unsloth/unsloth.egg-info/PKG-INFO

Metadata-Version: 2.1
Name: unsloth
Version: 2024.5
Summary: 2-5X faster LLM finetuning
Author: Unsloth AI team
Author-email: [email protected]
Maintainer-email: Daniel Han <[email protected]>, Michael Han <[email protected]>
License:                                  Apache License
                                   Version 2.0, January 2004
                                http://www.apache.org/licenses/
        
           TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION
        
           1. Definitions.
        
              "License" shall mean the terms and conditions for use, reproduction,
              and distribution as defined by Sections 1 through 9 of this document.
        
              "Licensor" shall mean the copyright owner or entity authorized by
              the copyright owner that is granting the License.
        
              "Legal Entity" shall mean the union of the acting entity and all
              other entities that control, are controlled by, or are under common
              control with that entity. For the purposes of this definition,
              "control" means (i) the power, direct or indirect, to cause the
              direction or management of such entity, whether by contract or
              otherwise, or (ii) ownership of fifty percent (50%) or more of the
              outstanding shares, or (iii) beneficial ownership of such entity.
        
              "You" (or "Your") shall mean an individual or Legal Entity
              exercising permissions granted by this License.
        
              "Source" form shall mean the preferred form for making modifications,
              including but not limited to software source code, documentation
              source, and configuration files.
        
              "Object" form shall mean any form resulting from mechanical
              transformation or translation of a Source form, including but
              not limited to compiled object code, generated documentation,
              and conversions to other media types.
        
              "Work" shall mean the work of authorship, whether in Source or
              Object form, made available under the License, as indicated by a
              copyright notice that is included in or attached to the work
              (an example is provided in the Appendix below).
        
              "Derivative Works" shall mean any work, whether in Source or Object
              form, that is based on (or derived from) the Work and for which the
              editorial revisions, annotations, elaborations, or other modifications
              represent, as a whole, an original work of authorship. For the purposes
              of this License, Derivative Works shall not include works that remain
              separable from, or merely link (or bind by name) to the interfaces of,
              the Work and Derivative Works thereof.
        
              "Contribution" shall mean any work of authorship, including
              the original version of the Work and any modifications or additions
              to that Work or Derivative Works thereof, that is intentionally
              submitted to Licensor for inclusion in the Work by the copyright owner
              or by an individual or Legal Entity authorized to submit on behalf of
              the copyright owner. For the purposes of this definition, "submitted"
              means any form of electronic, verbal, or written communication sent
              to the Licensor or its representatives, including but not limited to
              communication on electronic mailing lists, source code control systems,
              and issue tracking systems that are managed by, or on behalf of, the
              Licensor for the purpose of discussing and improving the Work, but
              excluding communication that is conspicuously marked or otherwise
              designated in writing by the copyright owner as "Not a Contribution."
        
              "Contributor" shall mean Licensor and any individual or Legal Entity
              on behalf of whom a Contribution has been received by Licensor and
              subsequently incorporated within the Work.
        
           2. Grant of Copyright License. Subject to the terms and conditions of
              this License, each Contributor hereby grants to You a perpetual,
              worldwide, non-exclusive, no-charge, royalty-free, irrevocable
              copyright license to reproduce, prepare Derivative Works of,
              publicly display, publicly perform, sublicense, and distribute the
              Work and such Derivative Works in Source or Object form.
        
           3. Grant of Patent License. Subject to the terms and conditions of
              this License, each Contributor hereby grants to You a perpetual,
              worldwide, non-exclusive, no-charge, royalty-free, irrevocable
              (except as stated in this section) patent license to make, have made,
              use, offer to sell, sell, import, and otherwise transfer the Work,
              where such license applies only to those patent claims licensable
              by such Contributor that are necessarily infringed by their
              Contribution(s) alone or by combination of their Contribution(s)
              with the Work to which such Contribution(s) was submitted. If You
              institute patent litigation against any entity (including a
              cross-claim or counterclaim in a lawsuit) alleging that the Work
              or a Contribution incorporated within the Work constitutes direct
              or contributory patent infringement, then any patent licenses
              granted to You under this License for that Work shall terminate
              as of the date such litigation is filed.
        
           4. Redistribution. You may reproduce and distribute copies of the
              Work or Derivative Works thereof in any medium, with or without
              modifications, and in Source or Object form, provided that You
              meet the following conditions:
        
              (a) You must give any other recipients of the Work or
                  Derivative Works a copy of this License; and
        
              (b) You must cause any modified files to carry prominent notices
                  stating that You changed the files; and
        
              (c) You must retain, in the Source form of any Derivative Works
                  that You distribute, all copyright, patent, trademark, and
                  attribution notices from the Source form of the Work,
                  excluding those notices that do not pertain to any part of
                  the Derivative Works; and
        
              (d) If the Work includes a "NOTICE" text file as part of its
                  distribution, then any Derivative Works that You distribute must
                  include a readable copy of the attribution notices contained
                  within such NOTICE file, excluding those notices that do not
                  pertain to any part of the Derivative Works, in at least one
                  of the following places: within a NOTICE text file distributed
                  as part of the Derivative Works; within the Source form or
                  documentation, if provided along with the Derivative Works; or,
                  within a display generated by the Derivative Works, if and
                  wherever such third-party notices normally appear. The contents
                  of the NOTICE file are for informational purposes only and
                  do not modify the License. You may add Your own attribution
                  notices within Derivative Works that You distribute, alongside
                  or as an addendum to the NOTICE text from the Work, provided
                  that such additional attribution notices cannot be construed
                  as modifying the License.
        
              You may add Your own copyright statement to Your modifications and
              may provide additional or different license terms and conditions
              for use, reproduction, or distribution of Your modifications, or
              for any such Derivative Works as a whole, provided Your use,
              reproduction, and distribution of the Work otherwise complies with
              the conditions stated in this License.
        
           5. Submission of Contributions. Unless You explicitly state otherwise,
              any Contribution intentionally submitted for inclusion in the Work
              by You to the Licensor shall be under the terms and conditions of
              this License, without any additional terms or conditions.
              Notwithstanding the above, nothing herein shall supersede or modify
              the terms of any separate license agreement you may have executed
              with Licensor regarding such Contributions.
        
           6. Trademarks. This License does not grant permission to use the trade
              names, trademarks, service marks, or product names of the Licensor,
              except as required for reasonable and customary use in describing the
              origin of the Work and reproducing the content of the NOTICE file.
        
           7. Disclaimer of Warranty. Unless required by applicable law or
              agreed to in writing, Licensor provides the Work (and each
              Contributor provides its Contributions) on an "AS IS" BASIS,
              WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or
              implied, including, without limitation, any warranties or conditions
              of TITLE, NON-INFRINGEMENT, MERCHANTABILITY, or FITNESS FOR A
              PARTICULAR PURPOSE. You are solely responsible for determining the
              appropriateness of using or redistributing the Work and assume any
              risks associated with Your exercise of permissions under this License.
        
           8. Limitation of Liability. In no event and under no legal theory,
              whether in tort (including negligence), contract, or otherwise,
              unless required by applicable law (such as deliberate and grossly
              negligent acts) or agreed to in writing, shall any Contributor be
              liable to You for damages, including any direct, indirect, special,
              incidental, or consequential damages of any character arising as a
              result of this License or out of the use or inability to use the
              Work (including but not limited to damages for loss of goodwill,
              work stoppage, computer failure or malfunction, or any and all
              other commercial damages or losses), even if such Contributor
              has been advised of the possibility of such damages.
        
           9. Accepting Warranty or Additional Liability. While redistributing
              the Work or Derivative Works thereof, You may choose to offer,
              and charge a fee for, acceptance of support, warranty, indemnity,
              or other liability obligations and/or rights consistent with this
              License. However, in accepting such obligations, You may act only
              on Your own behalf and on Your sole responsibility, not on behalf
              of any other Contributor, and only if You agree to indemnify,
              defend, and hold each Contributor harmless for any liability
              incurred by, or claims asserted against, such Contributor by reason
              of your accepting any such warranty or additional liability.
        
           END OF TERMS AND CONDITIONS
        
           APPENDIX: How to apply the Apache License to your work.
        
              To apply the Apache License to your work, attach the following
              boilerplate notice, with the fields enclosed by brackets "[]"
              replaced with your own identifying information. (Don't include
              the brackets!)  The text should be enclosed in the appropriate
              comment syntax for the file format. We also recommend that a
              file or class name and description of purpose be included on the
              same "printed page" as the copyright notice for easier
              identification within third-party archives.
        
           Copyright [2024-] [Unsloth AI, Daniel Han-Chen & Michael Han-Chen]
        
           Licensed under the Apache License, Version 2.0 (the "License");
           you may not use this file except in compliance with the License.
           You may obtain a copy of the License at
        
               http://www.apache.org/licenses/LICENSE-2.0
        
           Unless required by applicable law or agreed to in writing, software
           distributed under the License is distributed on an "AS IS" BASIS,
           WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
           See the License for the specific language governing permissions and
           limitations under the License.
        
Project-URL: homepage, http://www.unsloth.ai
Project-URL: documentation, https://github.com/unslothai/unsloth
Project-URL: repository, https://github.com/unslothai/unsloth
Keywords: ai,llm
Classifier: Programming Language :: Python
Requires-Python: >=3.9
Description-Content-Type: text/markdown
License-File: LICENSE
Provides-Extra: huggingface
Requires-Dist: tyro; extra == "huggingface"
Requires-Dist: transformers>=4.38.2; extra == "huggingface"
Requires-Dist: datasets>=2.16.0; extra == "huggingface"
Requires-Dist: sentencepiece>=0.2.0; extra == "huggingface"
Requires-Dist: tqdm; extra == "huggingface"
Requires-Dist: psutil; extra == "huggingface"
Requires-Dist: wheel>=0.42.0; extra == "huggingface"
Requires-Dist: numpy; extra == "huggingface"
Requires-Dist: accelerate>=0.26.1; extra == "huggingface"
Requires-Dist: trl>=0.7.9; extra == "huggingface"
Requires-Dist: peft>=0.7.1; extra == "huggingface"
Requires-Dist: protobuf<4.0.0; extra == "huggingface"
Provides-Extra: cu118only
Requires-Dist: xformers@ https://download.pytorch.org/whl/cu118/xformers-0.0.22.post7%2Bcu118-cp39-cp39-manylinux2014_x86_64.whl ; python_version == "3.9" and extra == "cu118only"
Requires-Dist: xformers@ https://download.pytorch.org/whl/cu118/xformers-0.0.22.post7%2Bcu118-cp310-cp310-manylinux2014_x86_64.whl ; python_version == "3.10" and extra == "cu118only"
Requires-Dist: xformers@ https://download.pytorch.org/whl/cu118/xformers-0.0.22.post7%2Bcu118-cp311-cp311-manylinux2014_x86_64.whl ; python_version == "3.11" and extra == "cu118only"
Provides-Extra: cu121only
Requires-Dist: xformers@ https://download.pytorch.org/whl/cu121/xformers-0.0.22.post7-cp39-cp39-manylinux2014_x86_64.whl ; python_version == "3.9" and extra == "cu121only"
Requires-Dist: xformers@ https://download.pytorch.org/whl/cu121/xformers-0.0.22.post7-cp310-cp310-manylinux2014_x86_64.whl ; python_version == "3.10" and extra == "cu121only"
Requires-Dist: xformers@ https://download.pytorch.org/whl/cu121/xformers-0.0.22.post7-cp311-cp311-manylinux2014_x86_64.whl ; python_version == "3.11" and extra == "cu121only"
Provides-Extra: cu118onlytorch211
Requires-Dist: xformers@ https://download.pytorch.org/whl/cu118/xformers-0.0.23%2Bcu118-cp39-cp39-manylinux2014_x86_64.whl ; python_version == "3.9" and extra == "cu118onlytorch211"
Requires-Dist: xformers@ https://download.pytorch.org/whl/cu118/xformers-0.0.23%2Bcu118-cp310-cp310-manylinux2014_x86_64.whl ; python_version == "3.10" and extra == "cu118onlytorch211"
Requires-Dist: xformers@ https://download.pytorch.org/whl/cu118/xformers-0.0.23%2Bcu118-cp311-cp311-manylinux2014_x86_64.whl ; python_version == "3.11" and extra == "cu118onlytorch211"
Provides-Extra: cu121onlytorch211
Requires-Dist: xformers@ https://download.pytorch.org/whl/cu121/xformers-0.0.23-cp39-cp39-manylinux2014_x86_64.whl ; python_version == "3.9" and extra == "cu121onlytorch211"
Requires-Dist: xformers@ https://download.pytorch.org/whl/cu121/xformers-0.0.23-cp310-cp310-manylinux2014_x86_64.whl ; python_version == "3.10" and extra == "cu121onlytorch211"
Requires-Dist: xformers@ https://download.pytorch.org/whl/cu121/xformers-0.0.23-cp311-cp311-manylinux2014_x86_64.whl ; python_version == "3.11" and extra == "cu121onlytorch211"
Provides-Extra: cu118onlytorch212
Requires-Dist: xformers@ https://download.pytorch.org/whl/cu118/xformers-0.0.23.post1%2Bcu118-cp39-cp39-manylinux2014_x86_64.whl ; python_version == "3.9" and extra == "cu118onlytorch212"
Requires-Dist: xformers@ https://download.pytorch.org/whl/cu118/xformers-0.0.23.post1%2Bcu118-cp310-cp310-manylinux2014_x86_64.whl ; python_version == "3.10" and extra == "cu118onlytorch212"
Requires-Dist: xformers@ https://download.pytorch.org/whl/cu118/xformers-0.0.23.post1%2Bcu118-cp311-cp311-manylinux2014_x86_64.whl ; python_version == "3.11" and extra == "cu118onlytorch212"
Provides-Extra: cu121onlytorch212
Requires-Dist: xformers@ https://download.pytorch.org/whl/cu121/xformers-0.0.23.post1-cp39-cp39-manylinux2014_x86_64.whl ; python_version == "3.9" and extra == "cu121onlytorch212"
Requires-Dist: xformers@ https://download.pytorch.org/whl/cu121/xformers-0.0.23.post1-cp310-cp310-manylinux2014_x86_64.whl ; python_version == "3.10" and extra == "cu121onlytorch212"
Requires-Dist: xformers@ https://download.pytorch.org/whl/cu121/xformers-0.0.23.post1-cp311-cp311-manylinux2014_x86_64.whl ; python_version == "3.11" and extra == "cu121onlytorch212"
Provides-Extra: cu118onlytorch220
Requires-Dist: xformers@ https://download.pytorch.org/whl/cu118/xformers-0.0.24%2Bcu118-cp39-cp39-manylinux2014_x86_64.whl ; python_version == "3.9" and extra == "cu118onlytorch220"
Requires-Dist: xformers@ https://download.pytorch.org/whl/cu118/xformers-0.0.24%2Bcu118-cp310-cp310-manylinux2014_x86_64.whl ; python_version == "3.10" and extra == "cu118onlytorch220"
Requires-Dist: xformers@ https://download.pytorch.org/whl/cu118/xformers-0.0.24%2Bcu118-cp311-cp311-manylinux2014_x86_64.whl ; python_version == "3.11" and extra == "cu118onlytorch220"
Provides-Extra: cu121onlytorch220
Requires-Dist: xformers@ https://download.pytorch.org/whl/cu121/xformers-0.0.24-cp39-cp39-manylinux2014_x86_64.whl ; python_version == "3.9" and extra == "cu121onlytorch220"
Requires-Dist: xformers@ https://download.pytorch.org/whl/cu121/xformers-0.0.24-cp310-cp310-manylinux2014_x86_64.whl ; python_version == "3.10" and extra == "cu121onlytorch220"
Requires-Dist: xformers@ https://download.pytorch.org/whl/cu121/xformers-0.0.24-cp311-cp311-manylinux2014_x86_64.whl ; python_version == "3.11" and extra == "cu121onlytorch220"
Provides-Extra: cu118onlytorch230
Requires-Dist: xformers@ https://download.pytorch.org/whl/cu118/xformers-0.0.26.post1%2Bcu118-cp39-cp39-manylinux2014_x86_64.whl ; python_version == "3.9" and extra == "cu118onlytorch230"
Requires-Dist: xformers@ https://download.pytorch.org/whl/cu118/xformers-0.0.26.post1%2Bcu118-cp310-cp310-manylinux2014_x86_64.whl ; python_version == "3.10" and extra == "cu118onlytorch230"
Requires-Dist: xformers@ https://download.pytorch.org/whl/cu118/xformers-0.0.26.post1%2Bcu118-cp311-cp311-manylinux2014_x86_64.whl ; python_version == "3.11" and extra == "cu118onlytorch230"
Provides-Extra: cu121onlytorch230
Requires-Dist: xformers@ https://download.pytorch.org/whl/cu121/xformers-0.0.26.post1-cp39-cp39-manylinux2014_x86_64.whl ; python_version == "3.9" and extra == "cu121onlytorch230"
Requires-Dist: xformers@ https://download.pytorch.org/whl/cu121/xformers-0.0.26.post1-cp310-cp310-manylinux2014_x86_64.whl ; python_version == "3.10" and extra == "cu121onlytorch230"
Requires-Dist: xformers@ https://download.pytorch.org/whl/cu121/xformers-0.0.26.post1-cp311-cp311-manylinux2014_x86_64.whl ; python_version == "3.11" and extra == "cu121onlytorch230"
Provides-Extra: cu118
Requires-Dist: unsloth[huggingface]; extra == "cu118"
Requires-Dist: bitsandbytes; extra == "cu118"
Requires-Dist: unsloth[cu118only]; extra == "cu118"
Provides-Extra: cu121
Requires-Dist: unsloth[huggingface]; extra == "cu121"
Requires-Dist: bitsandbytes; extra == "cu121"
Requires-Dist: unsloth[cu121only]; extra == "cu121"
Provides-Extra: cu118-torch211
Requires-Dist: unsloth[huggingface]; extra == "cu118-torch211"
Requires-Dist: bitsandbytes; extra == "cu118-torch211"
Requires-Dist: unsloth[cu118onlytorch211]; extra == "cu118-torch211"
Provides-Extra: cu121-torch211
Requires-Dist: unsloth[huggingface]; extra == "cu121-torch211"
Requires-Dist: bitsandbytes; extra == "cu121-torch211"
Requires-Dist: unsloth[cu121onlytorch211]; extra == "cu121-torch211"
Provides-Extra: cu118-torch212
Requires-Dist: unsloth[huggingface]; extra == "cu118-torch212"
Requires-Dist: bitsandbytes; extra == "cu118-torch212"
Requires-Dist: unsloth[cu118onlytorch212]; extra == "cu118-torch212"
Provides-Extra: cu121-torch212
Requires-Dist: unsloth[huggingface]; extra == "cu121-torch212"
Requires-Dist: bitsandbytes; extra == "cu121-torch212"
Requires-Dist: unsloth[cu121onlytorch212]; extra == "cu121-torch212"
Provides-Extra: cu118-torch220
Requires-Dist: unsloth[huggingface]; extra == "cu118-torch220"
Requires-Dist: bitsandbytes; extra == "cu118-torch220"
Requires-Dist: unsloth[cu118onlytorch220]; extra == "cu118-torch220"
Provides-Extra: cu121-torch220
Requires-Dist: unsloth[huggingface]; extra == "cu121-torch220"
Requires-Dist: bitsandbytes; extra == "cu121-torch220"
Requires-Dist: unsloth[cu121onlytorch220]; extra == "cu121-torch220"
Provides-Extra: cu118-torch230
Requires-Dist: unsloth[huggingface]; extra == "cu118-torch230"
Requires-Dist: bitsandbytes; extra == "cu118-torch230"
Requires-Dist: unsloth[cu118onlytorch230]; extra == "cu118-torch230"
Provides-Extra: cu121-torch230
Requires-Dist: unsloth[huggingface]; extra == "cu121-torch230"
Requires-Dist: bitsandbytes; extra == "cu121-torch230"
Requires-Dist: unsloth[cu121onlytorch230]; extra == "cu121-torch230"
Provides-Extra: kaggle
Requires-Dist: unsloth[huggingface]; extra == "kaggle"
Provides-Extra: kaggle-new
Requires-Dist: unsloth[huggingface]; extra == "kaggle-new"
Requires-Dist: bitsandbytes; extra == "kaggle-new"
Provides-Extra: conda
Requires-Dist: unsloth[huggingface]; extra == "conda"
Provides-Extra: colab-torch211
Requires-Dist: unsloth[huggingface]; extra == "colab-torch211"
Requires-Dist: bitsandbytes; extra == "colab-torch211"
Requires-Dist: unsloth[cu121onlytorch211]; extra == "colab-torch211"
Provides-Extra: colab-ampere-torch211
Requires-Dist: unsloth[huggingface]; extra == "colab-ampere-torch211"
Requires-Dist: bitsandbytes; extra == "colab-ampere-torch211"
Requires-Dist: unsloth[cu121onlytorch211]; extra == "colab-ampere-torch211"
Requires-Dist: packaging; extra == "colab-ampere-torch211"
Requires-Dist: ninja; extra == "colab-ampere-torch211"
Requires-Dist: flash-attn; extra == "colab-ampere-torch211"
Provides-Extra: colab-torch220
Requires-Dist: unsloth[huggingface]; extra == "colab-torch220"
Requires-Dist: bitsandbytes; extra == "colab-torch220"
Requires-Dist: unsloth[cu121onlytorch220]; extra == "colab-torch220"
Provides-Extra: colab-ampere-torch220
Requires-Dist: unsloth[huggingface]; extra == "colab-ampere-torch220"
Requires-Dist: bitsandbytes; extra == "colab-ampere-torch220"
Requires-Dist: unsloth[cu121onlytorch220]; extra == "colab-ampere-torch220"
Requires-Dist: packaging; extra == "colab-ampere-torch220"
Requires-Dist: ninja; extra == "colab-ampere-torch220"
Requires-Dist: flash-attn; extra == "colab-ampere-torch220"
Provides-Extra: colab-new
Requires-Dist: tyro; extra == "colab-new"
Requires-Dist: transformers>=4.38.2; extra == "colab-new"
Requires-Dist: datasets>=2.16.0; extra == "colab-new"
Requires-Dist: sentencepiece; extra == "colab-new"
Requires-Dist: tqdm; extra == "colab-new"
Requires-Dist: psutil; extra == "colab-new"
Requires-Dist: wheel>=0.42.0; extra == "colab-new"
Requires-Dist: numpy; extra == "colab-new"
Requires-Dist: protobuf<4.0.0; extra == "colab-new"
Provides-Extra: colab-no-deps
Requires-Dist: accelerate>=0.26.1; extra == "colab-no-deps"
Requires-Dist: trl>=0.7.9; extra == "colab-no-deps"
Requires-Dist: peft>=0.7.1; extra == "colab-no-deps"
Requires-Dist: xformers; extra == "colab-no-deps"
Requires-Dist: bitsandbytes; extra == "colab-no-deps"
Requires-Dist: protobuf<4.0.0; extra == "colab-no-deps"
Provides-Extra: colab
Requires-Dist: unsloth[cu121]; extra == "colab"
Provides-Extra: colab-ampere
Requires-Dist: unsloth[colab-ampere-torch220]; extra == "colab-ampere"
Requires-Dist: packaging; extra == "colab-ampere"
Requires-Dist: ninja; extra == "colab-ampere"
Requires-Dist: flash-attn; extra == "colab-ampere"
Provides-Extra: cu118-ampere
Requires-Dist: unsloth[huggingface]; extra == "cu118-ampere"
Requires-Dist: bitsandbytes; extra == "cu118-ampere"
Requires-Dist: unsloth[cu118only]; extra == "cu118-ampere"
Requires-Dist: packaging; extra == "cu118-ampere"
Requires-Dist: ninja; extra == "cu118-ampere"
Requires-Dist: flash-attn; extra == "cu118-ampere"
Provides-Extra: cu121-ampere
Requires-Dist: unsloth[huggingface]; extra == "cu121-ampere"
Requires-Dist: bitsandbytes; extra == "cu121-ampere"
Requires-Dist: unsloth[cu121only]; extra == "cu121-ampere"
Requires-Dist: packaging; extra == "cu121-ampere"
Requires-Dist: ninja; extra == "cu121-ampere"
Requires-Dist: flash-attn; extra == "cu121-ampere"
Provides-Extra: cu118-ampere-torch211
Requires-Dist: unsloth[huggingface]; extra == "cu118-ampere-torch211"
Requires-Dist: bitsandbytes; extra == "cu118-ampere-torch211"
Requires-Dist: unsloth[cu118onlytorch211]; extra == "cu118-ampere-torch211"
Requires-Dist: packaging; extra == "cu118-ampere-torch211"
Requires-Dist: ninja; extra == "cu118-ampere-torch211"
Requires-Dist: flash-attn; extra == "cu118-ampere-torch211"
Provides-Extra: cu121-ampere-torch211
Requires-Dist: unsloth[huggingface]; extra == "cu121-ampere-torch211"
Requires-Dist: bitsandbytes; extra == "cu121-ampere-torch211"
Requires-Dist: unsloth[cu121onlytorch211]; extra == "cu121-ampere-torch211"
Requires-Dist: packaging; extra == "cu121-ampere-torch211"
Requires-Dist: ninja; extra == "cu121-ampere-torch211"
Requires-Dist: flash-attn; extra == "cu121-ampere-torch211"
Provides-Extra: cu118-ampere-torch220
Requires-Dist: unsloth[huggingface]; extra == "cu118-ampere-torch220"
Requires-Dist: bitsandbytes; extra == "cu118-ampere-torch220"
Requires-Dist: unsloth[cu118onlytorch220]; extra == "cu118-ampere-torch220"
Requires-Dist: packaging; extra == "cu118-ampere-torch220"
Requires-Dist: ninja; extra == "cu118-ampere-torch220"
Requires-Dist: flash-attn; extra == "cu118-ampere-torch220"
Provides-Extra: cu121-ampere-torch220
Requires-Dist: unsloth[huggingface]; extra == "cu121-ampere-torch220"
Requires-Dist: bitsandbytes; extra == "cu121-ampere-torch220"
Requires-Dist: unsloth[cu121onlytorch220]; extra == "cu121-ampere-torch220"
Requires-Dist: packaging; extra == "cu121-ampere-torch220"
Requires-Dist: ninja; extra == "cu121-ampere-torch220"
Requires-Dist: flash-attn; extra == "cu121-ampere-torch220"
Provides-Extra: cu118-ampere-torch230
Requires-Dist: unsloth[huggingface]; extra == "cu118-ampere-torch230"
Requires-Dist: bitsandbytes; extra == "cu118-ampere-torch230"
Requires-Dist: unsloth[cu118onlytorch230]; extra == "cu118-ampere-torch230"
Requires-Dist: packaging; extra == "cu118-ampere-torch230"
Requires-Dist: ninja; extra == "cu118-ampere-torch230"
Requires-Dist: flash-attn; extra == "cu118-ampere-torch230"
Provides-Extra: cu121-ampere-torch230
Requires-Dist: unsloth[huggingface]; extra == "cu121-ampere-torch230"
Requires-Dist: bitsandbytes; extra == "cu121-ampere-torch230"
Requires-Dist: unsloth[cu121onlytorch230]; extra == "cu121-ampere-torch230"
Requires-Dist: packaging; extra == "cu121-ampere-torch230"
Requires-Dist: ninja; extra == "cu121-ampere-torch230"
Requires-Dist: flash-attn; extra == "cu121-ampere-torch230"

<div align="center">

  <a href="https://unsloth.ai"><picture>
    <source media="(prefers-color-scheme: dark)" srcset="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20logo%20white%20text.png">
    <source media="(prefers-color-scheme: light)" srcset="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20logo%20black%20text.png">
    <img alt="unsloth logo" src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20logo%20black%20text.png" height="110" style="max-width: 100%;">
  </picture></a>
  
<a href="https://colab.research.google.com/drive/135ced7oHytdxu3N2DNe1Z0kqjyYIkDXp?usp=sharing"><img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/start free finetune button.png" height="48"></a>
<a href="https://discord.gg/u54VK8m8tk"><img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/Discord button.png" height="48"></a>
<a href="https://ko-fi.com/unsloth"><img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/buy me a coffee button.png" height="48"></a>

### Finetune Llama 3, Mistral & Gemma 2-5x faster with 80% less memory!

![](https://i.ibb.co/sJ7RhGG/image-41.png)

</div>

## ✨ Finetune for Free

All notebooks are **beginner friendly**! Add your dataset, click "Run All", and you'll get a 2x faster finetuned model which can be exported to GGUF, vLLM or uploaded to Hugging Face.

| Unsloth supports | Free Notebooks | Performance | Memory use |
|-----------|---------|--------|----------|
| **Llama 3 (8B)**      | [▶️ Start for free](https://colab.research.google.com/drive/135ced7oHytdxu3N2DNe1Z0kqjyYIkDXp?usp=sharing)               | 2x faster | 60% less |
| **Mistral (7B)**    | [▶️ Start for free](https://colab.research.google.com/drive/1Dyauq4kTZoLewQ1cApceUQVNcnnNTzg_?usp=sharing)               | 2.2x faster | 73% less |
| **Gemma (7B)**      | [▶️ Start for free](https://colab.research.google.com/drive/10NbwlsRChbma1v55m8LAPYG15uQv6HLo?usp=sharing)               | 2.4x faster | 71% less |
| **ORPO**     | [▶️ Start for free](https://colab.research.google.com/drive/11t4njE3c4Lxl-07OD8lJSMKkfyJml3Tn?usp=sharing)               | 1.9x faster | 43% less |
| **DPO Zephyr**     | [▶️ Start for free](https://colab.research.google.com/drive/15vttTpzzVXv_tJwEk-hIcQ0S9FcEWvwP?usp=sharing)               | 1.9x faster | 43% less |
| **Phi-3 (3.8B)** | [▶️ Start for free](https://colab.research.google.com/drive/1NvkBmkHfucGO3Ve9s1NKZvMNlw5p83ym?usp=sharing)               | 2x faster | 50% less |
| **TinyLlama**  | [▶️ Start for free](https://colab.research.google.com/drive/1AZghoNBQaMDgWJpi4RbffGM1h6raLUj9?usp=sharing)               | 3.9x faster | 74% less |

- Benchmarking compared to FA2 + Hugging Face combined.
- **Kaggle Notebooks** for [Llama-3 8b](https://www.kaggle.com/code/danielhanchen/kaggle-llama-3-8b-unsloth-notebook), [Gemma 7b](https://www.kaggle.com/code/danielhanchen/kaggle-gemma-7b-unsloth-notebook/), [Mistral 7b](https://www.kaggle.com/code/danielhanchen/kaggle-mistral-7b-unsloth-notebook)
- This [conversational notebook](https://colab.research.google.com/drive/1XamvWYinY6FOSX9GLvnqSjjsNflxdhNc?usp=sharing) is useful for Llama-3. And ChatML for [Mistral 7b](https://colab.research.google.com/drive/1Aau3lgPzeZKQ-98h69CCu1UJcvIBLmy2?usp=sharing).
- This [text completion notebook](https://colab.research.google.com/drive/1ef-tab5bhkvWmBOObepl1WgJvfvSzn5Q?usp=sharing) is for continued pretraining / raw text.

## 🦥 Unsloth.ai News
- 📣 NEW! Qwen1.5-7B, Qwen1.5-14B, Qwen1.5-32B, Qwen1.5-72B now work, courtesy of Firefly's PR [#428](https://github.com/unslothai/unsloth/pull/428)
- 📣 NEW! [Llama-3 8b](https://colab.research.google.com/drive/135ced7oHytdxu3N2DNe1Z0kqjyYIkDXp?usp=sharing) now works! Llama-3 70b also works (change the model name in the notebook).
- 📣 NEW! [ORPO support](https://colab.research.google.com/drive/11t4njE3c4Lxl-07OD8lJSMKkfyJml3Tn?usp=sharing) is here!
- 📣 NEW! [Phi-3 3.8b support](https://colab.research.google.com/drive/1NvkBmkHfucGO3Ve9s1NKZvMNlw5p83ym?usp=sharing) is here!
- 📣 NEW! We cut memory usage by a [further 30%](https://unsloth.ai/blog/long-context) and now support fine-tuning of LLMs with [4x longer context windows](https://unsloth.ai/blog/long-context)! No change required if you're using our notebooks. To enable, simply change 1 line:
```python
model = FastLanguageModel.get_peft_model(
    model,
    use_gradient_checkpointing = "unsloth", # <<<<<<<
)
```
- 📣 [CodeGemma](https://colab.research.google.com/drive/19lwcRk_ZQ_ZtX-qzFP3qZBBHZNcMD1hh?usp=sharing) now works along with [Gemma 7b](https://colab.research.google.com/drive/10NbwlsRChbma1v55m8LAPYG15uQv6HLo?usp=sharing) and [Gemma 2b](https://colab.research.google.com/drive/15gGm7x_jTm017_Ic8e317tdIpDG53Mtu?usp=sharing)
- 📣 [2x faster inference](https://colab.research.google.com/drive/1aqlNQi7MMJbynFDyOQteD2t0yVfjb9Zh?usp=sharing) added for all our models

## 🔗 Links and Resources
| Type                            | Links                               |
| ------------------------------- | --------------------------------------- |
| 📚 **Wiki & FAQ**              | [Read Our Wiki](https://github.com/unslothai/unsloth/wiki) |
| <img height="14" src="https://upload.wikimedia.org/wikipedia/commons/6/6f/Logo_of_Twitter.svg" />&nbsp; **Twitter (aka X)**              |  [Follow us on X](https://twitter.com/unslothai)|
| 📜 **Documentation**              | [Read The Doc](https://github.com/unslothai/unsloth/tree/main#-documentation) |
| 💾 **Installation**               | [unsloth/README.md](https://github.com/unslothai/unsloth/tree/main#installation-instructions)|
| 🥇 **Benchmarking**                   | [Performance Tables](https://github.com/unslothai/unsloth/tree/main#-performance-benchmarking)
| 🌐 **Released Models**            | [Unsloth Releases](https://huggingface.co/unsloth)|
| ✍️ **Blog**                    | [Read our Blogs](https://unsloth.ai/blog)|

## ⭐ Key Features
- All kernels written in [OpenAI's Triton](https://openai.com/research/triton) language. **Manual backprop engine**.
- **0% loss in accuracy** - no approximation methods - all exact.
- No change of hardware. Supports NVIDIA GPUs since 2018+. Minimum CUDA Capability 7.0 (V100, T4, Titan V, RTX 20, 30, 40x, A100, H100, L40 etc) [Check your GPU!](https://developer.nvidia.com/cuda-gpus) GTX 1070, 1080 works, but is slow.
- Works on **Linux** and **Windows** via WSL.
- Supports 4bit and 16bit QLoRA / LoRA finetuning via [bitsandbytes](https://github.com/TimDettmers/bitsandbytes).
- Open source trains 5x faster - see [Unsloth Pro](https://unsloth.ai/) for up to **30x faster training**!
- If you trained a model with 🦥Unsloth, you can use this cool sticker! &nbsp; <img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/made with unsloth.png" height="50" align="center" />


## 🥇 Performance Benchmarking
- For the full list of **reproducable** benchmarking tables, [go to our website](https://unsloth.ai/blog/mistral-benchmark#Benchmark%20tables)

| 1 A100 40GB  | 🤗Hugging Face | Flash Attention | 🦥Unsloth Open Source | 🦥[Unsloth Pro](https://unsloth.ai/pricing) |
|--------------|--------------|-----------------|---------------------|-----------------|
| Alpaca       | 1x           | 1.04x           | 1.98x               | **15.64x**      |
| LAION Chip2  | 1x           | 0.92x           | 1.61x               | **20.73x**      |
| OASST        | 1x           | 1.19x           | 2.17x               | **14.83x**      |
| Slim Orca    | 1x           | 1.18x           | 2.22x               | **14.82x**      |

- Benchmarking table below was conducted by [🤗Hugging Face](https://huggingface.co/blog/unsloth-trl).

| Free Colab T4 | Dataset | 🤗Hugging Face | Pytorch 2.1.1 | 🦥Unsloth | 🦥 VRAM reduction |
| --- | --- | --- | --- | --- | --- |
| Llama-2 7b | OASST | 1x | 1.19x | 1.95x | -43.3% |
| Mistral 7b | Alpaca | 1x | 1.07x | 1.56x | -13.7% |
| Tiny Llama 1.1b | Alpaca | 1x | 2.06x | 3.87x | -73.8% |
| DPO with Zephyr | Ultra Chat | 1x | 1.09x | 1.55x | -18.6% |

![](https://i.ibb.co/sJ7RhGG/image-41.png)

## 💾 Installation Instructions
### Conda Installation
Select either `pytorch-cuda=11.8` for CUDA 11.8 or `pytorch-cuda=12.1` for CUDA 12.1. If you have `mamba`, use `mamba` instead of `conda` for faster solving. See this [Github issue](https://github.com/unslothai/unsloth/issues/73) for help on debugging Conda installs.
```bash
conda create --name unsloth_env python=3.10
conda activate unsloth_env

conda install pytorch-cuda=<12.1/11.8> pytorch cudatoolkit xformers -c pytorch -c nvidia -c xformers

pip install "unsloth[colab-new] @ git+https://github.com/unslothai/unsloth.git"

pip install --no-deps trl peft accelerate bitsandbytes
```

### Pip Installation
Do **NOT** use this if you have Anaconda. You must use the Conda install method, or else stuff will BREAK.

1. Find your CUDA version via
```python
import torch; torch.version.cuda
```
2. For Pytorch 2.1.0: You can update Pytorch via Pip (interchange `cu121` / `cu118`). Go to https://pytorch.org/ to learn more. Select either `cu118` for CUDA 11.8 or `cu121` for CUDA 12.1. If you have a RTX 3060 or higher (A100, H100 etc), use the `"ampere"` path. For Pytorch 2.1.1: go to step 3. For Pytorch 2.2.0: go to step 4.
```bash
pip install --upgrade --force-reinstall --no-cache-dir torch==2.1.0 triton \
  --index-url https://download.pytorch.org/whl/cu121
```
```bash
pip install "unsloth[cu118] @ git+https://github.com/unslothai/unsloth.git"
pip install "unsloth[cu121] @ git+https://github.com/unslothai/unsloth.git"
pip install "unsloth[cu118-ampere] @ git+https://github.com/unslothai/unsloth.git"
pip install "unsloth[cu121-ampere] @ git+https://github.com/unslothai/unsloth.git"
```
3. For Pytorch 2.1.1: Use the `"ampere"` path for newer RTX 30xx GPUs or higher.
```bash
pip install --upgrade --force-reinstall --no-cache-dir torch==2.1.1 triton \
  --index-url https://download.pytorch.org/whl/cu121
```
```bash
pip install "unsloth[cu118-torch211] @ git+https://github.com/unslothai/unsloth.git"
pip install "unsloth[cu121-torch211] @ git+https://github.com/unslothai/unsloth.git"
pip install "unsloth[cu118-ampere-torch211] @ git+https://github.com/unslothai/unsloth.git"
pip install "unsloth[cu121-ampere-torch211] @ git+https://github.com/unslothai/unsloth.git"
```
4. For Pytorch 2.2.0: Use the `"ampere"` path for newer RTX 30xx GPUs or higher.
```bash
pip install --upgrade --force-reinstall --no-cache-dir torch==2.2.0 triton \
  --index-url https://download.pytorch.org/whl/cu121
```
```bash
pip install "unsloth[cu118-torch220] @ git+https://github.com/unslothai/unsloth.git"
pip install "unsloth[cu121-torch220] @ git+https://github.com/unslothai/unsloth.git"
pip install "unsloth[cu118-ampere-torch220] @ git+https://github.com/unslothai/unsloth.git"
pip install "unsloth[cu121-ampere-torch220] @ git+https://github.com/unslothai/unsloth.git"
```
5. If you get errors, try the below first, then go back to step 1:
```bash
pip install --upgrade pip
```
6. For Pytorch 2.2.1:
```bash
# RTX 3090, 4090 Ampere GPUs:
pip install "unsloth[colab-new] @ git+https://github.com/unslothai/unsloth.git"
pip install --no-deps packaging ninja einops flash-attn xformers trl peft accelerate bitsandbytes

# Pre Ampere RTX 2080, T4, GTX 1080 GPUs:
pip install "unsloth[colab-new] @ git+https://github.com/unslothai/unsloth.git"
pip install --no-deps xformers trl peft accelerate bitsandbytes
```
7. For Pytorch 2.3.0: Use the `"ampere"` path for newer RTX 30xx GPUs or higher.
```bash
pip install "unsloth[cu118-torch230] @ git+https://github.com/unslothai/unsloth.git"
pip install "unsloth[cu121-torch230] @ git+https://github.com/unslothai/unsloth.git"
pip install "unsloth[cu118-ampere-torch230] @ git+https://github.com/unslothai/unsloth.git"
pip install "unsloth[cu121-ampere-torch230] @ git+https://github.com/unslothai/unsloth.git"
```
8. To troubleshoot installs try the below (all must succeed). Xformers should mostly all be available.
```bash
nvcc
python -m xformers.info
python -m bitsandbytes
```

## 📜 Documentation
- Go to our [Wiki page](https://github.com/unslothai/unsloth/wiki) for saving to GGUF, checkpointing, evaluation and more!
- We support Huggingface's TRL, Trainer, Seq2SeqTrainer or even Pytorch code!
- We're in 🤗Hugging Face's official docs! Check out the [SFT docs](https://huggingface.co/docs/trl/main/en/sft_trainer#accelerate-fine-tuning-2x-using-unsloth) and [DPO docs](https://huggingface.co/docs/trl/main/en/dpo_trainer#accelerate-dpo-fine-tuning-using-unsloth)!

```python
from unsloth import FastLanguageModel
import torch
from trl import SFTTrainer
from transformers import TrainingArguments
from datasets import load_dataset
max_seq_length = 2048 # Supports RoPE Scaling interally, so choose any!
# Get LAION dataset
url = "https://huggingface.co/datasets/laion/OIG/resolve/main/unified_chip2.jsonl"
dataset = load_dataset("json", data_files = {"train" : url}, split = "train")

# 4bit pre quantized models we support for 4x faster downloading + no OOMs.
fourbit_models = [
    "unsloth/mistral-7b-bnb-4bit",
    "unsloth/mistral-7b-instruct-v0.2-bnb-4bit",
    "unsloth/llama-2-7b-bnb-4bit",
    "unsloth/gemma-7b-bnb-4bit",
    "unsloth/gemma-7b-it-bnb-4bit", # Instruct version of Gemma 7b
    "unsloth/gemma-2b-bnb-4bit",
    "unsloth/gemma-2b-it-bnb-4bit", # Instruct version of Gemma 2b
    "unsloth/llama-3-8b-bnb-4bit", # [NEW] 15 Trillion token Llama-3
    "unsloth/Phi-3-mini-4k-instruct-bnb-4bit",
] # More models at https://huggingface.co/unsloth

model, tokenizer = FastLanguageModel.from_pretrained(
    model_name = "unsloth/llama-3-8b-bnb-4bit",
    max_seq_length = max_seq_length,
    dtype = None,
    load_in_4bit = True,
)

# Do model patching and add fast LoRA weights
model = FastLanguageModel.get_peft_model(
    model,
    r = 16,
    target_modules = ["q_proj", "k_proj", "v_proj", "o_proj",
                      "gate_proj", "up_proj", "down_proj",],
    lora_alpha = 16,
    lora_dropout = 0, # Supports any, but = 0 is optimized
    bias = "none",    # Supports any, but = "none" is optimized
    # [NEW] "unsloth" uses 30% less VRAM, fits 2x larger batch sizes!
    use_gradient_checkpointing = "unsloth", # True or "unsloth" for very long context
    random_state = 3407,
    max_seq_length = max_seq_length,
    use_rslora = False,  # We support rank stabilized LoRA
    loftq_config = None, # And LoftQ
)

trainer = SFTTrainer(
    model = model,
    train_dataset = dataset,
    dataset_text_field = "text",
    max_seq_length = max_seq_length,
    tokenizer = tokenizer,
    args = TrainingArguments(
        per_device_train_batch_size = 2,
        gradient_accumulation_steps = 4,
        warmup_steps = 10,
        max_steps = 60,
        fp16 = not torch.cuda.is_bf16_supported(),
        bf16 = torch.cuda.is_bf16_supported(),
        logging_steps = 1,
        output_dir = "outputs",
        optim = "adamw_8bit",
        seed = 3407,
    ),
)
trainer.train()

# Go to https://github.com/unslothai/unsloth/wiki for advanced tips like
# (1) Saving to GGUF / merging to 16bit for vLLM
# (2) Continued training from a saved LoRA adapter
# (3) Adding an evaluation loop / OOMs
# (4) Cutomized chat templates
```

<a name="DPO"></a>
## DPO Support
DPO (Direct Preference Optimization), PPO, Reward Modelling all seem to work as per 3rd party independent testing from [Llama-Factory](https://github.com/hiyouga/LLaMA-Factory). We have a preliminary Google Colab notebook for reproducing Zephyr on Tesla T4 here: [notebook](https://colab.research.google.com/drive/15vttTpzzVXv_tJwEk-hIcQ0S9FcEWvwP?usp=sharing).

We're in 🤗Hugging Face's official docs! We're on the [SFT docs](https://huggingface.co/docs/trl/main/en/sft_trainer#accelerate-fine-tuning-2x-using-unsloth) and the [DPO docs](https://huggingface.co/docs/trl/main/en/dpo_trainer#accelerate-dpo-fine-tuning-using-unsloth)!

```python
from unsloth import FastLanguageModel, PatchDPOTrainer
PatchDPOTrainer()
import torch
from transformers import TrainingArguments
from trl import DPOTrainer

model, tokenizer = FastLanguageModel.from_pretrained(
    model_name = "unsloth/zephyr-sft-bnb-4bit",
    max_seq_length = max_seq_length,
    dtype = None,
    load_in_4bit = True,
)

# Do model patching and add fast LoRA weights
model = FastLanguageModel.get_peft_model(
    model,
    r = 64,
    target_modules = ["q_proj", "k_proj", "v_proj", "o_proj",
                      "gate_proj", "up_proj", "down_proj",],
    lora_alpha = 64,
    lora_dropout = 0, # Supports any, but = 0 is optimized
    bias = "none",    # Supports any, but = "none" is optimized
    # [NEW] "unsloth" uses 30% less VRAM, fits 2x larger batch sizes!
    use_gradient_checkpointing = "unsloth", # True or "unsloth" for very long context
    random_state = 3407,
    max_seq_length = max_seq_length,
)

dpo_trainer = DPOTrainer(
    model = model,
    ref_model = None,
    args = TrainingArguments(
        per_device_train_batch_size = 4,
        gradient_accumulation_steps = 8,
        warmup_ratio = 0.1,
        num_train_epochs = 3,
        fp16 = not torch.cuda.is_bf16_supported(),
        bf16 = torch.cuda.is_bf16_supported(),
        logging_steps = 1,
        optim = "adamw_8bit",
        seed = 42,
        output_dir = "outputs",
    ),
    beta = 0.1,
    train_dataset = YOUR_DATASET_HERE,
    # eval_dataset = YOUR_DATASET_HERE,
    tokenizer = tokenizer,
    max_length = 1024,
    max_prompt_length = 512,
)
dpo_trainer.train()
```

## 🥇 Detailed Benchmarking Tables
- Click "Code" for fully reproducible examples
- "Unsloth Equal" is a preview of our PRO version, with code stripped out. All settings and the loss curve remains identical.
- For the full list of benchmarking tables, [go to our website](https://unsloth.ai/blog/mistral-benchmark#Benchmark%20tables)
  
| 1 A100 40GB | 🤗Hugging Face | Flash Attention 2 | 🦥Unsloth Open | Unsloth Equal | Unsloth Pro | Unsloth Max |
|--------------|-------------|-------------|-----------------|--------------|---------------|-------------|
| Alpaca       | 1x          | 1.04x       | 1.98x           | 2.48x        | 5.32x         | **15.64x**      |
| code | [Code](https://colab.research.google.com/drive/1u4dBeM-0vGNVmmO6X7cScAut-Hyt4KDF?usp=sharing) |    [Code](https://colab.research.google.com/drive/1fgTOxpMbVjloQBvZyz4lF4BacKSZOB2A?usp=sharing) |    [Code](https://colab.research.google.com/drive/1YIPY_18xm-K0iJDgvNkRoJsgkPMPAO3G?usp=sharing) |    [Code](https://colab.research.google.com/drive/1ANW8EFL3LVyTD7Gq4TkheC1Z7Rxw-rHp?usp=sharing) | | |
| seconds| 1040 | 1001 | 525 | 419 | 196 | 67  |
| memory MB| 18235 | 15365 | 9631 | 8525 | | |
| % saved| | 15.74 | 47.18 | 53.25 | | | |

### Llama-Factory 3rd party benchmarking
- [Link to performance table.](https://github.com/hiyouga/LLaMA-Factory/wiki/Performance-Comparison) TGS: tokens per GPU per second. Model: LLaMA2-7B. GPU: NVIDIA A100 * 1. Batch size: 4. Gradient accumulation: 2. LoRA rank: 8. Max length: 1024.

| Method | Bits | TGS | GRAM | Speed |
| --- | --- | --- | --- | --- |
| HF | 16 | 2392 | 18GB | 100% |
| HF+FA2 | 16 | 2954 | 17GB | 123% |
| Unsloth+FA2 | 16 | 4007 | 16GB | **168%** |
| HF | 4 | 2415 | 9GB | 101% |
| Unsloth+FA2 | 4 | 3726 | 7GB | **160%** |

### Performance comparisons between popular models
<details>
  <summary>Click for specific model benchmarking tables (Mistral 7b, CodeLlama 34b etc.)</summary>
  
### Mistral 7b
| 1 A100 40GB | Hugging Face | Flash Attention 2 | Unsloth Open | Unsloth Equal | Unsloth Pro | Unsloth Max |
|--------------|-------------|-------------|-----------------|--------------|---------------|-------------|
| Mistral 7B Slim Orca  | 1x | 1.15x        | 2.15x        | 2.53x            | 4.61x         | **13.69x**         |
| code | [Code](https://colab.research.google.com/drive/1mePk3KzwTD81hr5mcNcs_AX3Kbg_Ha0x?usp=sharing) | [Code](https://colab.research.google.com/drive/1dgHxjvTmX6hb0bPcLp26RXSE6_n9DKj7?usp=sharing) | [Code](https://colab.research.google.com/drive/1SKrKGV-BZoU4kv5q3g0jtE_OhRgPtrrQ?usp=sharing) | [Code](https://colab.research.google.com/drive/18yOiyX0T81mTwZqOALFSCX_tSAqju6aD?usp=sharing) | |
| seconds      | 1813        | 1571        | 842             | 718          | 393           | 132         |
| memory MB    | 32853       | 19385       | 12465           | 10271        |          |        |
| % saved| | 40.99      | 62.06       | 68.74           |         |          |

### CodeLlama 34b
| 1 A100 40GB | Hugging Face | Flash Attention 2 | Unsloth Open | Unsloth Equal | Unsloth Pro | Unsloth Max |
|--------------|-------------|-------------|-----------------|--------------|---------------|-------------|
| Code Llama 34B   | OOM ❌         | 0.99x        | 1.87x           | 2.61x        | 4.27x      | 12.82x      |
| code | [▶️ Code](https://colab.research.google.com/drive/1ykfz3BqrtC_AUFegCzUQjjfUNlxp6Otc?usp=sharing) | [Code](https://colab.research.google.com/drive/12ZypxQh7OC6kBXvWZI-5d05I4m-B_hoR?usp=sharing) | [Code](https://colab.research.google.com/drive/1gdHyAx8XJsz2yNV-DHvbHjR1iCef5Qmh?usp=sharing) | [Code](https://colab.research.google.com/drive/1fm7wqx9MJ0kRrwKOfmLkK1Rmw-pySahB?usp=sharing) | |
| seconds      | 1953  | 1982  | 1043  | 748   | 458   | 152   |
| memory MB    | 40000 | 33217 | 27413 | 22161 |       | |
| % saved|    | 16.96| 31.47 | 44.60 |       | | |

### 1 Tesla T4

| 1 T4 16GB  | Hugging Face | Flash Attention | Unsloth Open    | Unsloth Pro Equal | Unsloth Pro   | Unsloth Max |
|--------------|-------------|-----------------|-----------------|---------------|---------------|-------------|
| Alpaca       | 1x          | 1.09x           | 1.69x           | 1.79x         | 2.93x          | **8.3x**        |
| code | [▶️ Code](https://colab.research.google.com/drive/1XpLIV4s8Bj5uryB-X2gqM88oRGHEGdaB?usp=sharing) |    [Code](https://colab.research.google.com/drive/1LyXu6CjuymQg6ddHX8g1dpUvrMa1nn4L?usp=sharing) |    [Code](https://colab.research.google.com/drive/1gsv4LpY7C32otl1rgRo5wXTk4HIitXoM?usp=sharing) |    [Code](https://colab.research.google.com/drive/1VtULwRQwhEnVdNryjm27zXfdSM1tNfFK?usp=sharing) | | |
| seconds       | 1599        | 1468        | 942             | 894          | 545           | 193         |
| memory MB       | 7199        | 7059        | 6459            | 5443         |               |             |
| % saved        |         | 1.94        | 10.28           | 24.39        |               | |

### 2 Tesla T4s via DDP

 | 2 T4 DDP | Hugging Face | Flash Attention | Unsloth Open | Unsloth Equal | Unsloth Pro | Unsloth Max |
|--------------|----------|-------------|-----------------|--------------|---------------|-------------|
| Alpaca       | 1x       | 0.99x       | 4.95x           | 4.44x        | 7.28x         | **20.61x**      |
| code | [▶️ Code](https://www.kaggle.com/danielhanchen/hf-original-alpaca-t4-ddp) |   [Code](https://www.kaggle.com/danielhanchen/hf-sdpa-alpaca-t4-ddp) |   [Code](https://www.kaggle.com/danielhanchen/unsloth-alpaca-t4-ddp) | | |
| seconds       | 9882     | 9946        | 1996            | 2227         | 1357          | 480         |
| memory MB| 9176 | 9128 | 6904 | 6782 |  | |
| % saved |     | 0.52 | 24.76 | 26.09 |  | | |
</details>

### Performance comparisons on 1 Tesla T4 GPU:
<details>
  <summary>Click for Time taken for 1 epoch</summary>

One Tesla T4 on Google Colab
`bsz = 2, ga = 4, max_grad_norm = 0.3, num_train_epochs = 1, seed = 3047, lr = 2e-4, wd = 0.01, optim = "adamw_8bit", schedule = "linear", schedule_steps = 10`

| System | GPU | Alpaca (52K) | LAION OIG (210K) | Open Assistant (10K) | SlimOrca (518K) |
| --- | --- | --- | --- | --- | --- |
| Huggingface | 1 T4 | 23h 15m | 56h 28m | 8h 38m | 391h 41m |
| Unsloth Open | 1 T4 | 13h 7m (1.8x) | 31h 47m (1.8x) | 4h 27m (1.9x) | 240h 4m (1.6x) |
| Unsloth Pro | 1 T4 | 3h 6m (7.5x) | 5h 17m (10.7x) | 1h 7m (7.7x) | 59h 53m (6.5x) |
| Unsloth Max | 1 T4 | 2h 39m (8.8x) | 4h 31m (12.5x) | 0h 58m (8.9x) | 51h 30m (7.6x) |

**Peak Memory Usage**

| System | GPU | Alpaca (52K) | LAION OIG (210K) | Open Assistant (10K) | SlimOrca (518K) |
| --- | --- | --- | --- | --- | --- |
| Huggingface | 1 T4 | 7.3GB | 5.9GB | 14.0GB | 13.3GB |
| Unsloth Open | 1 T4 | 6.8GB | 5.7GB | 7.8GB | 7.7GB |
| Unsloth Pro | 1 T4 | 6.4GB | 6.4GB | 6.4GB | 6.4GB |
| Unsloth Max | 1 T4 | 11.4GB | 12.4GB | 11.9GB | 14.4GB |
</details>

<details>
  <summary>Click for Performance Comparisons on 2 Tesla T4 GPUs via DDP:</summary>
**Time taken for 1 epoch**

Two Tesla T4s on Kaggle
`bsz = 2, ga = 4, max_grad_norm = 0.3, num_train_epochs = 1, seed = 3047, lr = 2e-4, wd = 0.01, optim = "adamw_8bit", schedule = "linear", schedule_steps = 10`

| System | GPU | Alpaca (52K) | LAION OIG (210K) | Open Assistant (10K) | SlimOrca (518K) * |
| --- | --- | --- | --- | --- | --- |
| Huggingface | 2 T4 | 84h 47m | 163h 48m | 30h 51m | 1301h 24m * |
| Unsloth Pro | 2 T4 | 3h 20m (25.4x) | 5h 43m (28.7x) | 1h 12m (25.7x) | 71h 40m (18.1x) * |
| Unsloth Max | 2 T4 | 3h 4m (27.6x) | 5h 14m (31.3x) | 1h 6m (28.1x) | 54h 20m (23.9x) * |

**Peak Memory Usage on a Multi GPU System (2 GPUs)**

| System | GPU | Alpaca (52K) | LAION OIG (210K) | Open Assistant (10K) | SlimOrca (518K) * |
| --- | --- | --- | --- | --- | --- |
| Huggingface | 2 T4 | 8.4GB \| 6GB | 7.2GB \| 5.3GB | 14.3GB \| 6.6GB | 10.9GB \| 5.9GB * |
| Unsloth Pro | 2 T4 | 7.7GB \| 4.9GB | 7.5GB \| 4.9GB | 8.5GB \| 4.9GB | 6.2GB \| 4.7GB * |
| Unsloth Max | 2 T4 | 10.5GB \| 5GB | 10.6GB \| 5GB | 10.6GB \| 5GB | 10.5GB \| 5GB * |

* Slim Orca `bsz=1` for all benchmarks since `bsz=2` OOMs. We can handle `bsz=2`, but we benchmark it with `bsz=1` for consistency.
</details>

![](https://i.ibb.co/sJ7RhGG/image-41.png)
<br>

### Thank You to
- [HuyNguyen-hust](https://github.com/HuyNguyen-hust) for making [RoPE Embeddings 28% faster](https://github.com/unslothai/unsloth/pull/238)
- [RandomInternetPreson](https://github.com/RandomInternetPreson) for confirming WSL support
- [152334H](https://github.com/152334H) for experimental DPO support
- [atgctg](https://github.com/atgctg) for syntax highlighting