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408
LICENSE
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408
LICENSE
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Attribution-NonCommercial 4.0 International
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=======================================================================
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Creative Commons Corporation ("Creative Commons") is not a law firm and
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as amended and/or succeeded, as well as other essentially
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equivalent rights anywhere in the world.
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l. You means the individual or entity exercising the Licensed Rights
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Section 2 -- Scope.
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the Licensor hereby grants You a worldwide, royalty-free,
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|
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Exceptions and Limitations apply to Your use, this Public
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Licensor authorizes You to exercise the Licensed Rights in
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all media and formats whether now known or hereafter created,
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|
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|
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|
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|
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|
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|
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|
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|
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|
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extent necessary to allow You to exercise the Licensed
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|
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|
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under any voluntary or waivable statutory or compulsory
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|
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|
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Your exercise of the Licensed Rights is expressly made subject to the
|
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|
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|
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|
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1. If You Share the Licensed Material (including in modified
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form), You must:
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|
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|
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with the Licensed Material:
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|
||||
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|
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Material and any others designated to receive
|
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attribution, in any reasonable manner requested by
|
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the Licensor (including by pseudonym if
|
||||
designated);
|
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|
||||
ii. a copyright notice;
|
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|
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iii. a notice that refers to this Public License;
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|
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iv. a notice that refers to the disclaimer of
|
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|
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|
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extent reasonably practicable;
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|
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|
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retain an indication of any previous modifications; and
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|
||||
c. indicate the Licensed Material is licensed under this
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||||
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|
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reasonable manner based on the medium, means, and context in
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|
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information required by Section 3(a)(1)(A) to the extent
|
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reasonably practicable.
|
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|
||||
4. If You Share Adapted Material You produce, the Adapter's
|
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|
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|
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|
||||
|
||||
Section 4 -- Sui Generis Database Rights.
|
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|
||||
Where the Licensed Rights include Sui Generis Database Rights that
|
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apply to Your use of the Licensed Material:
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|
||||
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|
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|
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|
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only;
|
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|
||||
b. if You include all or a substantial portion of the database
|
||||
contents in a database in which You have Sui Generis Database
|
||||
Rights, then the database in which You have Sui Generis Database
|
||||
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|
||||
|
||||
c. You must comply with the conditions in Section 3(a) if You Share
|
||||
all or a substantial portion of the contents of the database.
|
||||
|
||||
For the avoidance of doubt, this Section 4 supplements and does not
|
||||
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|
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|
||||
|
||||
|
||||
Section 5 -- Disclaimer of Warranties and Limitation of Liability.
|
||||
|
||||
a. UNLESS OTHERWISE SEPARATELY UNDERTAKEN BY THE LICENSOR, TO THE
|
||||
EXTENT POSSIBLE, THE LICENSOR OFFERS THE LICENSED MATERIAL AS-IS
|
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|
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|
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|
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|
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|
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|
||||
KNOWN OR DISCOVERABLE. WHERE DISCLAIMERS OF WARRANTIES ARE NOT
|
||||
ALLOWED IN FULL OR IN PART, THIS DISCLAIMER MAY NOT APPLY TO YOU.
|
||||
|
||||
b. TO THE EXTENT POSSIBLE, IN NO EVENT WILL THE LICENSOR BE LIABLE
|
||||
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|
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|
||||
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|
||||
COSTS, EXPENSES, OR DAMAGES ARISING OUT OF THIS PUBLIC LICENSE OR
|
||||
USE OF THE LICENSED MATERIAL, EVEN IF THE LICENSOR HAS BEEN
|
||||
ADVISED OF THE POSSIBILITY OF SUCH LOSSES, COSTS, EXPENSES, OR
|
||||
DAMAGES. WHERE A LIMITATION OF LIABILITY IS NOT ALLOWED IN FULL OR
|
||||
IN PART, THIS LIMITATION MAY NOT APPLY TO YOU.
|
||||
|
||||
c. The disclaimer of warranties and limitation of liability provided
|
||||
above shall be interpreted in a manner that, to the extent
|
||||
possible, most closely approximates an absolute disclaimer and
|
||||
waiver of all liability.
|
||||
|
||||
|
||||
Section 6 -- Term and Termination.
|
||||
|
||||
a. This Public License applies for the term of the Copyright and
|
||||
Similar Rights licensed here. However, if You fail to comply with
|
||||
this Public License, then Your rights under this Public License
|
||||
terminate automatically.
|
||||
|
||||
b. Where Your right to use the Licensed Material has terminated under
|
||||
Section 6(a), it reinstates:
|
||||
|
||||
1. automatically as of the date the violation is cured, provided
|
||||
it is cured within 30 days of Your discovery of the
|
||||
violation; or
|
||||
|
||||
2. upon express reinstatement by the Licensor.
|
||||
|
||||
For the avoidance of doubt, this Section 6(b) does not affect any
|
||||
right the Licensor may have to seek remedies for Your violations
|
||||
of this Public License.
|
||||
|
||||
c. For the avoidance of doubt, the Licensor may also offer the
|
||||
Licensed Material under separate terms or conditions or stop
|
||||
distributing the Licensed Material at any time; however, doing so
|
||||
will not terminate this Public License.
|
||||
|
||||
d. Sections 1, 5, 6, 7, and 8 survive termination of this Public
|
||||
License.
|
||||
|
||||
|
||||
Section 7 -- Other Terms and Conditions.
|
||||
|
||||
a. The Licensor shall not be bound by any additional or different
|
||||
terms or conditions communicated by You unless expressly agreed.
|
||||
|
||||
b. Any arrangements, understandings, or agreements regarding the
|
||||
Licensed Material not stated herein are separate from and
|
||||
independent of the terms and conditions of this Public License.
|
||||
|
||||
|
||||
Section 8 -- Interpretation.
|
||||
|
||||
a. For the avoidance of doubt, this Public License does not, and
|
||||
shall not be interpreted to, reduce, limit, restrict, or impose
|
||||
conditions on any use of the Licensed Material that could lawfully
|
||||
be made without permission under this Public License.
|
||||
|
||||
b. To the extent possible, if any provision of this Public License is
|
||||
deemed unenforceable, it shall be automatically reformed to the
|
||||
minimum extent necessary to make it enforceable. If the provision
|
||||
cannot be reformed, it shall be severed from this Public License
|
||||
without affecting the enforceability of the remaining terms and
|
||||
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|
||||
|
||||
c. No term or condition of this Public License will be waived and no
|
||||
failure to comply consented to unless expressly agreed to by the
|
||||
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|
||||
|
||||
d. Nothing in this Public License constitutes or may be interpreted
|
||||
as a limitation upon, or waiver of, any privileges and immunities
|
||||
that apply to the Licensor or You, including from the legal
|
||||
processes of any jurisdiction or authority.
|
||||
|
||||
=======================================================================
|
||||
|
||||
Creative Commons is not a party to its public
|
||||
licenses. Notwithstanding, Creative Commons may elect to apply one of
|
||||
its public licenses to material it publishes and in those instances
|
||||
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|
||||
public licenses is dedicated to the public domain under the CC0 Public
|
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Domain Dedication. Except for the limited purpose of indicating that
|
||||
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|
||||
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|
||||
creativecommons.org/policies, Creative Commons does not authorize the
|
||||
use of the trademark "Creative Commons" or any other trademark or logo
|
||||
of Creative Commons without its prior written consent including,
|
||||
without limitation, in connection with any unauthorized modifications
|
||||
to any of its public licenses or any other arrangements,
|
||||
understandings, or agreements concerning use of licensed material. For
|
||||
the avoidance of doubt, this paragraph does not form part of the
|
||||
public licenses.
|
||||
|
||||
Creative Commons may be contacted at creativecommons.org.
|
||||
|
||||
52
Qwen-license
Normal file
52
Qwen-license
Normal file
@@ -0,0 +1,52 @@
|
||||
Qwen LICENSE AGREEMENT
|
||||
|
||||
Qwen LICENSE AGREEMENT Release Date: September 19, 2024
|
||||
|
||||
By clicking to agree or by using or distributing any portion or element of the Qwen Materials, you will be deemed to have recognized and accepted the content of this Agreement, which is effective immediately.
|
||||
|
||||
1. Definitions
|
||||
a. This Qwen LICENSE AGREEMENT (this "Agreement") shall mean the terms and conditions for use, reproduction, distribution and modification of the Materials as defined by this Agreement.
|
||||
b. "We" (or "Us") shall mean Alibaba Cloud.
|
||||
c. "You" (or "Your") shall mean a natural person or legal entity exercising the rights granted by this Agreement and/or using the Materials for any purpose and in any field of use.
|
||||
d. "Third Parties" shall mean individuals or legal entities that are not under common control with us or you.
|
||||
e. "Qwen" shall mean the large language models, and software and algorithms, consisting of trained model weights, parameters (including optimizer states), machine-learning model code, inference-enabling code, training-enabling code, fine-tuning enabling code and other elements of the foregoing distributed by us.
|
||||
f. "Materials" shall mean, collectively, Alibaba Cloud's proprietary Qwen and Documentation (and any portion thereof) made available under this Agreement.
|
||||
g. "Source" form shall mean the preferred form for making modifications, including but not limited to model source code, documentation source, and configuration files.
|
||||
h. "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.
|
||||
|
||||
2. Grant of Rights
|
||||
You are granted a non-exclusive, worldwide, non-transferable and royalty-free limited license under Alibaba Cloud's intellectual property or other rights owned by us embodied in the Materials to use, reproduce, distribute, copy, create derivative works of, and make modifications to the Materials.
|
||||
|
||||
3. Redistribution
|
||||
You may distribute copies or make the Materials, or derivative works thereof, available as part of a product or service that contains any of them, with or without modifications, and in Source or Object form, provided that you meet the following conditions:
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||||
a. You shall give any other recipients of the Materials or derivative works a copy of this Agreement;
|
||||
b. You shall cause any modified files to carry prominent notices stating that you changed the files;
|
||||
c. You shall retain in all copies of the Materials that you distribute the following attribution notices within a "Notice" text file distributed as a part of such copies: "Qwen is licensed under the Qwen LICENSE AGREEMENT, Copyright (c) Alibaba Cloud. All Rights Reserved."; and
|
||||
d. 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 terms and conditions of this Agreement.
|
||||
|
||||
4. Restrictions
|
||||
If you are commercially using the Materials, and your product or service has more than 100 million monthly active users, you shall request a license from us. You cannot exercise your rights under this Agreement without our express authorization.
|
||||
|
||||
5. Rules of use
|
||||
a. The Materials may be subject to export controls or restrictions in China, the United States or other countries or regions. You shall comply with applicable laws and regulations in your use of the Materials.
|
||||
b. If you use the Materials or any outputs or results therefrom to create, train, fine-tune, or improve an AI model that is distributed or made available, you shall prominently display “Built with Qwen” or “Improved using Qwen” in the related product documentation.
|
||||
|
||||
6. Intellectual Property
|
||||
a. We retain ownership of all intellectual property rights in and to the Materials and derivatives made by or for us. Conditioned upon compliance with the terms and conditions of this Agreement, with respect to any derivative works and modifications of the Materials that are made by you, you are and will be the owner of such derivative works and modifications.
|
||||
b. No trademark license is granted to use the trade names, trademarks, service marks, or product names of us, except as required to fulfill notice requirements under this Agreement or as required for reasonable and customary use in describing and redistributing the Materials.
|
||||
c. If you commence a lawsuit or other proceedings (including a cross-claim or counterclaim in a lawsuit) against us or any entity alleging that the Materials or any output therefrom, or any part of the foregoing, infringe any intellectual property or other right owned or licensable by you, then all licenses granted to you under this Agreement shall terminate as of the date such lawsuit or other proceeding is commenced or brought.
|
||||
|
||||
7. Disclaimer of Warranty and Limitation of Liability
|
||||
a. We are not obligated to support, update, provide training for, or develop any further version of the Qwen Materials or to grant any license thereto.
|
||||
b. THE MATERIALS ARE PROVIDED "AS IS" WITHOUT ANY EXPRESS OR IMPLIED WARRANTY OF ANY KIND INCLUDING WARRANTIES OF MERCHANTABILITY, NONINFRINGEMENT, OR FITNESS FOR A PARTICULAR PURPOSE. WE MAKE NO WARRANTY AND ASSUME NO RESPONSIBILITY FOR THE SAFETY OR STABILITY OF THE MATERIALS AND ANY OUTPUT THEREFROM.
|
||||
c. IN NO EVENT SHALL WE BE LIABLE TO YOU FOR ANY DAMAGES, INCLUDING, BUT NOT LIMITED TO ANY DIRECT, OR INDIRECT, SPECIAL OR CONSEQUENTIAL DAMAGES ARISING FROM YOUR USE OR INABILITY TO USE THE MATERIALS OR ANY OUTPUT OF IT, NO MATTER HOW IT’S CAUSED.
|
||||
d. You will defend, indemnify and hold harmless us from and against any claim by any third party arising out of or related to your use or distribution of the Materials.
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|
||||
8. Survival and Termination.
|
||||
a. The term of this Agreement shall commence upon your acceptance of this Agreement or access to the Materials and will continue in full force and effect until terminated in accordance with the terms and conditions herein.
|
||||
b. We may terminate this Agreement if you breach any of the terms or conditions of this Agreement. Upon termination of this Agreement, you must delete and cease use of the Materials. Sections 7 and 9 shall survive the termination of this Agreement.
|
||||
|
||||
9. Governing Law and Jurisdiction.
|
||||
a. This Agreement and any dispute arising out of or relating to it will be governed by the laws of China, without regard to conflict of law principles, and the UN Convention on Contracts for the International Sale of Goods does not apply to this Agreement.
|
||||
b. The People's Courts in Hangzhou City shall have exclusive jurisdiction over any dispute arising out of this Agreement.
|
||||
|
||||
423
README.md
Normal file
423
README.md
Normal file
@@ -0,0 +1,423 @@
|
||||
---
|
||||
license: cc-by-nc-4.0
|
||||
datasets:
|
||||
- HPAI-BSC/Aloe-Beta-General-Collection
|
||||
- HPAI-BSC/chain-of-diagnosis
|
||||
- HPAI-BSC/MedS-Ins
|
||||
- HPAI-BSC/ultramedical
|
||||
- HPAI-BSC/pubmedqa-cot-llama31
|
||||
- HPAI-BSC/medqa-cot-llama31
|
||||
- HPAI-BSC/medmcqa-cot-llama31
|
||||
- HPAI-BSC/headqa-cot-llama31
|
||||
- HPAI-BSC/MMLU-medical-cot-llama31
|
||||
- HPAI-BSC/Polymed-QA
|
||||
- HPAI-BSC/Aloe-Beta-General-Collection
|
||||
- HPAI-BSC/Aloe-Beta-General-Collection
|
||||
language:
|
||||
- en
|
||||
library_name: transformers
|
||||
tags:
|
||||
- biology
|
||||
- medical
|
||||
- healthcare
|
||||
pipeline_tag: question-answering
|
||||
---
|
||||
|
||||
<p align="center">
|
||||
<picture>
|
||||
<source media="(prefers-color-scheme: dark)" srcset="https://cdn-uploads.huggingface.co/production/uploads/6620f941eba5274b5c12f83d/ARcIVTFxuBMV5DKooCgJH.png">
|
||||
<img alt="prompt_engine" src="https://cdn-uploads.huggingface.co/production/uploads/6620f941eba5274b5c12f83d/ARcIVTFxuBMV5DKooCgJH.png" width=55%>
|
||||
</picture>
|
||||
</p>
|
||||
<h1 align="center">
|
||||
Aloe: A Family of Fine-tuned Open Healthcare LLMs
|
||||
</h1>
|
||||
<hr style="margin: 15px">
|
||||
<div align="center" style="line-height: 1;">
|
||||
<a href="https://hpai.bsc.es/" target="_blank" style="margin: 1px;">
|
||||
<img alt="Web" src="https://img.shields.io/badge/Website-HPAI-8A2BE2" style="display: inline-block; vertical-align: middle;"/>
|
||||
</a>
|
||||
<a href="https://huggingface.co/HPAI-BSC" target="_blank" style="margin: 1px;">
|
||||
<img alt="Hugging Face" src="https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-HPAI-ffc107?color=ffc107&logoColor=white" style="display: inline-block; vertical-align: middle;"/>
|
||||
</a>
|
||||
<a href="https://github.com/HPAI-BSC" target="_blank" style="margin: 1px;">
|
||||
<img alt="GitHub" src="https://img.shields.io/badge/GitHub-HPAI-%23121011.svg?logo=github&logoColor=white" style="display: inline-block; vertical-align: middle;"/>
|
||||
</a>
|
||||
</div>
|
||||
<div align="center" style="line-height: 1;">
|
||||
<a href="https://www.linkedin.com/company/hpai" target="_blank" style="margin: 1px;">
|
||||
<img alt="Linkedin" src="https://img.shields.io/badge/Linkedin-HPAI-blue" style="display: inline-block; vertical-align: middle;"/>
|
||||
</a>
|
||||
<a href="https://bsky.app/profile/hpai.bsky.social" target="_blank" style="margin: 1px;">
|
||||
<img alt="BlueSky" src="https://img.shields.io/badge/Bluesky-HPAI-0285FF?logo=bluesky&logoColor=fff" style="display: inline-block; vertical-align: middle;"/>
|
||||
</a>
|
||||
<a href="https://linktr.ee/hpai_bsc" target="_blank" style="margin: 1px;">
|
||||
<img alt="LinkTree" src="https://img.shields.io/badge/Linktree-HPAI-43E55E?style=flat&logo=linktree&logoColor=white" style="display: inline-block; vertical-align: middle;"/>
|
||||
</a>
|
||||
</div>
|
||||
<div align="center" style="line-height: 1;">
|
||||
<a href="https://arxiv.org/abs/2505.04388" target="_blank" style="margin: 1px;">
|
||||
<img alt="Arxiv" src="https://img.shields.io/badge/arXiv-2505.04388-b31b1b.svg" style="display: inline-block; vertical-align: middle;"/>
|
||||
</a>
|
||||
<a href="LICENSE" style="margin: 1px;">
|
||||
<img
|
||||
alt="CC BY-NC 4.0 License"
|
||||
src="https://licensebuttons.net/l/by-nc/4.0/88x31.png"
|
||||
/>
|
||||
</a>
|
||||
</div>
|
||||
|
||||
|
||||
|
||||
|
||||
Qwen2.5-Aloe-Beta-7B is an **open healthcare LLM** achieving **state-of-the-art performance** on several medical tasks. Aloe Beta is made available in four model sizes: [7B](https://huggingface.co/HPAI-BSC/Qwen2.5-Aloe-Beta-7B/), [8B](https://huggingface.co/HPAI-BSC/Llama3.1-Aloe-Beta-8B), [70B](https://huggingface.co/HPAI-BSC/Llama3.1-Aloe-Beta-70B), and [72B](https://huggingface.co/HPAI-BSC/Qwen2.5-Aloe-Beta-72B). All models are trained using the same recipe, on top of two different families of models: Llama3.1 and Qwen2.5.
|
||||
|
||||
Aloe is trained on 20 medical tasks, resulting in a robust and versatile healthcare model. Evaluations show Aloe models to be among the best in their class. When combined with a RAG system ([also released](https://github.com/HPAI-BSC/prompt_engine)) the 7B and 8B version gets close to the performance of closed models like MedPalm-2, GPT4. With the same RAG system, Llama3.1-Aloe-Beta-70B and Qwen2.5-Aloe-Beta-72B outperforms those private alternatives, producing state-of-the-art results.
|
||||
|
||||
# Aloe-Beta-7B
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
**Aloe-Beta** is the latest iteration in the **Aloe family**, building and improving on the success of its predecessor, [Aloe-8B-Alpha](https://huggingface.co/HPAI-BSC/Llama3-Aloe-8B-Alpha).
|
||||
Beta more than triples the training data used by Alpha, for a total of **1.8B tokens**, including a wider variety of medical tasks and instructions (e.g., text summarization, explanation, diagnosis, text classification, treatment recommendation, ...).
|
||||
|
||||
|
||||
|
||||
To mitigate catastrophic forgetting and enable the model to effectively learn new capabilities like **function calling**, we incorporated a diverse set of high-quality general-purpose data constituting 20% of the total training set. The curated data includes some of the highest-quality content available across a range of topics, including mathematics, programming, STEM, and very long instructions (> 8k tokens), to enrich the model's adaptability and comprehension across diverse domains.
|
||||
|
||||
Beta also boosts the alignment and safety stages with respect to Alpha. This includes a [medical preference dataset](https://huggingface.co/datasets/TsinghuaC3I/UltraMedical-Preference), as well as the red-teaming dataset (available soon).
|
||||
|
||||
Complete training details, model merging configurations, and all training data (including synthetically generated data) can be found below. This includes [the RAG system](https://github.com/HPAI-BSC/prompt_engine) that was developed to test Aloe Beta in a deployment setup. Aloe comes with a healthcare-specific risk assessment to facilitate to the safe use and deployment of such systems.
|
||||
|
||||
|
||||
## Model Details
|
||||
|
||||
### [](https://huggingface.co/templates/model-card-example#model-description)Model Description
|
||||
|
||||
- **Developed by:** [HPAI](https://hpai.bsc.es/)
|
||||
- **Model type:** Causal decoder-only transformer language model
|
||||
- **Language(s) (NLP):** English (capable but not formally evaluated on other languages)
|
||||
- **License:** This model is based on [Qwen2.5-7B](https://huggingface.co/Qwen/Qwen2.5-7B) originally released with Apache 2.0 license. All Aloe modifications are available under a CC-BY-NC-4.0 license for non-commercial use, research purposes only.
|
||||
- **Base model :** [Qwen2.5-7B](https://huggingface.co/Qwen/Qwen2.5-7B)
|
||||
- **Paper:** https://arxiv.org/abs/2505.04388
|
||||
- **RAG Repository:** https://github.com/HPAI-BSC/prompt_engine
|
||||
|
||||
### [](https://huggingface.co/templates/model-card-example#model-sources-optional)Model Sources [optional]
|
||||
|
||||
## Model Performance
|
||||
|
||||
Aloe Beta has been tested on the most popular healthcare QA datasets, with and without Medprompt inference technique. Results show competitive performance, achieving SOTA within models of the same size.
|
||||
|
||||
|
||||
|
||||
|
||||
The Beta model has been developed to excel in several different medical tasks. For this reason, we evaluated the model in many different medical tasks:
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
We also compared the performance of the model in the general domain, using the OpenLLM Leaderboard benchmark. Aloe-Beta gets competitive results with the current SOTA general models in the most used general benchmarks and outperforms the medical models:
|
||||
|
||||
|
||||
|
||||
|
||||
## Uses
|
||||
|
||||
### Direct Use
|
||||
|
||||
We encourage the use of Aloe for research purposes, as a stepping stone to build better foundational models for healthcare. In production, Aloe should always be used under the supervision of a human expert.
|
||||
|
||||
### Out-of-Scope Use
|
||||
|
||||
These models are not to be used for clinical practice, medical diagnosis, or any other form of direct or indirect healthcare advice. Models are prone to error and can produce toxic content. The use of Aloe models for activities harmful to individuals, such as spam, fraud, or impersonation, is strictly prohibited. Minors should not be left alone to interact with Aloe without supervision.
|
||||
|
||||
## Bias, Risks, and Limitations
|
||||
|
||||
Aloe can produce toxic content under the appropriate prompts, and it includes multiple undesirable biases. While significant efforts where conducted to mitigate this (see Alignment details below), model safety cannot be fully guaranteed. We avoid the use of all personal data in our training.
|
||||
|
||||
We identify at least three risk cases specific to healthcare LLMs:
|
||||
- Healthcare professional impersonation, a fraudulent behaviour which currently generates billions of dollars in [profit](https://www.justice.gov/opa/pr/justice-department-charges-dozens-12-billion-health-care-fraud). A model such as Aloe could be used to increase the efficacy of such deceiving activities, making them more widespread. The main preventive actions are public literacy on the unreliability of digitised information and the importance of medical registration, and legislation enforcing AI-generated content disclaimers.
|
||||
- Medical decision-making without professional supervision. While this is already an issue in modern societies (eg self-medication) a model such as Aloe, capable of producing high-quality conversational data, can facilitate self-delusion, particularly in the presence of sycophancy. By producing tailored responses, it can also be used to generate actionable answers. Public literacy on the dangers of self-diagnosis is one of the main defenses, together with the introduction of disclaimers and warnings on the models' outputs.
|
||||
- Access to information on dangerous substances or procedures. While the literature on sensitive content can already be found on different sources (eg libraries, the internet, dark web), LLMs can centralize such access, making it nearly impossible to control the flow of such information. Model alignment can help in that regard, but so far the effects remain insufficient, as jailbreaking methods still overcome it.
|
||||
|
||||
|
||||
<!---
|
||||
Table below shows the performance of Aloe at several AI safety tasks:
|
||||
|
||||
TO BE UPDATED
|
||||
|
||||
<img src="https://cdn-uploads.huggingface.co/production/uploads/62972c4979f193515da1d38e/T6Jblpf1kmTkM04K716rM.png" width="95%">
|
||||
|
||||
|
||||
We analyzed the safety and robustness of the model using red teaming techniques. We designed a benchmark using different types of attacks and analyzed the performance of Aloe and some extra models, and we confirm that our model is aligned properly and successfully resisting most attacks:
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
-->
|
||||
|
||||
## How to Get Started with the Model
|
||||
|
||||
Use the code below to get started with the model. You can run conversational inference using the Transformers pipeline abstraction, or by leveraging the Auto classes with the `generate()` function. Let's see examples for both.
|
||||
|
||||
#### Transformers pipeline
|
||||
|
||||
```python
|
||||
import transformers
|
||||
import torch
|
||||
|
||||
model_id = "HPAI-BSC/Qwen2.5-Aloe-Beta-7B"
|
||||
|
||||
pipeline = transformers.pipeline(
|
||||
"text-generation",
|
||||
model=model_id,
|
||||
model_kwargs={"torch_dtype": torch.bfloat16},
|
||||
device_map="auto",
|
||||
)
|
||||
|
||||
messages = [
|
||||
{"role": "system", "content": "You are an expert medical assistant named Aloe, developed by the High Performance Artificial Intelligence Group at Barcelona Supercomputing Center(BSC). You are to be a helpful, respectful, and honest assistant."},
|
||||
{"role": "user", "content": "Hello."},
|
||||
]
|
||||
|
||||
prompt = pipeline.tokenizer.apply_chat_template(
|
||||
messages,
|
||||
tokenize=False,
|
||||
add_generation_prompt=True
|
||||
)
|
||||
|
||||
terminators = [
|
||||
pipeline.tokenizer.eos_token_id,
|
||||
pipeline.tokenizer.convert_tokens_to_ids("<|im_end|>")
|
||||
]
|
||||
|
||||
outputs = pipeline(
|
||||
prompt,
|
||||
max_new_tokens=256,
|
||||
eos_token_id=terminators,
|
||||
do_sample=True,
|
||||
temperature=0.7,
|
||||
top_p=0.8,
|
||||
top_k=20,
|
||||
repetition_penalty=1.05
|
||||
)
|
||||
print(outputs[0]["generated_text"][len(prompt):])
|
||||
```
|
||||
|
||||
#### Transformers AutoModelForCausalLM
|
||||
|
||||
```python
|
||||
from transformers import AutoTokenizer, AutoModelForCausalLM
|
||||
import torch
|
||||
|
||||
model_id = "HPAI-BSC/Qwen2.5-Aloe-Beta-7B"
|
||||
|
||||
tokenizer = AutoTokenizer.from_pretrained(model_id)
|
||||
model = AutoModelForCausalLM.from_pretrained(
|
||||
model_id,
|
||||
torch_dtype=torch.bfloat16,
|
||||
device_map="auto",
|
||||
)
|
||||
|
||||
messages = [
|
||||
{"role": "system", "content": "You are an expert medical assistant named Aloe, developed by the High Performance Artificial Intelligence Group at Barcelona Supercomputing Center(BSC). You are to be a helpful, respectful, and honest assistant."},
|
||||
{"role": "user", "content": "Hello"},
|
||||
]
|
||||
|
||||
input_ids = tokenizer.apply_chat_template(
|
||||
messages,
|
||||
add_generation_prompt=True,
|
||||
return_tensors="pt"
|
||||
).to(model.device)
|
||||
|
||||
terminators = [
|
||||
tokenizer.eos_token_id,
|
||||
tokenizer.convert_tokens_to_ids("<|im_end|>")
|
||||
]
|
||||
|
||||
outputs = model.generate(
|
||||
input_ids,
|
||||
max_new_tokens=256,
|
||||
eos_token_id=terminators,
|
||||
do_sample=True,
|
||||
temperature=0.7,
|
||||
top_p=0.8,
|
||||
top_k=20,
|
||||
repetition_penalty=1.05
|
||||
)
|
||||
response = outputs[0][input_ids.shape[-1]:]
|
||||
print(tokenizer.decode(response, skip_special_tokens=True))
|
||||
```
|
||||
|
||||
## Training Details
|
||||
|
||||
### Supervised fine-tuning
|
||||
SFT on top of Qwen2.5-7B using axolotl (https://github.com/axolotl-ai-cloud/axolotl).
|
||||
|
||||
We used Deepspeed's Zero-3 distributed training using the following hardware:
|
||||
|
||||
* 7B: 32x NVIDIA Hopper H100 64GB of the *Marenostrum 5*.
|
||||
* 8B: 32x NVIDIA Hopper H100 64GB of the *Marenostrum 5*.
|
||||
* 70B: 64x NVIDIA Hopper H100 64GB of the *Marenostrum 5*.
|
||||
* 72B: 92x NVIDIA Hopper H100 64GB of the *Marenostrum 5*.
|
||||
|
||||
|
||||
<!---
|
||||
^^^ TO BE COMPLETED AND DETAILED ^^^
|
||||
-->
|
||||
|
||||
|
||||
|
||||
#### Training Data
|
||||
|
||||
The training set consists of around 1.8B tokens, having 3 different types of data:
|
||||
|
||||
- Medical domain datasets. Includes data from 20 different medical tasks.
|
||||
- [HPAI-BSC/Aloe-Beta-General-Collection](https://huggingface.co/datasets/HPAI-BSC/Aloe-Beta-General-Collection)
|
||||
- [HPAI-BSC/chain-of-diagnosis](https://huggingface.co/datasets/HPAI-BSC/chain-of-diagnosis)
|
||||
- [HPAI-BSC/MedS-Ins](https://huggingface.co/datasets/HPAI-BSC/MedS-Ins)
|
||||
- [HPAI-BSC/ultramedica](https://huggingface.co/datasets/HPAI-BSC/ultramedical)
|
||||
- Synthetic data. We expanded our training data by generating high-quality answers using Llama3.1-70B.
|
||||
- [HPAI-BSC/pubmedqa-cot-llama31](https://huggingface.co/datasets/HPAI-BSC/pubmedqa-cot-llama31)
|
||||
- [HPAI-BSC/medqa-cot-llama31](https://huggingface.co/datasets/HPAI-BSC/medqa-cot-llama31)
|
||||
- [HPAI-BSC/medmcqa-cot-llama31](https://huggingface.co/datasets/HPAI-BSC/medmcqa-cot-llama31)
|
||||
- [HPAI-BSC/headqa-cot-llama31](https://huggingface.co/datasets/HPAI-BSC/headqa-cot-llama31)
|
||||
- [HPAI-BSC/MMLU-medical-cot-llama31](https://huggingface.co/datasets/HPAI-BSC/MMLU-medical-cot-llama31)
|
||||
- [HPAI-BSC/Polymed-QA](https://huggingface.co/datasets/HPAI-BSC/Polymed-QA)
|
||||
- Genstruct data (coming soon)
|
||||
- General data. It includes maths, STEM, code, function calling, and instructions with a very long context.
|
||||
- [HPAI-BSC/Aloe-Beta-General-Collection](https://huggingface.co/datasets/HPAI-BSC/Aloe-Beta-General-Collection)
|
||||
|
||||
#### Training parameters
|
||||
- Epochs: 3
|
||||
- Sequence length: 16384
|
||||
- Optimizer: adamw_torch
|
||||
- Learning rate: 1e-5
|
||||
- Learning rate scheduler: cosine
|
||||
- Warmup steps: 100
|
||||
- Weight decay: 0
|
||||
- Gradient checkpointing
|
||||
- Zero 3
|
||||
- Total batch size: 128
|
||||
- Batch size per device: 1
|
||||
- Gradient accumulation steps: 4
|
||||
|
||||
### Model Merging
|
||||
The model trained was merged with the Qwen2.5-7B-Instruct model using the DARE_TIES technique. [Mergekit](https://github.com/arcee-ai/mergekit) was used to conduct the merging.
|
||||
|
||||
### Model Alignment
|
||||
The model is aligned using the Direct Preference Optimization (DPO) technique through a two-step process:
|
||||
|
||||
1. General DPO Alignment: This step uses a dataset combining medical, general preference, and safety data. We used our dataset [HPAI-BSC/Aloe-Beta-DPO](https://huggingface.co/datasets/HPAI-BSC/Aloe-Beta-DPO). We split the dataset into five parts, and the model was trained iteratively for one epoch on each chunk. We used a learning rate of 2e-7.
|
||||
2. Red-Teaming Alignment: This step further fine-tunes the model to resist a variety of potential attacks, enhancing its robustness and security. Dataset will be shared soon. In this stage, we set the learning rate to 1e-7.
|
||||
|
||||
<!---
|
||||
^^^ LINKS TO DPO DATA (DPO added, missing the RT^^^
|
||||
-->
|
||||
|
||||
|
||||
We used [OpenRLHF](https://github.com/OpenRLHF/OpenRLHF) library. We aligned the model using 16x NVIDA HOOPER H100 64GB of the *Marenostrum 5*. Common hyperparameters:
|
||||
|
||||
- Sequence length: 4096
|
||||
- Optimizer: Fused adam
|
||||
- Total batch size 128
|
||||
- Batch size per device: 1
|
||||
- Gradient accumulation steps: 8
|
||||
- Beta: 0.1
|
||||
|
||||
|
||||
|
||||
## Evaluation
|
||||
|
||||
### Testing Data, Factors & Metrics
|
||||
|
||||
#### Testing Data
|
||||
|
||||
|
||||
- [ACI-BENCH](https://github.com/wyim/aci-bench)
|
||||
- [MTS-Dialog](https://github.com/abachaa/MTS-Dialog)
|
||||
- [MedText](https://huggingface.co/datasets/BI55/MedText)
|
||||
- [Medical Text classification](https://www.kaggle.com/datasets/chaitanyakck/medical-text/data)
|
||||
- [OLAPH](https://github.com/dmis-lab/OLAPH)
|
||||
- CareQA Open
|
||||
- [MedDialog](https://huggingface.co/datasets/bigbio/meddialog)
|
||||
- [MEDIQA QA](https://huggingface.co/datasets/bigbio/mediqa_qa)
|
||||
- [Meddialog Qsumm](https://huggingface.co/datasets/lighteval/med_dialog)
|
||||
- [Biored](https://huggingface.co/datasets/YufeiHFUT/BioRED_all_info)
|
||||
- [MIMIC-III](https://huggingface.co/datasets/dmacres/mimiciii-hospitalcourse-meta)
|
||||
- [Medical Prescription](https://huggingface.co/datasets/devlocalhost/prescription-full)
|
||||
- [MedQA (USMLE)](https://huggingface.co/datasets/bigbio/med_qa)
|
||||
- [MedMCQA](https://huggingface.co/datasets/medmcqa)
|
||||
- [PubMedQA](https://huggingface.co/datasets/bigbio/pubmed_qa)
|
||||
- [MMLU-Medical](https://huggingface.co/datasets/lukaemon/mmlu)
|
||||
- [MedQA-4-Option](https://huggingface.co/datasets/GBaker/MedQA-USMLE-4-options)
|
||||
- [CareQA](https://huggingface.co/datasets/HPAI-BSC/CareQA)
|
||||
- [Open LLM Leaderboard 2](https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard)
|
||||
|
||||
<!---
|
||||
^^^ CAREQA Open link MISSING ^^^
|
||||
-->
|
||||
|
||||
#### Metrics
|
||||
|
||||
- Accuracy: suite the evaluation of multiple-choice question-answering tasks.
|
||||
- Rouge1: refers to the overlap of unigrams between the system and the gold standard.
|
||||
|
||||
|
||||
<!---
|
||||
^^^ MORE METRICS MISSING ^^^
|
||||
-->
|
||||
|
||||
#### Summary
|
||||
|
||||
To compare Aloe with the most competitive open models (both general purpose and healthcare-specific) we use popular healthcare datasets (PubMedQA, MedMCQA, MedQA and MMLU for six medical tasks only), together with the new and highly reliable CareQA. However, while MCQA benchmarks provide valuable insights into a model's ability to handle structured queries, they fall short in representing the full range of challenges faced in medical practice. Building upon this idea, Aloe-Beta represents the next step in the evolution of the Aloe Family, designed to broaden the scope beyond the multiple-choice question-answering tasks that defined Aloe-Alpha.
|
||||
|
||||
|
||||
Benchmark results indicate the training conducted on Aloe has boosted its performance above all other open models within the same model size. Both Qwen2.5-Aloe-Beta-7B and Llama3.1-Aloe-Beta-8B also outperforms other medical models like Llama3-OpenBioLLM and Llama3-Med42. All these results make Aloe-Beta the best healthcare LLM of its size.
|
||||
|
||||
With the help of prompting techniques the performance of Qwen2.5-Aloe-Beta-7B is significantly improved. Medprompting in particular provides a 9% increase in reported accuracy, after which Qwen2.5-Aloe-7B-Beta only lags behind much bigger models like Llama-3.1-70B-Instruct or MedPalm-2. This improvement is mostly consistent across the OpenLLM Leaderboard and the other medical tasks.
|
||||
|
||||
## Environmental Impact
|
||||
|
||||
- **Hardware Type:** 32xH100
|
||||
- **Hours used (8B):** 544 GPU hours
|
||||
- **Hours used (70B):** 4500 GPU hours
|
||||
- **Hardware Provider:** Barcelona Supercomputing Center (BSC)
|
||||
- **Compute Region:** Spain
|
||||
- **Carbon Emitted:** 34.1 kg of CO2
|
||||
|
||||
<!---
|
||||
^^^ ARE CARBON EMISSIONS FOR BOTH? ^^^
|
||||
-->
|
||||
|
||||
|
||||
## Authors
|
||||
Aloe Beta has been developed by the [High Performance Artificial Intelligence](https://hpai.bsc.es/) research group, from the [Barcelona Supercomping Center - BSC](https://www.bsc.es/). Main authors are [Jordi Bayarri Planas](https://huggingface.co/JordiBayarri), [Ashwin Kumar Gururajan](https://huggingface.co/G-AshwinKumar) and [Dario Garcia-Gasulla](https://huggingface.co/dariog). Red teaming efforts lead by Adrian Tormos.
|
||||
|
||||
mailto:hpai@bsc.es
|
||||
|
||||
## Citations
|
||||
|
||||
|
||||
<!---
|
||||
Add the prompt engine paper below
|
||||
-->
|
||||
|
||||
If you use this repository in a published work, please cite the corresponding paper as source:
|
||||
|
||||
```
|
||||
@article{garcia2025aloe,
|
||||
title={The Aloe Family Recipe for Open and Specialized Healthcare LLMs},
|
||||
author={Garcia-Gasulla, Dario and Bayarri-Planas, Jordi and Gururajan, Ashwin Kumar and Lopez-Cuena, Enrique and Tormos, Adrian and Hinjos, Daniel and Bernabeu-Perez, Pablo and Arias-Duart, Anna and Martin-Torres, Pablo Agustin and Gonzalez-Mallo, Marta and others},
|
||||
year={2025},
|
||||
eprint={2505.04388},
|
||||
archivePrefix={arXiv},
|
||||
}
|
||||
```
|
||||
24
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30
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Normal file
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Normal file
@@ -0,0 +1,30 @@
|
||||
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|
||||
"_name_or_path": "/home/jovyan/minio/heka/aloe/models/Aloe-Beta-7B/Qwen2.5-Aloe-Beta-7B-Merged-DPO-RT",
|
||||
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|
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
"num_hidden_layers": 28,
|
||||
"num_key_value_heads": 4,
|
||||
"rms_norm_eps": 1e-06,
|
||||
"rope_scaling": null,
|
||||
"rope_theta": 1000000.0,
|
||||
"sliding_window": null,
|
||||
"tie_word_embeddings": false,
|
||||
"torch_dtype": "bfloat16",
|
||||
"transformers_version": "4.47.0",
|
||||
"use_cache": false,
|
||||
"use_mrope": false,
|
||||
"use_sliding_window": false,
|
||||
"vocab_size": 152064
|
||||
}
|
||||
14
generation_config.json
Normal file
14
generation_config.json
Normal file
@@ -0,0 +1,14 @@
|
||||
{
|
||||
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|
||||
"do_sample": true,
|
||||
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|
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|
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|
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|
||||
"top_k": 20,
|
||||
"top_p": 0.8,
|
||||
"transformers_version": "4.47.0"
|
||||
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|
||||
151388
merges.txt
Normal file
151388
merges.txt
Normal file
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Load Diff
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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|
||||
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|
||||
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|
||||
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|
||||
"model.layers.9.self_attn.v_proj.weight": "model-00002-of-00004.safetensors",
|
||||
"model.norm.weight": "model-00003-of-00004.safetensors"
|
||||
}
|
||||
}
|
||||
38
special_tokens_map.json
Normal file
38
special_tokens_map.json
Normal file
@@ -0,0 +1,38 @@
|
||||
{
|
||||
"additional_special_tokens": [
|
||||
"<|im_start|>",
|
||||
"<|im_end|>",
|
||||
"<|object_ref_start|>",
|
||||
"<|object_ref_end|>",
|
||||
"<|box_start|>",
|
||||
"<|box_end|>",
|
||||
"<|quad_start|>",
|
||||
"<|quad_end|>",
|
||||
"<|vision_start|>",
|
||||
"<|vision_end|>",
|
||||
"<|vision_pad|>",
|
||||
"<|image_pad|>",
|
||||
"<|video_pad|>"
|
||||
],
|
||||
"bos_token": {
|
||||
"content": "<|im_start|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false
|
||||
},
|
||||
"eos_token": {
|
||||
"content": "<|im_end|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false
|
||||
},
|
||||
"pad_token": {
|
||||
"content": "<|endoftext|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false
|
||||
}
|
||||
}
|
||||
303283
tokenizer.json
Normal file
303283
tokenizer.json
Normal file
File diff suppressed because it is too large
Load Diff
207
tokenizer_config.json
Normal file
207
tokenizer_config.json
Normal file
@@ -0,0 +1,207 @@
|
||||
{
|
||||
"add_bos_token": false,
|
||||
"add_prefix_space": false,
|
||||
"added_tokens_decoder": {
|
||||
"151643": {
|
||||
"content": "<|endoftext|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"151644": {
|
||||
"content": "<|im_start|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"151645": {
|
||||
"content": "<|im_end|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"151646": {
|
||||
"content": "<|object_ref_start|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"151647": {
|
||||
"content": "<|object_ref_end|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"151648": {
|
||||
"content": "<|box_start|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"151649": {
|
||||
"content": "<|box_end|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"151650": {
|
||||
"content": "<|quad_start|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"151651": {
|
||||
"content": "<|quad_end|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"151652": {
|
||||
"content": "<|vision_start|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"151653": {
|
||||
"content": "<|vision_end|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"151654": {
|
||||
"content": "<|vision_pad|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"151655": {
|
||||
"content": "<|image_pad|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"151656": {
|
||||
"content": "<|video_pad|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"151657": {
|
||||
"content": "<tool_call>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": false
|
||||
},
|
||||
"151658": {
|
||||
"content": "</tool_call>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": false
|
||||
},
|
||||
"151659": {
|
||||
"content": "<|fim_prefix|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": false
|
||||
},
|
||||
"151660": {
|
||||
"content": "<|fim_middle|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": false
|
||||
},
|
||||
"151661": {
|
||||
"content": "<|fim_suffix|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": false
|
||||
},
|
||||
"151662": {
|
||||
"content": "<|fim_pad|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": false
|
||||
},
|
||||
"151663": {
|
||||
"content": "<|repo_name|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": false
|
||||
},
|
||||
"151664": {
|
||||
"content": "<|file_sep|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": false
|
||||
}
|
||||
},
|
||||
"additional_special_tokens": [
|
||||
"<|im_start|>",
|
||||
"<|im_end|>",
|
||||
"<|object_ref_start|>",
|
||||
"<|object_ref_end|>",
|
||||
"<|box_start|>",
|
||||
"<|box_end|>",
|
||||
"<|quad_start|>",
|
||||
"<|quad_end|>",
|
||||
"<|vision_start|>",
|
||||
"<|vision_end|>",
|
||||
"<|vision_pad|>",
|
||||
"<|image_pad|>",
|
||||
"<|video_pad|>"
|
||||
],
|
||||
"bos_token": "<|im_start|>",
|
||||
"chat_template": "{%- if tools %}\n {{- '<|im_start|>system\\n' }}\n {%- if messages[0]['role'] == 'system' %}\n {{- messages[0]['content'] }}\n {%- else %}\n {{- 'You are a helpful assistant.' }}\n {%- endif %}\n {{- \"\\n\\n# Tools\\n\\nYou may call one or more functions to assist with the user query.\\n\\nYou are provided with function signatures within <tools></tools> XML tags:\\n<tools>\" }}\n {%- for tool in tools %}\n {{- \"\\n\" }}\n {{- tool | tojson }}\n {%- endfor %}\n {{- \"\\n</tools>\\n\\nFor each function call, return a json object with function name and arguments within <tool_call></tool_call> XML tags:\\n<tool_call>\\n{\\\"name\\\": <function-name>, \\\"arguments\\\": <args-json-object>}\\n</tool_call><|im_end|>\\n\" }}\n{%- else %}\n {%- if messages[0]['role'] == 'system' %}\n {{- '<|im_start|>system\\n' + messages[0]['content'] + '<|im_end|>\\n' }}\n {%- else %}\n {{- '<|im_start|>system\\nYou are a helpful assistant.<|im_end|>\\n' }}\n {%- endif %}\n{%- endif %}\n{%- for message in messages %}\n {%- if (message.role == \"user\") or (message.role == \"system\" and not loop.first) or (message.role == \"assistant\" and not message.tool_calls) %}\n {{- '<|im_start|>' + message.role + '\\n' + message.content + '<|im_end|>' + '\\n' }}\n {%- elif message.role == \"assistant\" %}\n {{- '<|im_start|>' + message.role }}\n {%- if message.content %}\n {{- '\\n' + message.content }}\n {%- endif %}\n {%- for tool_call in message.tool_calls %}\n {%- if tool_call.function is defined %}\n {%- set tool_call = tool_call.function %}\n {%- endif %}\n {{- '\\n<tool_call>\\n{\"name\": \"' }}\n {{- tool_call.name }}\n {{- '\", \"arguments\": ' }}\n {{- tool_call.arguments | tojson }}\n {{- '}\\n</tool_call>' }}\n {%- endfor %}\n {{- '<|im_end|>\\n' }}\n {%- elif message.role == \"tool\" %}\n {%- if (loop.index0 == 0) or (messages[loop.index0 - 1].role != \"tool\") %}\n {{- '<|im_start|>user' }}\n {%- endif %}\n {{- '\\n<tool_response>\\n' }}\n {{- message.content }}\n {{- '\\n</tool_response>' }}\n {%- if loop.last or (messages[loop.index0 + 1].role != \"tool\") %}\n {{- '<|im_end|>\\n' }}\n {%- endif %}\n {%- endif %}\n{%- endfor %}\n{%- if add_generation_prompt %}\n {{- '<|im_start|>assistant\\n' }}\n{%- endif %}\n",
|
||||
"clean_up_tokenization_spaces": false,
|
||||
"eos_token": "<|im_end|>",
|
||||
"errors": "replace",
|
||||
"model_max_length": 131072,
|
||||
"pad_token": "<|endoftext|>",
|
||||
"split_special_tokens": false,
|
||||
"tokenizer_class": "Qwen2Tokenizer",
|
||||
"unk_token": null
|
||||
}
|
||||
1
vocab.json
Normal file
1
vocab.json
Normal file
File diff suppressed because one or more lines are too long
Reference in New Issue
Block a user