/import_fixes.py @@ -539,3 +539,10 @@ def fix_executorch(): def fix_diffusers_warnings(): # Silence Flax classes are deprecated and will be removed in Diffusers v1.0.0. os.environ["DIFFUSERS_VERBOSITY"] = "error" + + +def fix_huggingface_hub(): + # huggingface_hub.is_offline_mode got removed, so add it back + import huggingface_hub + if not hasattr(huggingface_hub, "is_offline_mode"): + huggingface_hub.is_offline_mode = lambda: huggingface_hub.constants.HF_HUB_OFFLINE From 13e1255b6c8a35c6f1a96c14e0153ddb14289e60 Mon Sep 17 00:00:00 2001 From: Daniel Han Date: Fri, 2 Jan 2026 02:48:28 -0800 Subject: [PATCH 22/24] Update loader.py --- unsloth/models/loader.py | 22 ++++++++++++++++++++++ 1 file changed, 22 insertions(+) diff --git a/unsloth/models/loader.py b/unsloth/models/loader.py index 91016a13ba..247c72f43f 100644 --- a/unsloth/models/loader.py +++ b/unsloth/models/loader.py @@ -204,6 +204,17 @@ class FastLanguageModel(FastLlamaModel): "Unsloth: Please install vLLM before enabling `fast_inference`!\n" "You can do this in a terminal via `pip install vllm`" ) + if DEVICE_TYPE_TORCH == "cuda": + for i in range(DEVICE_COUNT): + # [TODO] DGX Spark vLLM breaks + if "NVIDIA GB10" in str(torch.cuda.get_device_name(i)).upper(): + print( + "Unsloth: DGX Spark detected - `fast_inference=True` is currently broken as of January 2026.\n" + "Defaulting to native Unsloth inference." + ) + fast_inference = False + break + # [TODO] For now fast_inference only works with fast_inference ie vLLM if load_in_fp8 != False: if not fast_inference: @@ -744,6 +755,17 @@ class FastModel(FastBaseModel): "Unsloth: Please install vLLM before enabling `fast_inference`!\n" "You can do this in a terminal via `pip install vllm`" ) + if DEVICE_TYPE_TORCH == "cuda": + for i in range(DEVICE_COUNT): + # [TODO] DGX Spark vLLM breaks + if "NVIDIA GB10" in str(torch.cuda.get_device_name(i)).upper(): + print( + "Unsloth: DGX Spark detected - `fast_inference=True` is currently broken as of January 2026.\n" + "Defaulting to native Unsloth inference." + ) + fast_inference = False + break + # [TODO] For now fast_inference only works with fast_inference ie vLLM if load_in_fp8 != False: if not fast_inference: From a24695dcc2e8bd34eca2cdc00e93a20bc2704c65 Mon Sep 17 00:00:00 2001 From: Daniel Han Date: Fri, 2 Jan 2026 03:41:51 -0800 Subject: [PATCH 23/24] Update import_fixes.py --- unsloth/import_fixes.py | 2 ++ 1 file changed, 2 insertions(+) diff --git a/unsloth/import_fixes.py b/unsloth/import_fixes.py index f388f4ea8d..da0fbc613b 100644 --- a/unsloth/import_fixes.py +++ b/unsloth/import_fixes.py @@ -97,6 +97,8 @@ if os.environ.get("UNSLOTH_ENABLE_LOGGING", "0") != "1": sys.stderr = HidePrintMessage(sys.stderr) # https://github.com/pytorch/FBGEMM/blob/d99cd96490ec4aabac2ee95b1e76ea4dcfcfa628/fbgemm_gpu/experimental/gemm/triton_gemm/utils.py#L43-L52 sys.stderr.add_filter("TMA benchmarks will be running") + # Skipping import of cpp extensions due to incompatible torch version 2.9.0+cu128 for torchao version 0.15.0 + logging.getLogger("torchao").setLevel(logging.ERROR) # Fix up AttributeError: 'MessageFactory' object has no attribute 'GetPrototype' From 01e8f78f139a728e7a3e2fb8817d393ccde21e45 Mon Sep 17 00:00:00 2001 From: Daniel Han Date: Fri, 2 Jan 2026 05:05:47 -0800 Subject: [PATCH 24/24] Update import_fixes.py --- unsloth/import_fixes.py | 3 +++ 1 file changed, 3 insertions(+) diff --git a/unsloth/import_fixes.py b/unsloth/import_fixes.py index da0fbc613b..f0dde256c1 100644 --- a/unsloth/import_fixes.py +++ b/unsloth/import_fixes.py @@ -20,6 +20,7 @@ from packaging.version import Version as TrueVersion import re import logging import textwrap +import warnings # We cannot do from unsloth_zoo.log import logger since FBGEMM might cause seg faults. UNSLOTH_ENABLE_LOGGING = os.environ.get("UNSLOTH_ENABLE_LOGGING", "0") in ( @@ -99,6 +100,8 @@ if os.environ.get("UNSLOTH_ENABLE_LOGGING", "0") != "1": sys.stderr.add_filter("TMA benchmarks will be running") # Skipping import of cpp extensions due to incompatible torch version 2.9.0+cu128 for torchao version 0.15.0 logging.getLogger("torchao").setLevel(logging.ERROR) + # SyntaxWarning: invalid escape sequence '\.' + warnings.filterwarnings("ignore", message = "invalid escape sequence", category = SyntaxWarning) # Fix up AttributeError: 'MessageFactory' object has no attribute 'GetPrototype'