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 diff --git a/README.md b/README.md index 43c09381fc..ae1fccfbba 100644 --- a/README.md +++ b/README.md @@ -1,6 +1,6 @@
- + unsloth logo @@ -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://unsloth.ai/docs/get-started/fine-tuning-llms-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 @@ -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://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) -- **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) @@ -84,27 +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. -- 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. @@ -124,7 +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). @@ -139,19 +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` @@ -268,7 +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). @@ -283,9 +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://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! -- 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: @@ -310,8 +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 ) @@ -350,7 +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 @@ -359,14 +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) @@ -426,6 +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! diff --git a/pyproject.toml b/pyproject.toml index decc0e9f5f..e7b84f3c8e 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -60,7 +60,7 @@ huggingfacenotorch = [ ] huggingface = [ "unsloth[huggingfacenotorch]", - "unsloth_zoo>=2025.12.7", + "unsloth_zoo>=2026.1.1", "torchvision", "unsloth[triton]", ] @@ -523,7 +523,7 @@ colab-ampere-torch220 = [ "flash-attn>=2.6.3 ; ('linux' in sys_platform)", ] colab-new = [ - "unsloth_zoo>=2025.12.7", + "unsloth_zoo>=2026.1.1", "packaging", "tyro", "transformers>=4.51.3,!=4.52.0,!=4.52.1,!=4.52.2,!=4.52.3,!=4.53.0,!=4.54.0,!=4.55.0,!=4.55.1,!=4.57.0,<=4.57.3", diff --git a/unsloth/__init__.py b/unsloth/__init__.py index d10a0f8030..86fb00fe0e 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, @@ -76,7 +79,7 @@ from importlib.metadata import PackageNotFoundError # Check for unsloth_zoo try: unsloth_zoo_version = importlib_version("unsloth_zoo") - if Version(unsloth_zoo_version) < Version("2025.12.4"): + if Version(unsloth_zoo_version) < Version("2026.1.1"): print( "Unsloth: Please update Unsloth and Unsloth-Zoo to the latest version!\n" "Do this via `pip install --upgrade --force-reinstall --no-cache-dir --no-deps unsloth unsloth_zoo`" @@ -123,6 +126,7 @@ from .import_fixes import ( fix_xformers_performance_issue, fix_vllm_aimv2_issue, fix_vllm_guided_decoding_params, + fix_vllm_pdl_blackwell, ignore_logger_messages, patch_ipykernel_hf_xet, patch_trackio, @@ -135,6 +139,7 @@ from .import_fixes import ( fix_xformers_performance_issue() fix_vllm_aimv2_issue() fix_vllm_guided_decoding_params() +fix_vllm_pdl_blackwell() ignore_logger_messages() patch_ipykernel_hf_xet() patch_trackio() @@ -146,6 +151,7 @@ fix_executorch() del fix_xformers_performance_issue del fix_vllm_aimv2_issue del fix_vllm_guided_decoding_params +del fix_vllm_pdl_blackwell del ignore_logger_messages del patch_ipykernel_hf_xet del patch_trackio 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 diff --git a/unsloth/import_fixes.py b/unsloth/import_fixes.py index efc7a7f4cd..1e05e462e9 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 ( @@ -97,6 +98,12 @@ 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) + # SyntaxWarning: invalid escape sequence '\.' + warnings.filterwarnings( + "ignore", message = "invalid escape sequence", category = SyntaxWarning + ) # Fix up AttributeError: 'MessageFactory' object has no attribute 'GetPrototype' @@ -539,3 +546,128 @@ 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 + ) + + +def fix_vllm_pdl_blackwell(): + """ + Fix vLLM PDL (Programmatic Dependent Launch) bug on Blackwell GPUs (SM100). + + The issue: vLLM's LoRA Triton kernels use tl.extra.cuda.gdc_wait() for PDL + optimization on SM90+ GPUs. This fails on SM100 (B200/B100) during CUDA graph + capture because Triton's pipeliner can't handle gdc_wait in complex kernels. + + See: https://github.com/vllm-project/vllm/issues/30872 + """ + if importlib.util.find_spec("vllm") is None: + return + + # Check if any CUDA GPU is SM100 (Blackwell) + try: + import torch + + if not torch.cuda.is_available(): + return + + # Scan all GPUs for SM100 - fix applies globally via env var and monkey-patch + has_sm100 = False + sm100_gpu_name = None + for i in range(torch.cuda.device_count()): + major, minor = torch.cuda.get_device_capability(i) + if major == 10: + has_sm100 = True + sm100_gpu_name = torch.cuda.get_device_name(i) + break + + if not has_sm100: + return + except Exception: + return + + # Helper to check if module spec exists + def _spec_exists(name): + try: + return importlib.util.find_spec(name) is not None + except (ModuleNotFoundError, ValueError): + return False + + # Check if vLLM has the PDL-related modules before doing internet check + has_utils = _spec_exists("vllm.lora.ops.triton_ops.utils") + has_expand_op = _spec_exists("vllm.lora.ops.triton_ops.lora_expand_op") + has_shrink_op = _spec_exists("vllm.lora.ops.triton_ops.lora_shrink_op") + + if not has_utils and not has_expand_op and not has_shrink_op: + # Old vLLM version without PDL support - nothing to patch + return + + # Check if vLLM version includes the fix + VLLM_PDL_FIX_VERSION = "0.13.2" + try: + vllm_version = Version(importlib_version("vllm")) + if vllm_version > Version(VLLM_PDL_FIX_VERSION): + logger.info( + f"Unsloth: SM100 ({sm100_gpu_name}) detected but vLLM {vllm_version} " + f"should include PDL fix - skipping workaround" + ) + return + except Exception as e: + logger.debug( + f"Unsloth: vLLM version check failed ({e}), applying PDL workaround." + ) + + # Apply the PDL fix + os.environ["TRITON_DISABLE_PDL"] = "1" + + def fake_supports_pdl(*args, **kwargs): + return False + + patched = [] + + # First, patch the source module (utils.py) where supports_pdl is defined. + # This is critical because supports_pdl uses @lru_cache - we must clear the + # cache to prevent stale cached results from the original function. + try: + utils_module = importlib.import_module("vllm.lora.ops.triton_ops.utils") + if hasattr(utils_module, "supports_pdl"): + original_fn = utils_module.supports_pdl + if hasattr(original_fn, "cache_clear"): + original_fn.cache_clear() + utils_module.supports_pdl = fake_supports_pdl + patched.append("utils") + except (ImportError, ModuleNotFoundError, AttributeError): + pass + + # Also patch the consumer modules that import supports_pdl from utils. + # This ensures the patched function is used even if the module was already + # imported before this fix runs. + consumer_modules = { + "lora_expand_op": "vllm.lora.ops.triton_ops.lora_expand_op", + "lora_shrink_op": "vllm.lora.ops.triton_ops.lora_shrink_op", + "fused_moe_lora_op": "vllm.lora.ops.triton_ops.fused_moe_lora_op", + } + for name, path in consumer_modules.items(): + try: + module = importlib.import_module(path) + if hasattr(module, "supports_pdl"): + module.supports_pdl = fake_supports_pdl + patched.append(name) + except (ImportError, ModuleNotFoundError, AttributeError): + pass + + if patched: + logger.info( + f"Unsloth: Applied PDL fix for SM100 ({sm100_gpu_name}) - " + f"patched: {', '.join(patched)}" + ) + else: + # Just set the env var - vLLM might be an older version without supports_pdl + logger.info(f"Unsloth: Set TRITON_DISABLE_PDL=1 for SM100 ({sm100_gpu_name})") diff --git a/unsloth/kernels/fast_lora.py b/unsloth/kernels/fast_lora.py index 60d0c318c3..f1c0e298d9 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 - 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) + # 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 + X_for_matmul = X + if X.dim() == 3: + 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: + 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, 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, diff --git a/unsloth/models/_utils.py b/unsloth/models/_utils.py index abc8380562..5952d4af0c 100644 --- a/unsloth/models/_utils.py +++ b/unsloth/models/_utils.py @@ -12,7 +12,7 @@ # See the License for the specific language governing permissions and # limitations under the License. -__version__ = "2025.12.9" +__version__ = "2026.1.1" __all__ = [ "SUPPORTS_BFLOAT16", @@ -73,6 +73,7 @@ __all__ = [ "verify_fp8_support_if_applicable", "_get_inference_mode_context_manager", "hf_login", + "make_fast_generate_wrapper", ] import torch @@ -1981,9 +1982,10 @@ 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": @@ -2197,6 +2199,18 @@ 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": @@ -2365,3 +2379,59 @@ def hf_login(token: Optional[str] = None) -> Optional[str]: except Exception as e: logger.info(f"Failed to login to huggingface using token with error: {e}") return token + + +def make_fast_generate_wrapper(original_generate): + """ + Creates a wrapper around model.generate that checks for incorrect + vLLM-style usage when fast_inference=False. + """ + + @functools.wraps(original_generate) + def _fast_generate_wrapper(*args, **kwargs): + # Check for vLLM-specific arguments + if "sampling_params" in kwargs: + raise ValueError( + "Unsloth: `sampling_params` is only supported when `fast_inference=True` (vLLM). " + "Since `fast_inference=False`, use HuggingFace generate arguments instead:\n" + " model.fast_generate(**tokens.to('cuda'), max_new_tokens=64, temperature=1.0, top_p=0.95)" + ) + + if "lora_request" in kwargs: + raise ValueError( + "Unsloth: `lora_request` is only supported when `fast_inference=True` (vLLM). " + "Since `fast_inference=False`, LoRA weights are already merged into the model." + ) + + # Check if first positional argument is a string or list of strings + if len(args) > 0: + first_arg = args[0] + is_string_input = False + + if isinstance(first_arg, str): + is_string_input = True + elif isinstance(first_arg, (list, tuple)) and len(first_arg) > 0: + if isinstance(first_arg[0], str): + is_string_input = True + + if is_string_input: + raise ValueError( + "Unsloth: Passing text strings to `fast_generate` is only supported " + "when `fast_inference=True` (vLLM). Since `fast_inference=False`, you must " + "tokenize the input first:\n\n" + " messages = tokenizer.apply_chat_template(\n" + ' [{"role": "user", "content": "Your prompt here"}],\n' + " tokenize=True, add_generation_prompt=True,\n" + ' return_tensors="pt", return_dict=True\n' + " )\n" + " output = model.fast_generate(\n" + " **messages.to('cuda'),\n" + " max_new_tokens=64,\n" + " temperature=1.0,\n" + " )" + ) + + # Call original generate + return original_generate(*args, **kwargs) + + return _fast_generate_wrapper 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/llama.py b/unsloth/models/llama.py index 1d7695b9aa..92d51b73ad 100644 --- a/unsloth/models/llama.py +++ b/unsloth/models/llama.py @@ -2326,7 +2326,7 @@ class FastLlamaModel: attn_implementation = "eager", **kwargs, ) - model.fast_generate = model.generate + model.fast_generate = make_fast_generate_wrapper(model.generate) model.fast_generate_batches = None else: from unsloth_zoo.vllm_utils import ( @@ -2600,6 +2600,7 @@ 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": @@ -2629,6 +2630,7 @@ 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": @@ -2779,9 +2781,10 @@ 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": @@ -2952,6 +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: @@ -3001,6 +3005,55 @@ 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 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) + 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/loader.py b/unsloth/models/loader.py index 91016a13ba..eb3b21e206 100644 --- a/unsloth/models/loader.py +++ b/unsloth/models/loader.py @@ -151,8 +151,41 @@ class FastLanguageModel(FastLlamaModel): *args, **kwargs, ): + # Respect user-provided quantization_config (e.g. BitsAndBytesConfig) + quantization_config = kwargs.get("quantization_config", None) + if quantization_config is not None: + if isinstance(quantization_config, dict): + q_load_in_4bit = quantization_config.get("load_in_4bit", False) + q_load_in_8bit = quantization_config.get("load_in_8bit", False) + else: + q_load_in_4bit = getattr(quantization_config, "load_in_4bit", False) + q_load_in_8bit = getattr(quantization_config, "load_in_8bit", False) + if q_load_in_4bit: + load_in_4bit = True + load_in_8bit = False + if q_load_in_8bit: + load_in_8bit = True + load_in_4bit = False + # Login to allow private models token = hf_login(token) + # Align dtype with bnb_4bit_compute_dtype if provided and dtype is unset. + if dtype is None and quantization_config is not None: + bnb_compute_dtype = None + if isinstance(quantization_config, dict): + if quantization_config.get("load_in_4bit", False): + bnb_compute_dtype = quantization_config.get( + "bnb_4bit_compute_dtype", None + ) + else: + if getattr(quantization_config, "load_in_4bit", False): + bnb_compute_dtype = getattr( + quantization_config, "bnb_4bit_compute_dtype", None + ) + if isinstance(bnb_compute_dtype, str): + bnb_compute_dtype = getattr(torch, bnb_compute_dtype, None) + if isinstance(bnb_compute_dtype, torch.dtype): + dtype = bnb_compute_dtype if load_in_8bit or full_finetuning or qat_scheme is not None: return FastModel.from_pretrained( model_name = model_name, @@ -204,6 +237,17 @@ 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: @@ -531,11 +575,17 @@ class FastLanguageModel(FastLlamaModel): if fast_inference: fast_inference, model_name = fast_inference_setup(model_name, model_config) + load_in_4bit_kwargs = load_in_4bit + load_in_8bit_kwargs = load_in_8bit + if quantization_config is not None and not fast_inference: + load_in_4bit_kwargs = False + load_in_8bit_kwargs = False + model, tokenizer = dispatch_model.from_pretrained( model_name = model_name, max_seq_length = max_seq_length, dtype = _get_dtype(dtype), - load_in_4bit = load_in_4bit, + load_in_4bit = load_in_4bit_kwargs, token = token, device_map = device_map, rope_scaling = rope_scaling, @@ -572,22 +622,30 @@ class FastLanguageModel(FastLlamaModel): ) if load_in_4bit: - # Fix up bitsandbytes config - compute_dtype = dtype_from_config(model.config) - quantization_config = { - # Sometimes compute_dtype is not a string!! - "bnb_4bit_compute_dtype": compute_dtype, - "bnb_4bit_quant_type": "nf4", - "bnb_4bit_use_double_quant": True, - "llm_int8_enable_fp32_cpu_offload": False, - "llm_int8_has_fp16_weight": False, - "llm_int8_skip_modules": None, - "llm_int8_threshold": 6.0, - "load_in_4bit": True, - "load_in_8bit": False, - "quant_method": "bitsandbytes", - } - model.config.update({"quantization_config": quantization_config}) + # Fix up bitsandbytes config, but respect user-provided quantization_config + if quantization_config is None: + compute_dtype = dtype_from_config(model.config) + quantization_config = { + # Sometimes compute_dtype is not a string!! + "bnb_4bit_compute_dtype": compute_dtype, + "bnb_4bit_quant_type": "nf4", + "bnb_4bit_use_double_quant": True, + "llm_int8_enable_fp32_cpu_offload": False, + "llm_int8_has_fp16_weight": False, + "llm_int8_skip_modules": None, + "llm_int8_threshold": 6.0, + "load_in_4bit": True, + "load_in_8bit": False, + "quant_method": "bitsandbytes", + } + model.config.update({"quantization_config": quantization_config}) + else: + if hasattr(quantization_config, "to_dict"): + model.config.update( + {"quantization_config": quantization_config.to_dict()} + ) + elif isinstance(quantization_config, dict): + model.config.update({"quantization_config": quantization_config}) if load_in_fp8 != False: _tag_model_with_fp8_torchao_config(model, fp8_mode) @@ -679,12 +737,45 @@ class FastModel(FastBaseModel): *args, **kwargs, ): + # Respect user-provided quantization_config (e.g. BitsAndBytesConfig) + quantization_config = kwargs.get("quantization_config", None) + if quantization_config is not None: + if isinstance(quantization_config, dict): + q_load_in_4bit = quantization_config.get("load_in_4bit", False) + q_load_in_8bit = quantization_config.get("load_in_8bit", False) + else: + q_load_in_4bit = getattr(quantization_config, "load_in_4bit", False) + q_load_in_8bit = getattr(quantization_config, "load_in_8bit", False) + if q_load_in_4bit: + load_in_4bit = True + load_in_8bit = False + if q_load_in_8bit: + load_in_8bit = True + load_in_4bit = False + # Login to allow private models token = hf_login(token) if whisper_language is not None: assert type(whisper_language) is str if whisper_task is not None: assert type(whisper_task) is str + # Align dtype with bnb_4bit_compute_dtype if provided and dtype is unset. + if dtype is None and quantization_config is not None: + bnb_compute_dtype = None + if isinstance(quantization_config, dict): + if quantization_config.get("load_in_4bit", False): + bnb_compute_dtype = quantization_config.get( + "bnb_4bit_compute_dtype", None + ) + else: + if getattr(quantization_config, "load_in_4bit", False): + bnb_compute_dtype = getattr( + quantization_config, "bnb_4bit_compute_dtype", None + ) + if isinstance(bnb_compute_dtype, str): + bnb_compute_dtype = getattr(torch, bnb_compute_dtype, None) + if isinstance(bnb_compute_dtype, torch.dtype): + dtype = bnb_compute_dtype SUPPORTS_BFLOAT16 = is_bfloat16_supported() if dtype is None: dtype = torch.float16 if not SUPPORTS_BFLOAT16 else torch.bfloat16 @@ -744,6 +835,17 @@ 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: @@ -1147,12 +1249,18 @@ class FastModel(FastBaseModel): if auto_model is None: auto_model = AutoModelForVision2Seq if is_vlm else AutoModelForCausalLM + load_in_4bit_kwargs = load_in_4bit + load_in_8bit_kwargs = load_in_8bit + if quantization_config is not None and not fast_inference: + load_in_4bit_kwargs = False + load_in_8bit_kwargs = False + model, tokenizer = FastBaseModel.from_pretrained( model_name = model_name, max_seq_length = max_seq_length, dtype = _get_dtype(dtype), - load_in_4bit = load_in_4bit, - load_in_8bit = load_in_8bit, + load_in_4bit = load_in_4bit_kwargs, + load_in_8bit = load_in_8bit_kwargs, load_in_16bit = load_in_16bit, full_finetuning = full_finetuning, token = token, @@ -1198,22 +1306,30 @@ class FastModel(FastBaseModel): ) if load_in_4bit: - # Fix up bitsandbytes config - compute_dtype = dtype_from_config(model.config) - quantization_config = { - # Sometimes compute_dtype is not a string!! - "bnb_4bit_compute_dtype": compute_dtype, - "bnb_4bit_quant_type": "nf4", - "bnb_4bit_use_double_quant": True, - "llm_int8_enable_fp32_cpu_offload": False, - "llm_int8_has_fp16_weight": False, - "llm_int8_skip_modules": None, - "llm_int8_threshold": 6.0, - "load_in_4bit": True, - "load_in_8bit": False, - "quant_method": "bitsandbytes", - } - model.config.update({"quantization_config": quantization_config}) + # Fix up bitsandbytes config, but respect user-provided quantization_config + if quantization_config is None: + compute_dtype = dtype_from_config(model.config) + quantization_config = { + # Sometimes compute_dtype is not a string!! + "bnb_4bit_compute_dtype": compute_dtype, + "bnb_4bit_quant_type": "nf4", + "bnb_4bit_use_double_quant": True, + "llm_int8_enable_fp32_cpu_offload": False, + "llm_int8_has_fp16_weight": False, + "llm_int8_skip_modules": None, + "llm_int8_threshold": 6.0, + "load_in_4bit": True, + "load_in_8bit": False, + "quant_method": "bitsandbytes", + } + model.config.update({"quantization_config": quantization_config}) + else: + if hasattr(quantization_config, "to_dict"): + model.config.update( + {"quantization_config": quantization_config.to_dict()} + ) + elif isinstance(quantization_config, dict): + model.config.update({"quantization_config": quantization_config}) if load_in_fp8 != False: _tag_model_with_fp8_torchao_config(model, fp8_mode) 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/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 1327208c46..fa95a809b2 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 @@ -200,15 +199,15 @@ def PatchRL(FastLanguageModel): unwrap = "unwrap_model_for_generation" for trainer in trainers: try: - current_trainer = eval(f"trl.trainer.{trainer}") + current_trainer = getattr(trl.trainer, trainer) except: continue if hasattr(current_trainer, unwrap): try: - exec(f"trl.trainer.{trainer}.{unwrap} = unsloth_{unwrap}") + setattr(current_trainer, unwrap, unsloth_unwrap_model_for_generation) except: continue - exec(f"Trainer.prediction_step=unsloth_prediction_step") + Trainer.prediction_step = unsloth_prediction_step selective_log_softmax = RL_REPLACEMENTS["selective_log_softmax"] @@ -227,6 +226,7 @@ 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 @@ -234,6 +234,10 @@ from transformers.training_args import ParallelMode # Also patches W&B since multiple runs must use wandb.finish() import functools from types import MethodType +try: + from unsloth_zoo.gradient_checkpointing import reset_unsloth_gradient_checkpointing_buffers +except: + def reset_unsloth_gradient_checkpointing_buffers(): pass def prepare_for_training_mode(f): @functools.wraps(f) def wrapper(self, *args, **kwargs): @@ -250,6 +254,11 @@ def prepare_for_training_mode(f): self.model.for_inference() elif _was_training is True and hasattr(self.model, "for_training"): self.model.for_training() + # Reset gradient checkpointing buffers to free memory while staying ready for next run + try: + reset_unsloth_gradient_checkpointing_buffers() + except: + pass # Patch W&B to enable logging on future runs, otherwise it'll overwrite the first run try: import wandb @@ -591,8 +600,12 @@ 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 @@ -713,6 +726,19 @@ def _patch_trl_rl_trainers(trainer_file = "grpo_trainer"): ) RLTrainer_post += training_check + # Sync chat_template from processing_class to vLLM's tokenizer + # This fixes base models that have custom chat templates applied after loading + if "model" in call_args: + vllm_chat_template_sync = ( + "if hasattr(self, 'llm') and self.llm is not None and hasattr(self.llm, 'get_tokenizer'):\n" + " _vllm_tok = self.llm.get_tokenizer()\n" + " _pc = getattr(self, 'processing_class', None) or getattr(self, 'tokenizer', None)\n" + " if _vllm_tok is not None and _pc is not None and getattr(_pc, 'chat_template', None) is not None and getattr(_vllm_tok, 'chat_template', None) is None:\n" + " _vllm_tok.chat_template = _pc.chat_template\n" + "pass\n" + ) + RLTrainer_post += vllm_chat_template_sync + # Edit optional metrics other_metrics_processor = "" if trainer_file in RL_METRICS_CHANGES: @@ -845,7 +871,7 @@ def _patch_trl_rl_trainers(trainer_file = "grpo_trainer"): num_proc_check = ( "if dataset_num_proc is None:\n" " import psutil\n" - " dataset_num_proc = min(max(psutil.cpu_count()+4, 2), 64)\n" + " dataset_num_proc = min(max((psutil.cpu_count() or 1)+4, 2), 64)\n" " memory_gb_left = psutil.virtual_memory().available / (1024**3)\n" " if memory_gb_left <= 4: dataset_num_proc = 1 # Too risky, so set to 1\n" " elif memory_gb_left <= 6: dataset_num_proc = min(2, dataset_num_proc)\n" @@ -932,9 +958,9 @@ 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 @@ -1022,10 +1048,10 @@ def _patch_trl_rl_trainers(trainer_file = "grpo_trainer"): # Temporary patch _is_vlm to False # as of 0.22 it only exists in sfttrainer - oriignal_is_vlm_text = "self._is_vlm = True" + original_is_vlm_text = "self._is_vlm = True" new_is_vlm_text = "self._is_vlm = False" RLTrainer_source = RLTrainer_source.replace( - oriignal_is_vlm_text, new_is_vlm_text + original_is_vlm_text, new_is_vlm_text ) # Remove multiple doc strings diff --git a/unsloth/models/vision.py b/unsloth/models/vision.py index b78b190bcb..6de942d7d2 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 @@ -149,7 +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: @@ -158,7 +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 @@ -531,6 +529,7 @@ class FastBaseModel: del kwargs["attn_implementation"] bnb_config = None + user_quantization_config = kwargs.get("quantization_config", None) if full_finetuning and (load_in_4bit or load_in_8bit): print( "Unsloth: You selected full finetuning support, but 4bit / 8bit is enabled - disabling LoRA / QLoRA." @@ -598,7 +597,8 @@ class FastBaseModel: ): pass else: - kwargs["quantization_config"] = bnb_config + if user_quantization_config is None: + kwargs["quantization_config"] = bnb_config else: if auto_config is None: auto_config = AutoConfig.from_pretrained( @@ -643,7 +643,8 @@ class FastBaseModel: ) except: pass - kwargs["quantization_config"] = quantization_config + if user_quantization_config is None: + kwargs["quantization_config"] = quantization_config # Check if using forced float32 - we load it in bfloat16, then cast to float16! torch_dtype = dtype @@ -675,7 +676,7 @@ class FastBaseModel: **kwargs, ) if hasattr(model, "generate"): - model.fast_generate = model.generate + model.fast_generate = make_fast_generate_wrapper(model.generate) model.fast_generate_batches = error_out_no_vllm if offload_embedding: if bool( @@ -718,9 +719,13 @@ 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: @@ -936,6 +941,7 @@ class FastBaseModel: task_type = TaskType.CAUSAL_LM, temporary_location = "_unsloth_temporary_saved_buffers", qat_scheme = None, + 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": diff --git a/unsloth/save.py b/unsloth/save.py index 3a275cf0c3..071e032c53 100644 --- a/unsloth/save.py +++ b/unsloth/save.py @@ -131,6 +131,13 @@ ALLOWED_QUANTS = { } +def has_curl(): + return shutil.which("curl") is not None + + +CURL_FLAG = "-DLLAMA_CURL=ON" if has_curl() else "-DLLAMA_CURL=OFF" + + def print_quantization_methods(): for key, value in ALLOWED_QUANTS.items(): print(f'"{key}" ==> {value}') @@ -547,7 +554,7 @@ 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) @@ -872,15 +879,16 @@ def install_llama_cpp_make_non_blocking(): IS_CMAKE = False if check == 0: # Uses old MAKE - n_jobs = max(int(psutil.cpu_count() * 1.5), 1) + n_jobs = max(int((psutil.cpu_count() or 1) * 1.5), 1) full_command = ["make", "all", "-j" + str(n_jobs), "-C", "llama.cpp"] IS_CMAKE = False else: # Uses new CMAKE - n_jobs = max(int(psutil.cpu_count()), 1) # Use less CPUs since 1.5x faster + n_jobs = max(int(psutil.cpu_count() or 1), 1) # Use less CPUs since 1.5x faster check = os.system( - "cmake llama.cpp -B llama.cpp/build -DBUILD_SHARED_LIBS=OFF -DGGML_CUDA=OFF -DLLAMA_CURL=ON" + f"cmake llama.cpp -B llama.cpp/build -DBUILD_SHARED_LIBS=OFF -DGGML_CUDA=OFF {CURL_FLAG}" ) + if check != 0: raise RuntimeError( f"*** Unsloth: Failed compiling llama.cpp using os.system(...) with error {check}. Please report this ASAP!" @@ -986,16 +994,17 @@ def install_llama_cpp_old(version = -10): # Try using MAKE commands = [ "make clean -C llama.cpp", - f"make all -j{psutil.cpu_count()*2} -C llama.cpp", + f"make all -j{(psutil.cpu_count() or 1)*2} -C llama.cpp", ] if try_execute(commands) == "CMAKE": # Instead use CMAKE commands = [ - "cmake llama.cpp -B llama.cpp/build -DBUILD_SHARED_LIBS=OFF -DGGML_CUDA=OFF -DLLAMA_CURL=ON", - f"cmake --build llama.cpp/build --config Release -j{psutil.cpu_count()*2} --clean-first --target {' '.join(LLAMA_CPP_TARGETS)}", + f"cmake llama.cpp -B llama.cpp/build -DBUILD_SHARED_LIBS=OFF -DGGML_CUDA=OFF {CURL_FLAG}", + f"cmake --build llama.cpp/build --config Release -j{(psutil.cpu_count() or 1)*2} --clean-first --target {' '.join(LLAMA_CPP_TARGETS)}", "cp llama.cpp/build/bin/llama-* llama.cpp", "rm -rf llama.cpp/build", ] + try_execute(commands) # Check if successful @@ -1031,14 +1040,14 @@ def install_llama_cpp_blocking(use_cuda = False): "make clean -C llama.cpp", # https://github.com/ggerganov/llama.cpp/issues/7062 # Weirdly GPU conversion for GGUF breaks?? - # f"{use_cuda} make all -j{psutil.cpu_count()*2} -C llama.cpp", - f"make all -j{psutil.cpu_count()*2} -C llama.cpp", + # f"{use_cuda} make all -j{(psutil.cpu_count() or 1)*2} -C llama.cpp", + f"make all -j{(psutil.cpu_count() or 1)*2} -C llama.cpp", ] if try_execute(commands) == "CMAKE": # Instead use CMAKE commands = [ - "cmake llama.cpp -B llama.cpp/build -DBUILD_SHARED_LIBS=OFF -DGGML_CUDA=OFF -DLLAMA_CURL=ON", - f"cmake --build llama.cpp/build --config Release -j{psutil.cpu_count()*2} --clean-first --target {' '.join(LLAMA_CPP_TARGETS)}", + f"cmake llama.cpp -B llama.cpp/build -DBUILD_SHARED_LIBS=OFF -DGGML_CUDA=OFF {CURL_FLAG}", + f"cmake --build llama.cpp/build --config Release -j{(psutil.cpu_count() or 1)*2} --clean-first --target {' '.join(LLAMA_CPP_TARGETS)}", "cp llama.cpp/build/bin/llama-* llama.cpp", "rm -rf llama.cpp/build", ] 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 diff --git a/unsloth/utils/attention_dispatch.py b/unsloth/utils/attention_dispatch.py index ccd49dada8..a7620549be 100644 --- a/unsloth/utils/attention_dispatch.py +++ b/unsloth/utils/attention_dispatch.py @@ -32,9 +32,6 @@ from ..utils.packing import ( if HAS_FLASH_ATTENTION: from flash_attn import flash_attn_func, flash_attn_varlen_func HAS_XFORMERS = xformers is not None -BlockDiagonalCausalMask = None -if HAS_XFORMERS: - BlockDiagonalCausalMask = xformers.attn_bias.BlockDiagonalCausalMask SDPA_HAS_GQA = "enable_gqa" in (scaled_dot_product_attention.__doc__ or "") FLASH_VARLEN = "flash_varlen" @@ -219,16 +216,10 @@ def run_attention( ) if config.n_groups != 1 and not requires_grad: - if has_block: - out = out.view(bsz, q_len, config.n_kv_heads, config.n_groups, head_dim) - else: - out = out.view(bsz, q_len, config.n_kv_heads, config.n_groups, head_dim) + out = out.view(bsz, q_len, config.n_kv_heads, config.n_groups, head_dim) out = out.reshape(bsz, q_len, n_heads, head_dim) else: - if has_block: - out = out.view(bsz, q_len, n_heads, head_dim) - else: - out = out.view(bsz, q_len, n_heads, head_dim) + out = out.view(bsz, q_len, n_heads, head_dim) return out else: local_mask = context.attention_mask 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(