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+*.7z filter=lfs diff=lfs merge=lfs -text
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+*.bin filter=lfs diff=lfs merge=lfs -text
+*.bz2 filter=lfs diff=lfs merge=lfs -text
+*.ckpt filter=lfs diff=lfs merge=lfs -text
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+*.gz filter=lfs diff=lfs merge=lfs -text
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+*.joblib filter=lfs diff=lfs merge=lfs -text
+*.lfs.* filter=lfs diff=lfs merge=lfs -text
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+*.model filter=lfs diff=lfs merge=lfs -text
+*.msgpack filter=lfs diff=lfs merge=lfs -text
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+*.pickle filter=lfs diff=lfs merge=lfs -text
+*.pkl filter=lfs diff=lfs merge=lfs -text
+*.pt filter=lfs diff=lfs merge=lfs -text
+*.pth filter=lfs diff=lfs merge=lfs -text
+*.rar filter=lfs diff=lfs merge=lfs -text
+*.safetensors filter=lfs diff=lfs merge=lfs -text
+saved_model/**/* filter=lfs diff=lfs merge=lfs -text
+*.tar.* filter=lfs diff=lfs merge=lfs -text
+*.tar filter=lfs diff=lfs merge=lfs -text
+*.tflite filter=lfs diff=lfs merge=lfs -text
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+*.wasm filter=lfs diff=lfs merge=lfs -text
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+*tfevents* filter=lfs diff=lfs merge=lfs -text
+t5gemma/tokenizer.json filter=lfs diff=lfs merge=lfs -text
+t5gemma-b-b-ul2/tokenizer.json filter=lfs diff=lfs merge=lfs -text
+Stable_Audio_3.0_Thumbnail_1x1.png filter=lfs diff=lfs merge=lfs -text

LICENSE.md

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+STABILITY AI COMMUNITY LICENSE AGREEMENT
+	
+Last Updated: July 5, 2024
+
+1. INTRODUCTION
+
+This Agreement applies to any individual person or entity (“You”, “Your” or “Licensee”) that uses or distributes any portion or element of the Stability AI Materials  or Derivative Works thereof for any Research & Non-Commercial or Commercial purpose. Capitalized terms not otherwise defined herein are defined in Section V below.
+
+This Agreement is intended to allow research, non-commercial, and limited commercial uses of the Models free of charge. In order to ensure that certain limited commercial uses of the Models continue to be allowed, this Agreement  preserves free access to the Models for people or organizations  generating annual revenue of less than US $1,000,000 (or local currency equivalent).  
+
+By clicking “I Accept”  or by using or distributing or using any portion or element of the Stability Materials or Derivative Works, You agree that You have read, understood and are bound by the terms of this Agreement. If You are acting on behalf of a company, organization or other entity, then “You” includes you and that entity, and You agree that You: (i) are an authorized representative of such entity with the authority to bind such entity to this Agreement, and (ii) You agree to the terms of this Agreement on that entity’s behalf. 
+
+2. RESEARCH & NON-COMMERCIAL USE LICENSE
+
+Subject to the terms of this Agreement, Stability AI grants You a non-exclusive, worldwide, non-transferable, non-sublicensable, revocable and royalty-free limited license under Stability AI’s intellectual property or other rights owned by Stability AI embodied in the Stability AI Materials to use, reproduce, distribute, and create Derivative Works of, and make modifications to, the Stability AI Materials for any Research or Non-Commercial Purpose. “Research Purpose” means academic or scientific advancement, and in each case, is not primarily intended for commercial advantage or monetary compensation to You or others. “Non-Commercial Purpose” means any purpose other than a Research Purpose that is not primarily intended for commercial advantage or monetary compensation to You or others, such as personal use (i.e., hobbyist) or evaluation and testing.  
+
+3. COMMERCIAL USE LICENSE
+
+Subject to the terms of this Agreement (including the remainder of this Section III), Stability AI grants You a non-exclusive, worldwide, non-transferable, non-sublicensable, revocable and royalty-free limited license under Stability AI’s intellectual property or other rights owned by Stability AI embodied in the Stability AI Materials to use, reproduce, distribute, and create Derivative Works of, and make modifications to, the Stability AI Materials for any Commercial Purpose. “Commercial Purpose” means any purpose other than a Research Purpose or Non-Commercial Purpose that is primarily intended for commercial advantage or monetary compensation to You or others, including but not limited to, (i) creating, modifying, or distributing Your product or service, including via a hosted service or application programming interface, and (ii) for Your business’s or organization’s internal operations. 
+If You are using or distributing the Stability AI Materials for a Commercial Purpose, You must register with Stability AI at (https://stability.ai/community-license). If at any time You or Your Affiliate(s), either individually or in aggregate, generate more than USD $1,000,000 in annual revenue (or the equivalent thereof in Your local currency), regardless of whether that revenue is generated directly or indirectly from the Stability AI Materials or Derivative Works, any licenses granted to You under this Agreement shall terminate as of such date. You must request a license from Stability AI at (https://stability.ai/enterprise) , which Stability AI may grant to You in its sole discretion. If you receive Stability AI Materials, or any Derivative Works thereof, from a Licensee as part of an integrated end user product, then Section III of this Agreement will not apply to you.
+
+4. GENERAL TERMS
+
+Your Research, Non-Commercial, and Commercial License(s) under this Agreement are subject to the following terms.
+a.  Distribution & Attribution. If You distribute or make available the Stability AI Materials or a Derivative Work to a third party, or a product or service that uses any portion of them, You shall: (i) provide a copy of this Agreement to that third party, (ii) retain the following attribution notice within a "Notice" text file distributed as a part of such copies: "This Stability AI Model is licensed under the Stability AI Community License, Copyright ©  Stability AI Ltd. All Rights Reserved”, and (iii) prominently display “Powered by Stability AI” on a related website, user interface, blogpost, about page, or product documentation.  If You create a Derivative Work, You may add your own attribution notice(s) to the “Notice” text file included with that Derivative Work, provided that You clearly indicate which attributions apply to the Stability AI Materials and state in the “Notice” text file that You changed the Stability AI Materials and how it was modified.
+b.  Use Restrictions. Your use of the Stability AI Materials and Derivative Works, including any output or results of the Stability AI Materials or Derivative Works, must comply with applicable laws and regulations (including Trade Control Laws and equivalent regulations) and adhere to the Documentation and Stability AI’s AUP, which is hereby incorporated by reference. Furthermore, You will not use the Stability AI Materials or Derivative Works, or any output or results of the Stability AI Materials or Derivative Works, to create or improve any foundational generative AI model (excluding the Models or Derivative Works). 
+c.  Intellectual Property. 
+(i) Trademark License.  No trademark licenses are granted under this Agreement, and in connection with the Stability AI Materials or Derivative Works, You may not use any name or mark owned by or associated with Stability AI or any of its Affiliates, except as required under Section IV(a) herein.
+(ii)  Ownership of Derivative Works.  As between You and Stability AI, You are the owner of Derivative Works You create, subject to Stability AI’s ownership of the Stability AI Materials and any Derivative Works made by or for Stability AI.
+(iii)  Ownership of Outputs. As between You and Stability AI, You own any outputs generated from the Models or Derivative Works to the extent permitted by applicable law. 
+(iv)  Disputes.  If You or Your Affiliate(s) institute litigation or other proceedings against Stability AI (including a cross-claim or counterclaim in a lawsuit) alleging that the Stability AI Materials, Derivative Works or associated outputs or results, or any portion of any of the foregoing, constitutes infringement of intellectual property or other rights owned or licensable by You, then any licenses granted to You under this Agreement shall terminate as of the date such litigation or claim is filed or instituted. You will indemnify and hold harmless Stability AI from and against any claim by any third party arising out of or related to Your use or distribution of the Stability AI Materials or Derivative Works in violation of this Agreement.
+(v)  Feedback.  From time to time, You may provide Stability AI with verbal and/or written suggestions, comments or other feedback related to Stability AI’s existing or prospective technology, products or services (collectively, “Feedback”). You are not obligated to provide Stability AI with Feedback, but to the extent that You do, You hereby grant Stability AI a perpetual, irrevocable, royalty-free, fully-paid, sub-licensable, transferable, non-exclusive, worldwide right and license to exploit the Feedback in any manner without restriction. Your Feedback is provided “AS IS” and You make no warranties whatsoever about any Feedback.
+d.  Disclaimer Of Warranty. UNLESS REQUIRED BY APPLICABLE LAW, THE STABILITY AI MATERIALS AND ANY OUTPUT AND RESULTS THEREFROM ARE PROVIDED ON AN "AS IS" BASIS, WITHOUT WARRANTIES OF ANY KIND, EITHER EXPRESS OR IMPLIED, INCLUDING, WITHOUT LIMITATION, ANY WARRANTIES OF TITLE, NON-INFRINGEMENT, MERCHANTABILITY, OR FITNESS FOR A PARTICULAR PURPOSE. YOU ARE SOLELY RESPONSIBLE FOR DETERMINING THE APPROPRIATENESS OR LAWFULNESS OF USING OR REDISTRIBUTING THE STABILITY AI MATERIALS, DERIVATIVE WORKS OR ANY OUTPUT OR RESULTS AND ASSUME ANY RISKS ASSOCIATED WITH YOUR USE OF THE STABILITY AI MATERIALS, DERIVATIVE WORKS AND ANY OUTPUT AND RESULTS.
+e.  Limitation Of Liability. IN NO EVENT WILL STABILITY AI OR ITS AFFILIATES BE LIABLE UNDER ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, TORT, NEGLIGENCE, PRODUCTS LIABILITY, OR OTHERWISE, ARISING OUT OF THIS AGREEMENT, FOR ANY LOST PROFITS OR ANY DIRECT, INDIRECT, SPECIAL, CONSEQUENTIAL, INCIDENTAL, EXEMPLARY OR PUNITIVE DAMAGES, EVEN IF STABILITY AI OR ITS AFFILIATES HAVE BEEN ADVISED OF THE POSSIBILITY OF ANY OF THE FOREGOING.
+f.  Term And Termination. The term of this Agreement will commence upon Your acceptance of this Agreement or access to the Stability AI Materials and will continue in full force and effect until terminated in accordance with the terms and conditions herein. Stability AI may terminate this Agreement if You are in breach of any term or condition of this Agreement. Upon termination of this Agreement, You shall delete and cease use of any Stability AI Materials or Derivative Works. Section IV(d), (e), and (g) shall survive the termination of this Agreement.
+g.  Governing Law.  This Agreement will be governed by and constructed in accordance with the laws of the United States and the State of California without regard to choice of law principles, and the UN Convention on Contracts for International Sale of Goods does not apply to this Agreement.
+
+5. DEFINITIONS 
+
+“Affiliate(s)” means any entity that directly or indirectly controls, is controlled by, or is under common control with the subject entity; for purposes of this definition, “control” means direct or indirect ownership or control of more than 50% of the voting interests of the subject entity.
+
+"Agreement" means this Stability AI Community License Agreement.
+
+“AUP” means the Stability AI Acceptable Use Policy available at (https://stability.ai/use-policy), as may be updated from time to time.
+
+"Derivative Work(s)” means (a) any derivative work of the Stability AI Materials as recognized by U.S. copyright laws and (b) any modifications to a Model, and any other model created which is based on or derived from the Model or the Model’s output, including “fine tune” and “low-rank adaptation” models derived from a Model or a Model’s output, but do not include the output of any Model.
+
+“Documentation” means any specifications, manuals, documentation, and other written information provided by Stability AI related to the Software or Models.
+
+“Model(s)" means, collectively, Stability AI’s proprietary models and algorithms, including machine-learning models, trained model weights and other elements of the foregoing listed on Stability’s Core Models Webpage available at (https://stability.ai/core-models), as may be updated from time to time.
+
+"Stability AI" or "we" means Stability AI Ltd. and its Affiliates.
+
+"Software" means Stability AI’s proprietary software made available under this Agreement now or in the future.
+
+“Stability AI Materials” means, collectively, Stability’s proprietary Models, Software and Documentation (and any portion or combination thereof) made available under this Agreement. 
+
+“Trade Control Laws” means any applicable U.S. and non-U.S. export control and trade sanctions laws and regulations.

LICENSE_GEMMA.md

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+# Gemma Terms of Use
+
+The terms below apply to Gemma models listed in the Appendix at bottom of this page. For Gemma 4 terms, see the [Gemma 4 license](https://ai.google.dev/gemma/apache_2).
+
+Last modified: April 1, 2026
+
+By using, reproducing, modifying, distributing, performing or displaying any
+portion or element of Gemma, Model Derivatives including via any Hosted Service,
+(each as defined below) (collectively, the "**Gemma Services**") or otherwise
+accepting the terms of this Agreement, you agree to be bound by this Agreement.
+
+## Section 1: DEFINITIONS
+
+### 1.1 Definitions
+
+(a) "**Agreement** " or "**Gemma Terms of Use**" means these terms and conditions
+that govern the use, reproduction, Distribution or modification of the Gemma
+Services and any terms and conditions incorporated by reference.
+
+(b) "**Distribution** " or "**Distribute** " means any transmission, publication,
+or other sharing of Gemma or Model Derivatives to a third party, including by
+providing or making Gemma or its functionality available as a hosted service via
+API, web access, or any other electronic or remote means ("**Hosted Service**").
+
+(c) "**Gemma** " means the set of machine learning language models, trained model
+weights and parameters identified in the [Appendix](https://ai.google.dev/gemma/terms#appendix),
+regardless of the source that you obtained it from.
+
+(d) "**Google**" means Google LLC.
+
+(e) "**Model Derivatives**" means all (i) modifications to Gemma, (ii) works based
+on Gemma, or (iii) any other machine learning model which is created by transfer
+of patterns of the weights, parameters, operations, or Output of Gemma, to that
+model in order to cause that model to perform similarly to Gemma, including
+distillation methods that use intermediate data representations or methods based
+on the generation of synthetic data Outputs by Gemma for training that model.
+For clarity, Outputs are not deemed Model Derivatives.
+
+(f) "**Output**" means the information content output of Gemma or a Model
+Derivative that results from operating or otherwise using Gemma or the Model
+Derivative, including via a Hosted Service.
+
+### 1.2
+
+As used in this Agreement, "**including** " means
+"**including without limitation**".
+
+## Section 2: ELIGIBILITY AND USAGE
+
+### 2.1 Eligibility
+
+You represent and warrant that you have the legal capacity to enter into this
+Agreement (including being of sufficient age of consent). If you are accessing
+or using any of the Gemma Services for or on behalf of a legal entity, (a) you
+are entering into this Agreement on behalf of yourself and that legal entity,
+(b) you represent and warrant that you have the authority to act on behalf of
+and bind that entity to this Agreement and (c) references to "**you** " or
+"**your**" in the remainder of this Agreement refers to both you (as an
+individual) and that entity.
+
+### 2.2 Use
+
+You may use, reproduce, modify, Distribute, perform or display any of the Gemma
+Services only in accordance with the terms of this Agreement, and must not
+violate (or encourage or permit anyone else to violate) any term of this
+Agreement.
+
+## Section 3: DISTRIBUTION AND RESTRICTIONS
+
+### 3.1 Distribution and Redistribution
+
+You may reproduce or Distribute copies of Gemma or Model Derivatives if you meet
+all of the following conditions:
+
+1. You must include the use restrictions referenced in Section 3.2 as an enforceable provision in any agreement (e.g., license agreement, terms of use, etc.) governing the use and/or distribution of Gemma or Model Derivatives and you must provide notice to subsequent users you Distribute to that Gemma or Model Derivatives are subject to the use restrictions in Section 3.2.
+2. You must provide all third party recipients of Gemma or Model Derivatives a copy of this Agreement.
+3. You must cause any modified files to carry prominent notices stating that you modified the files.
+4. All Distributions (other than through a Hosted Service) must be accompanied by a "**Notice** " text file that contains the following notice: "**Gemma is provided under and subject to the Gemma Terms of Use found at ai.google.dev/gemma/terms**".
+
+You may add your own intellectual property statement to your modifications and,
+except as set forth in this Section, may provide additional or different terms
+and conditions for use, reproduction, or Distribution of your modifications, or
+for any such Model Derivatives as a whole, provided your use, reproduction,
+modification, Distribution, performance, and display of Gemma otherwise complies
+with the terms and conditions of this Agreement. Any additional or different
+terms and conditions you impose must not conflict with the terms of this
+Agreement.
+
+### 3.2 Use Restrictions
+
+You must not use any of the Gemma Services:
+
+1. for the restricted uses set forth in the Gemma Prohibited Use Policy at [ai.google.dev/gemma/prohibited_use_policy](https://ai.google.dev/gemma/prohibited_use_policy) ("**Prohibited Use Policy**"), which is hereby incorporated by reference into this Agreement; or
+2. in violation of applicable laws and regulations.
+
+To the maximum extent permitted by law, Google reserves the right to restrict
+(remotely or otherwise) usage of any of the Gemma Services that Google
+reasonably believes are in violation of this Agreement.
+
+### 3.3 Generated Output
+
+Google claims no rights in Outputs you generate using Gemma. You and your users
+are solely responsible for Outputs and their subsequent uses.
+
+## Section 4: ADDITIONAL PROVISIONS
+
+### 4.1 Updates
+
+Google may update Gemma from time to time.
+
+### 4.2 Trademarks
+
+Nothing in this Agreement grants you any rights to use Google's trademarks,
+trade names, logos or to otherwise suggest endorsement or misrepresent the
+relationship between you and Google. Google reserves any rights not expressly
+granted herein.
+
+### 4.3 DISCLAIMER OF WARRANTY
+
+UNLESS REQUIRED BY APPLICABLE LAW, THE GEMMA SERVICES, AND OUTPUTS, ARE PROVIDED
+ON AN "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, EITHER
+EXPRESS OR IMPLIED, INCLUDING 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, REPRODUCING,
+MODIFYING, PERFORMING, DISPLAYING OR DISTRIBUTING ANY OF THE GEMMA SERVICES
+OR OUTPUTS AND ASSUME ANY AND ALL RISKS ASSOCIATED WITH YOUR USE OR DISTRIBUTION
+OF ANY OF THE GEMMA SERVICES OR OUTPUTS AND YOUR EXERCISE OF RIGHTS AND
+PERMISSIONS UNDER THIS AGREEMENT.
+
+### 4.4 LIMITATION OF LIABILITY
+
+TO THE FULLEST EXTENT PERMITTED BY APPLICABLE LAW, IN NO EVENT AND UNDER NO
+LEGAL THEORY, WHETHER IN TORT (INCLUDING NEGLIGENCE), PRODUCT LIABILITY,
+CONTRACT, OR OTHERWISE, UNLESS REQUIRED BY APPLICABLE LAW, SHALL GOOGLE OR ITS
+AFFILIATES BE LIABLE TO YOU FOR DAMAGES, INCLUDING ANY DIRECT, INDIRECT,
+SPECIAL, INCIDENTAL, EXEMPLARY, CONSEQUENTIAL, OR PUNITIVE DAMAGES, OR LOST
+PROFITS OF ANY KIND ARISING FROM THIS AGREEMENT OR RELATED TO, ANY OF THE GEMMA
+SERVICES OR OUTPUTS EVEN IF GOOGLE OR ITS AFFILIATES HAVE BEEN ADVISED OF THE
+POSSIBILITY OF SUCH DAMAGES.
+
+### 4.5 Term, Termination, and Survival
+
+The term of this Agreement will commence upon your acceptance of this Agreement
+(including acceptance by your use, modification, or Distribution, reproduction,
+performance or display of any portion or element of the Gemma Services) and will
+continue in full force and effect until terminated in accordance with the terms
+of this Agreement. Google may terminate this Agreement if you are in breach of
+any term of this Agreement. Upon termination of this Agreement, you must delete
+and cease use and Distribution of all copies of Gemma and Model Derivatives in
+your possession or control. Sections 1, 2.1, 3.3, 4.2 to 4.9 shall survive the
+termination of this Agreement.
+
+### 4.6 Governing Law and Jurisdiction
+
+This Agreement will be governed by the laws of the State of California without
+regard to choice of law principles. The UN Convention on Contracts for the
+International Sale of Goods does not apply to this Agreement. The state and
+federal courts of Santa Clara County, California shall have exclusive
+jurisdiction of any dispute arising out of this Agreement.
+
+### 4.7 Severability
+
+If any provision of this Agreement is held to be invalid, illegal or
+unenforceable, the remaining provisions shall be unaffected thereby and remain
+valid as if such provision had not been set forth herein.
+
+### 4.8 Entire Agreement
+
+This Agreement states all the terms agreed between the parties and supersedes
+all other agreements between the parties as of the date of acceptance relating
+to its subject matter.
+
+### 4.9 No Waiver
+
+Google will not be treated as having waived any rights by not exercising (or
+delaying the exercise of) any rights under this Agreement.
+
+## Appendix
+
+- [Gemma 1](https://ai.google.dev/gemma/docs/core/model_card)
+- [Gemma 1.1](https://ai.google.dev/gemma/docs/core/model_card)
+- [Gemma 2](https://ai.google.dev/gemma/docs/core/model_card_2)
+- [Gemma 3](https://ai.google.dev/gemma/docs/core/model_card_3)
+- [Gemma 3n](https://ai.google.dev/gemma/docs/3n)
+- [FunctionGemma](https://ai.google.dev/gemma/docs/functiongemma)
+- [EmbeddingGemma](https://ai.google.dev/gemma/docs/embeddinggemma)
+- [PaliGemma](https://ai.google.dev/gemma/docs/paligemma/model-card)
+- [PaliGemma 2](https://ai.google.dev/gemma/docs/paligemma/model-card-2)
+- [ShieldGemma](https://ai.google.dev/gemma/docs/shieldgemma/model_card)
+- [ShieldGemma 2](https://ai.google.dev/gemma/docs/shieldgemma/model_card_2)
+- [CodeGemma](https://ai.google.dev/gemma/docs/codegemma/model_card)
+- [CodeGemma 1.1](https://ai.google.dev/gemma/docs/codegemma/model_card)
+- [Gemma 2 JPN](https://huggingface.co/google/gemma-2-2b-jpn-it)
+- [DataGemma RIG](https://www.kaggle.com/models/google/datagemma-rig)
+- [DataGemma RAG](https://www.kaggle.com/models/google/datagemma-rag)
+- [RecurrentGemma](https://ai.google.dev/gemma/docs/recurrentgemma/model_card)
+- [Gemma Scope](https://ai.google.dev/gemma/docs/gemma_scope)
+- [Gemma-APS](https://ai.google.dev/gemma/docs/gemma-aps)
+- [T5Gemma](https://www.kaggle.com/models/google/t5gemma)
+- [VaultGemma](https://www.kaggle.com/models/google/vaultgemma)
+- [FunctionGemma](https://www.kaggle.com/models/google/functiongemma)
+- [T5Gemma 2](https://www.kaggle.com/models/google/t5gemma-2)
+- [TranslateGemma](https://www.kaggle.com/models/google/translategemma)
+
+> [!NOTE]
+> **Note:** Previous versions of these Terms are [archived here](https://ai.google.dev/gemma/terms-archive).

NOTICE

@@ -0,0 +1 @@
+Gemma is provided under and subject to the Gemma Terms of Use found at ai.google.dev/gemma/terms

README.md

@@ -0,0 +1,114 @@
+---
+language:
+- en
+library_name: stable-audio-3
+license: other
+license_name: stable-audio-community
+license_link: LICENSE
+pipeline_tag: text-to-audio
+tags:
+- audio-generation
+- sound-effects
+- diffusion
+---
+
+# Stable Audio 3 Small SFX (Base)
+
+> **Note:** This is the base (pre-trained) model intended for fine-tuning. If you are looking to generate audio directly, please use [Stable Audio 3 Small SFX](https://huggingface.co/stabilityai/stable-audio-3-small-sfx) instead.
+
+Please note: For commercial use, please refer to [https://stability.ai/license](https://stability.ai/license)
+
+## Model Description
+`Stable Audio 3` is a family of fast latent diffusion models (small, medium, large) for variable length audio generation and editing. Since our models can generate several minutes of audio,
+variable-length generations are key to avoid the cost of producing full-length generations for short
+sounds. We also support inpainting, enabling targeted audio editing and the continuation of short
+recordings. Our latent diffusion models operate on top of a novel semantic-acoustic autoencoder that
+projects audio into a compact latent space, enabling efficient diffusion-based generation while preserving audio fidelity and encouraging semantic structure in the latent. Finally, we run adversarial
+post-training to both accelerate inference and improve generation quality, reducing the number of inference steps while improving fidelity and prompt adherence. Stable Audio 3 models are trained on
+licensed and Creative Commons data to generate music and sounds in less than a 2s on an H200 GPU
+and less than a few seconds on a MacBook Pro M4. We release the weights of small and medium,
+that can run on consumer-grade hardware, together with their training and inference pipeline.
+
+## Usage
+
+This model can be used with:
+1. the [`stable-audio-3`](https://github.com/Stability-AI/stable-audio-3) inference and fine-tuning library
+2. the [`stable-audio-tools`](https://github.com/Stability-AI/stable-audio-tools) research library
+
+
+### Using with `stable-audio-3`
+```python
+from stable_audio_3 import StableAudioModel
+
+model = StableAudioModel.from_pretrained("small-sfx-base")
+audio = model.generate(
+    prompt="chugging train coming into station with horn",
+    duration=7,
+    steps=50,
+    cfg_scale=7.0
+)
+```
+
+### Using with `stable-audio-tools`
+
+```python
+import torch
+import torchaudio
+from einops import rearrange
+from stable_audio_tools import get_pretrained_model
+from stable_audio_tools.inference.generation import generate_diffusion_cond_inpaint
+
+device = "cuda" if torch.cuda.is_available() else "cpu"
+if device == "cuda":
+  model_half = True
+
+# Download model
+model, model_config = get_pretrained_model("stabilityai/stable-audio-3-small-sfx-base")
+sample_rate = model_config["sample_rate"]
+sample_size = model_config["sample_size"]
+
+model = model.to(device)
+if model_half:
+  model = model.to(torch.float16)
+# Set up text and timing conditioning
+conditioning = [{
+    "prompt": "chugging train coming into station with horn",
+    "seconds_total": 7
+}]
+
+# Generate stereo audio
+output = generate_diffusion_cond_inpaint(
+    model,
+    steps=50,
+    cfg_scale=7.0,
+    conditioning=conditioning,
+    sample_size=sample_size,
+    sampler_type="euler",
+    device=device
+)
+
+# Rearrange audio batch to a single sequence
+output = rearrange(output, "b d n -> d (b n)")
+
+# Peak normalize, clip, convert to int16, and save to file
+output = output.to(torch.float32).div(torch.max(torch.abs(output))).clamp(-1, 1).mul(32767).to(torch.int16).cpu()
+torchaudio.save("output.wav", output, sample_rate)
+```
+
+
+## Model Details
+* **Model type**: `Stable Audio 3` is a latent diffusion model based on a transformer architecture.
+* **Language(s)**: English
+* **License**: [Stability AI Community License](https://stability.ai/license).
+* **Research Paper**: [https://arxiv.org/abs/2605.17991](https://arxiv.org/abs/2605.17991)
+
+We use a publicly available pre-trained T5Gemma model ([t5gemma-b-b-ul2](https://huggingface.co/google/t5gemma-b-b-ul2)) for text conditioning. T5Gemma is redistributed under the [Gemma Terms of Use](LICENSE_GEMMA.md).
+
+## Training dataset
+
+### Datasets Used
+Our dataset consists of 1,278,902 audio recordings, where 806,284 recordings are licensed from [AudioSparx](https://www.audiosparx.com/) and a further 472,618 are from [Freesound](https://freesound.org/). 
+The Freesound portion consists of recordings licensed under CC-0, CC-BY, or CCSampling+. To ensure no copyrighted content was present in the Freesound data, music recordings were identified
+using the PANNs [89] tagger. We flagged audio that activated music-related tags for at least 30s (threshold of 0.15),
+that was sent to a trusted content detection company to verify the absence of copyrighted material. All identified copyrighted content was removed. After filtering, the Freesound part includes 266,324 CC-0, 194,840 CC-BY, and 11,454
+CC-Sampling+ recordings. The same subset of Freesound audio we used to train Stable Audio Open: https://info.stability.ai/attributions.

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model_config.json

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+{
+    "model_type": "diffusion_cond_inpaint",
+    "sample_size": 5324800,
+    "sample_rate": 44100,
+    "audio_channels": 2,
+    "model": {
+        "pretransform": {
+            "type": "autoencoder",
+            "iterate_batch": false,
+            "chunked": true,
+            "enable_grad": false,
+            "scale": 1.0,
+            "config": {
+                "pretransform": {
+                    "type": "patched",
+                    "enable_grad": false,
+                    "config": {
+                        "patch_size": 256,
+                        "channels": 2
+                    }
+                },
+                "encoder": {
+                    "type": "taae_v2",
+                    "requires_grad": false,
+                    "config": {
+                        "in_channels": 512,
+                        "channels": 128,
+                        "c_mults": [
+                            6
+                        ],
+                        "strides": [
+                            16
+                        ],
+                        "latent_dim": 256,
+                        "transformer_depths": [
+                            6
+                        ],
+                        "checkpointing": false,
+                        "differential": true,
+                        "mapping_style": "none",
+                        "dim_heads": 64,
+                        "variable_stride": true,
+                        "use_flash": true,
+                        "dyt": true,
+                        "chunk_size": 32,
+                        "chunk_midpoint_shift": true,
+                        "conv_mapping": false,
+                        "mask_noise": 0.0
+                    }
+                },
+                "decoder": {
+                    "type": "taae_v2",
+                    "requires_grad": false,
+                    "config": {
+                        "out_channels": 512,
+                        "channels": 128,
+                        "c_mults": [
+                            6
+                        ],
+                        "strides": [
+                            16
+                        ],
+                        "latent_dim": 256,
+                        "transformer_depths": [
+                            6
+                        ],
+                        "sinusoidal_blocks": [
+                            0
+                        ],
+                        "checkpointing": false,
+                        "differential": true,
+                        "mapping_style": "none",
+                        "dim_heads": 64,
+                        "variable_stride": true,
+                        "use_flash": true,
+                        "dyt": true,
+                        "chunk_size": 32,
+                        "conv_mapping": true,
+                        "chunk_midpoint_shift": true,
+                        "freeze_backbone": false,
+                        "mask_noise": 0.01
+                    }
+                },
+                "bottleneck": {
+                    "type": "softnorm",
+                    "requires_grad": false,
+                    "config": {
+                        "dim": 256,
+                        "noise_augment_dim": 0,
+                        "noise_regularize": true,
+                        "auto_scale": true,
+                        "freeze": true
+                    }
+                },
+                "latent_dim": 256,
+                "downsampling_ratio": 4096,
+                "io_channels": 2
+            }
+        },
+        "conditioning": {
+            "configs": [
+                {
+                    "id": "prompt",
+                    "type": "t5gemma",
+                    "config": {
+                        "max_length": 256,
+                        "padding_mode": "learned",
+                        "repo_id": "stabilityai/stable-audio-3-small-sfx",
+                        "subfolder": "t5gemma-b-b-ul2"
+                    }
+                },
+                {
+                    "id": "seconds_total",
+                    "type": "number",
+                    "config": {
+                        "min_val": 0,
+                        "max_val": 384,
+                        "fourier_features_type": "expo"
+                    }
+                }
+            ],
+            "cond_dim": 768
+        },
+        "diffusion": {
+            "cross_attention_cond_ids": [
+                "prompt",
+                "seconds_total"
+            ],
+            "global_cond_ids": [
+                "seconds_total"
+            ],
+            "local_add_cond_ids": [
+                "inpaint_mask",
+                "inpaint_masked_input"
+            ],
+            "type": "dit",
+            "diffusion_objective": "rectified_flow",
+            "use_effective_length_for_schedule": true,
+            "mask_padding_attention": true,
+            "distribution_shift_options": {
+                "type": "full",
+                "min_length": 256,
+                "max_length": 4096
+            },
+            "config": {
+                "io_channels": 256,
+                "embed_dim": 1024,
+                "depth": 20,
+                "num_heads": 16,
+                "cond_token_dim": 768,
+                "global_cond_dim": 768,
+                "local_add_cond_dim": 257,
+                "global_cond_type": "adaLN",
+                "timestep_features_type": "expo",
+                "timestep_features_logsnr": false,
+                "attn_kwargs": {
+                    "qk_norm": "rms",
+                    "differential": false
+                },
+                "norm_type": "rms_norm",
+                "norm_kwargs": {
+                    "force_fp32": true
+                },
+                "ff_kwargs": {
+                    "mult": 4.0
+                },
+                "num_memory_tokens": 64
+            }
+        },
+        "io_channels": 256
+    },
+    "training": {
+        "use_ema": true,
+        "log_loss_info": false,
+        "pre_encoded": true,
+        "timestep_sampler": "trunc_logit_normal",
+        "mask_loss_weight": 1.0,
+        "log_every_n_steps": 100,
+        "silence_extension_scale_seconds": 4.0,
+        "ot_coupling": true,
+        "inpainting": {
+            "mask_kwargs": {
+                "mask_type_probabilities": [
+                    0.2,
+                    0.6,
+                    0.2
+                ]
+            }
+        },
+        "optimizer_configs": {
+            "diffusion": {
+                "optimizer": {
+                    "type": "MuonAdamW",
+                    "config": {
+                        "muon_lr": 0.001,
+                        "muon_momentum": 0.95,
+                        "fused_layer_patterns": [
+                            "*.to_qkv.*",
+                            "*.to_kv.*",
+                            "*.to_q.*",
+                            "*.ff.*.proj.*"
+                        ],
+                        "adam_lr": 5e-05,
+                        "adam_betas": [
+                            0.9,
+                            0.95
+                        ],
+                        "adam_weight_decay": 0.01
+                    }
+                },
+                "scheduler": {
+                    "type": "InverseLR",
+                    "config": {
+                        "inv_gamma": 1000000,
+                        "power": 0.5,
+                        "warmup": 0.995
+                    }
+                }
+            }
+        },
+        "demo": {
+            "demo_every": 500,
+            "demo_steps": 50,
+            "demo_cfg_scales": [
+                2,
+                4,
+                7
+            ],
+            "demo_cond": [
+                {
+                    "prompt": "A beautiful piano arpeggio grows into a grand cinematic climax",
+                    "seconds_total": 119
+                },
+                {
+                    "prompt": "Elegant and sophisticated Latin jazz piece with a Cuban base and a whispered melodic female voice",
+                    "seconds_total": 100
+                },
+                {
+                    "prompt": "Amen break 174 BPM",
+                    "seconds_total": 9
+                },
+                {
+                    "prompt": "lofi house loop",
+                    "seconds_total": 35
+                }
+            ],
+            "inpaint_demos": {
+                "num_random_spans": 2,
+                "num_causal": 1
+            },
+            "log_snr_sampling": true
+        }
+    }
+}

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t5gemma-b-b-ul2/README.md

@@ -0,0 +1,260 @@
+---
+license: gemma
+library_name: transformers
+pipeline_tag: text2text-generation
+extra_gated_heading: Access Gemma on Hugging Face
+extra_gated_prompt: To access Gemma on Hugging Face, you’re required to review and
+  agree to Google’s usage license. To do this, please ensure you’re logged in to Hugging
+  Face and click below. Requests are processed immediately.
+extra_gated_button_content: Acknowledge license
+base_model: google/t5gemma-b-b-ul2
+---
+
+> [!Note]
+> This repository corresponds to T5Gemma (pretrained) with B encoder and B decoder (adapted using UL2)
+
+# T5Gemma model card
+
+**Model Page**: [T5Gemma](https://ai.google.dev/gemma/docs/t5gemma)  
+
+**Resources and Technical Documentation**:
+
+- [T5Gemma Technical Report](https://arxiv.org/abs/2504.06225)
+- [Responsible Generative AI Toolkit](https://ai.google.dev/responsible)
+- [T5Gemma on Kaggle](https://www.kaggle.com/models/google/t5gemma)
+- [T5Gemma on Vertex Model Garden](https://console.cloud.google.com/vertex-ai/publishers/google/model-garden/t5gemma)
+
+**Terms of Use**: [Terms](https://ai.google.dev/gemma/terms)  
+
+**Authors**: Google DeepMind
+
+## Model Information
+
+Summary description and brief definition of inputs and outputs.
+
+### Description
+
+T5Gemma is a family of lightweight yet powerful encoder-decoder research models from Google. These models are created by adapting pretrained decoder-only models into a encoder-decoder. This adaptation allows T5Gemma to inherit the foundational capabilities of the decoder-only models while also offering a more favorable quality-efficiency trade-off. A key feature is the flexibility to pair encoders and decoders of different sizes(e.g., a 9B encoder with a 2B decoder).  
+T5Gemma is released in two different series:
+
+- **Gemma 2 Series**:, Models directly adapted from the official Gemma 2 2B and 9B checkpoints. It includes 2B-2B, 9B-9B, and 9B-2B variants.
+- **T5-compatible Series**: Models pretrained from scratch using the Gemma 2 recipe but with architectures and parameter counts that align with traditional T5 models (Small, Base, Large, XL). This series also includes an ML (Medium-Large, ~2B) model to bridge the gap between Large and XL.
+
+These models are text-to-text, available in English, with open weights for pre-trained variants (adapted via objectives like PrefixLM or UL2) and instruction-tuned variants. T5Gemma models are well-suited for a variety of generative tasks, including question answering, summarization, and reasoning. Meanwhile, their encoders can be leveraged for discriminative tasks, providing strong performance on classification and understanding benchmarks. Their relatively small size makes it possible to deploy them in environments with limited resources such as laptops, desktops or your own cloud infrastructure, democratizing access to state of the art AI models and helping foster innovation for everyone.
+
+### Usage
+
+Below we share some code snippets on how to get quickly started with running the model. First, install the Transformers library with:
+```sh
+pip install -U transformers
+```
+
+Then, copy the snippet from the section that is relevant for your usecase.
+
+#### Running with the `pipeline` API
+
+```python
+import torch
+from transformers import pipeline
+
+pipe = pipeline(
+    "text2text-generation",
+    model="google/t5gemma-b-b-ul2",
+    device="cuda",  # replace with "mps" to run on a Mac device
+)
+
+text = "Once upon a time,"
+outputs = pipe(text, max_new_tokens=32)
+response = outputs[0]["generated_text"]
+print(response)
+```
+
+#### Running the model on a single / multi GPU
+
+```python
+# pip install accelerate
+from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
+import torch
+
+tokenizer = AutoTokenizer.from_pretrained("google/t5gemma-b-b-ul2")
+model = AutoModelForSeq2SeqLM.from_pretrained(
+    "google/t5gemma-b-b-ul2",
+    device_map="auto",
+)
+
+input_text = "Write me a poem about Machine Learning. Answer:"
+input_ids = tokenizer(input_text, return_tensors="pt").to("cuda")
+
+outputs = model.generate(**input_ids, max_new_tokens=32)
+print(tokenizer.decode(outputs[0]))
+```
+
+
+
+### Inputs and outputs
+
+- **Input:**
+  - Text string, such as a question, a prompt, or a document to be summarized
+
+- **Output:**
+  - Generated English-language text in response to the input, such as an answer to a question, or a summary of a document.
+
+### Citation
+
+```none
+@article{t5gemma_2025,
+  title={Encoder-Decoder Gemma: Improving the Quality-Efficiency Trade-Off via Adaptation},
+  author={Zhang, Biao and Moiseev, Fedor and Ainslie, Joshua and Suganthan, Paul and Ma, Min and Bhupatiraju, Surya and Lebron, Fede and Firat, Orhan and Joulin, Armand and Dong, Zhe},
+  year={2025}
+}
+```
+
+## Model Data
+
+Data used for model training and how the data was processed.
+
+### Training Dataset
+
+These models were trained on a dataset of text data that includes a wide variety of sources. The 9B-9B, 9B-2B, and 2B-2B models were adapted with 2 trillion tokens, and the T5-sized models (Small, Base, Large, ML and XL) were first pretrained with 2 trillion tokens (decoder-only) and then adapted with 2 trillion tokens (encoder-decoder). Here are the key components:
+
+- Web Documents: A diverse collection of web text ensures the model is exposed to a broad range of linguistic styles, topics, and vocabulary. Primarily English-language content.
+- Code: Exposing the model to code helps it to learn the syntax and patterns of programming languages, which improves its ability to generate code or understand code-related questions.
+- Mathematics: Training on mathematical text helps the model learn logical reasoning, symbolic representation, and to address mathematical queries.
+
+The combination of these diverse data sources is crucial for training a powerful language model that can handle a wide variety of different tasks and text formats.
+
+### Data Preprocessing
+
+Here are the key data cleaning and filtering methods applied to the training data:
+
+- CSAM Filtering: Rigorous CSAM (Child Sexual Abuse Material) filtering was applied at multiple stages in the data preparation process to ensure the exclusion of harmful and illegal content.
+- Sensitive Data Filtering: As part of making Gemma pre-trained models safe and reliable, automated techniques were used to filter out certain personal information and other sensitive data from training sets.
+- Additional methods: Filtering based on content quality and safety in line with [our policies](https://ai.google/static/documents/ai-responsibility-update-published-february-2025.pdf).
+
+## Implementation Information
+
+Details about the model internals.
+
+### Hardware
+
+T5Gemma was trained using [Tensor Processing Unit (TPU)](https://cloud.google.com/tpu/docs/intro-to-tpu) hardware (TPUv4p, TPUv5p and TPUv5e). Training large language models requires significant computational power. TPUs, designed specifically for matrix operations common in machine learning, offer several advantages in this domain:
+
+- Performance: TPUs are specifically designed to handle the massive computations involved in training LLMs. They can speed up training considerably compared to CPUs.
+- Memory: TPUs often come with large amounts of high-bandwidth memory, allowing for the handling of large models and batch sizes during training. This can lead to better model quality.
+- Scalability: TPU Pods (large clusters of TPUs) provide a scalable solution for handling the growing complexity of large foundation models. You can distribute training across multiple TPU devices for faster and more efficient processing.
+- Cost-effectiveness: In many scenarios, TPUs can provide a more cost-effective solution for training large models compared to CPU-based infrastructure, especially when considering the time and resources saved due to faster training.
+- These advantages are aligned with [Google's commitments to operate sustainably](https://sustainability.google/operating-sustainably/).
+
+### Software
+
+Training was done using [JAX](https://github.com/jax-ml/jax) and [ML Pathways](https://blog.google/technology/ai/introducing-pathways-next-generation-ai-architecture/). JAX allows researchers to take advantage of the latest generation of hardware, including TPUs, for faster and more efficient training of large models. ML Pathways is Google's latest effort to build artificially intelligent systems capable of generalizing across multiple tasks. This is specially suitable for foundation models, including large language models like these ones.  
+Together, JAX and ML Pathways are used as described in the [paper about the Gemini family of models](https://goo.gle/gemma2report); _"the 'single controller' programming model of Jax and Pathways allows a single Python process to orchestrate the entire training run, dramatically simplifying the development workflow."_
+
+## Evaluation
+
+Model evaluation metrics and results.
+
+### Benchmark Results
+
+These models were evaluated against a large collection of different datasets and metrics to cover different aspects of text generation.
+
+_PT models. XX/YY: results for PrefixLM/UL2 checkpoints._
+
+| Benchmark | Metric | 2B-2B | 9B-2B | 9B-9B | S-S | B-B | L-L | ML-ML | XL-XL |
+| :--- | :--- | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: |
+| [MMLU](https://arxiv.org/abs/2009.03300) | 5-shot, top-1 | 46.8/50.4 | 60.3/64.8 | 71.3/72.1 | 24.7/25.2 | 24.8/25.7 | 27.3/27.5 | 27.3/29.1 | 34.8/36.6 |
+| [HellaSwag](https://arxiv.org/abs/1905.07830) | 10-shot | 74.9/74.0 | 75.7/74.3 | 81.0/82.5 | 30.9/30.5 | 40.5/38.6 | 57.3/54.9 | 65.4/64.5 | 68.9/69.0 |
+| [PIQA](https://arxiv.org/abs/1911.11641) | 0-shot | 79.0/78.8 | 78.3/78.2 | 81.1/82.4 | 62.8/61.5 | 67.0/66.2 | 71.2/70.9 | 74.3/75.5 | 76.2/78.0 |
+| [BoolQ](https://arxiv.org/abs/1905.10044) | 0-shot | 75.6/77.5 | 84.6/85.1 | 85.6/87.0 | 53.1/61.1 | 52.3/49.6 | 62.2/62.3 | 62.6/61.7 | 69.9/68.0 |
+| [WinoGrande](https://arxiv.org/abs/1907.10641) | partial score | 69.5/69.8 | 68.1/58.8 | 78.7/78.2 | 52.0/50.0 | 53.9/51.6 | 58.1/56.7 | 64.6/62.4 | 64.7/65.1 |
+| [ARC-e](https://arxiv.org/abs/1911.01547) | 0-shot | 77.1/76.5 | 82.9/81.1 | 85.3/86.0 | 42.3/43.8 | 48.5/47.9 | 59.5/56.9 | 65.8/63.5 | 71.2/69.2 |
+| [ARC-c](https://arxiv.org/abs/1911.01547) | 25-shot | 52.0/53.5 | 59.9/59.6 | 65.0/66.5 | 23.0/23.4 | 25.1/25.7 | 32.7/31.5 | 41.4/40.4 | 46.5/45.9 |
+| [TriviaQA](https://arxiv.org/abs/1705.03551) | 5-shot | 51.2/51.1 | 66.2/58.3 | 75.2/73.3 | 3.2/3.3 | 7.2/5.9 | 19.4/15.9 | 33.2/25.4 | 41.0/34.3 |
+| [Natural Questions](https://github.com/google-research-datasets/natural-questions) | 5-shot | 28.4/28.3 | 37.1/33.9 | 43.1/44.0 | 7.1/7.7 | 10.8/10.9 | 15.6/15.3 | 21.5/19.6 | 23.7/21.8 |
+| [HumanEval](https://arxiv.org/abs/2107.03374) | pass@1 | 27.4/28.0 | 33.5/22.0 | 40.2/37.2 | 0.6/0.0 | 3.7/1.8 | 12.8/8.5 | 17.1/15.9 | 23.2/19.5 |
+| [MBPP](https://arxiv.org/abs/2108.07732) | 3-shot | 37.4/36.4 | 43.4/38.6 | 55.6/55.2 | 1.4/0.0 | 4.6/3.4 | 15.0/11.8 | 27/24.6 | 30.0/28.0 |
+| [GSM8K](https://arxiv.org/abs/2110.14168) | 5-shot, maj@1 | 41.7/35.8 | 48.7/39.7 | 72.8/74.0 | 2.0/0.8 | 2.2/1.5 | 6.6/4.1 | 13.7/17.5 | 25.8/22.4 |
+| [MATH-500](https://arxiv.org/abs/2103.03874) | 4-shot | 24.2/20.4 | 23.6/18.4 | 37.8/39.2 | 1.0/1.2 | 1.8/2.4 | 5.0/4.8 | 11.0/12 | 15.6/12.4 |
+| [AGIEval](https://arxiv.org/abs/2304.06364) | 3-5-shot | 35.0/37.0 | 43.6/45.7 | 53.1/56.4 | 20.8/21.4 | 21.8/21.3 | 22.5/23.0 | 23.4/24.5 | 28.0/27.4 |
+| [BIG-Bench](https://arxiv.org/abs/2206.04615) | 3-shot, CoT | 51.9/50.5 | 51.6/52.1 | 74.7/76.3 | 24.7/22.7 | 23.0/24.8 | 29.9/31.3 | 37.3/35.9 | 44.5/43.1 |
+
+## Ethics and Safety
+
+Ethics and safety evaluation approach and results.
+
+### Evaluation Approach
+
+Our evaluation methods include structured evaluations and internal red-teaming testing of relevant content policies. Red-teaming was conducted by a number of different teams, each with different goals and human evaluation metrics. These models were evaluated against a number of different categories relevant to ethics and safety, including:
+
+- **Child Safety**: Evaluation of text-to-text prompts covering child safety policies, including child sexual abuse and exploitation.
+- **Content Safety:** Evaluation of text-to-text prompts covering safety policies including, harassment, violence and gore, and hate speech.
+- **Representational Harms**: Evaluation of text-to-text prompts covering safety policies including bias, stereotyping, and harmful associations or inaccuracies.
+
+In addition to development level evaluations, we conduct "assurance evaluations" which are our ‘arms-length' internal evaluations for responsibility governance decision making. They are conducted separately from the model development team, to inform decision making about release. High level findings are fed back to the model team, but prompt sets are held-out to prevent overfitting and preserve the results' ability to inform decision making. Assurance evaluation results are reported to our Responsibility & Safety Council as part of release review.
+
+### Evaluation Results
+
+For all areas of safety testing, we saw major improvements in the categories of child safety, content safety, and representational harms relative to previous Gemma models. All testing was conducted without safety filters to evaluate the model capabilities and behaviors. For both text-to-text and image-to-text, and across all model sizes, the model produced minimal policy violations, and showed significant  improvements over previous Gemma models' performance with respect to ungrounded inferences. A limitation of our evaluations was they included only English language prompts.
+
+## Usage and Limitations
+
+These models have certain limitations that users should be aware of.
+
+### Intended Usage
+
+Open large language models (LLMs) models have a wide range of applications across various industries and domains. The following list of potential uses is not comprehensive. The purpose of this list is to provide contextual information about the possible use-cases that the model creators considered as part of model training and development.
+
+- Content Creation and Communication
+  - Text Generation: These models can be used to generate creative text formats such as poems, scripts, code, marketing copy, and email drafts.
+  - Text Summarization: Generate concise summaries of a text corpus, research papers, or reports.
+
+- Research and Education
+  - Natural Language Processing (NLP) Research: These models can serve as a foundation for researchers to experiment with NLP techniques, develop algorithms, and contribute to the advancement of the field.
+
+### Limitations
+
+- Training Data
+  - The quality and diversity of the training data significantly influence the model's capabilities. Biases or gaps in the training data can lead to limitations in the model's responses.
+  - The scope of the training dataset determines the subject areas the model can handle effectively.
+
+- Context and Task Complexity
+  - Models are better at tasks that can be framed with clear prompts and instructions. Open-ended or highly complex tasks might be challenging.
+  - A model's performance can be influenced by the amount of context provided (longer context generally leads to better outputs, up to a certain point).
+
+- Language Ambiguity and Nuance
+  - Natural language is inherently complex. Models might struggle to grasp subtle nuances, sarcasm, or figurative language.
+
+- Factual Accuracy
+  - Models generate responses based on information they learned from their training datasets, but they are not knowledge bases. They may generate incorrect or outdated factual statements.
+
+- Common Sense
+  - Models rely on statistical patterns in language. They might lack the ability to apply common sense reasoning in certain situations.
+
+### Ethical Considerations and Risks
+
+The development of large language models (LLMs) raises several ethical concerns. In creating an open model, we have carefully considered the following:
+
+- Bias and Fairness
+  - LLMs trained on large-scale, real-world text data can reflect socio-cultural biases embedded in the training material. These models underwent careful scrutiny, input data pre-processing described and posterior evaluations reported in this card.
+
+- Misinformation and Misuse
+  - LLMs can be misused to generate text that is false, misleading, or harmful.
+  - Guidelines are provided for responsible use with the model, see the [Responsible Generative AI Toolkit](https://ai.google.dev/responsible).
+
+- Transparency and Accountability:
+  - This model card summarizes details on the models' architecture, capabilities, limitations, and evaluation processes.
+  - A responsibly developed open model offers the opportunity to share innovation by making LLM technology accessible to developers and researchers across the AI ecosystem.
+
+Risks identified and mitigations:
+
+- **Perpetuation of biases**: It's encouraged to perform continuous monitoring (using evaluation metrics, human review) and the exploration of de-biasing techniques during model training, fine-tuning, and other use cases.
+- **Generation of harmful content**: Mechanisms and guidelines for content safety are essential. Developers are encouraged to exercise caution and implement appropriate content safety safeguards based on their specific product policies and application use cases.
+- **Misuse for malicious purposes**: Technical limitations and developer and end-user education can help mitigate against malicious applications of LLMs. Educational resources and reporting mechanisms for users to flag misuse are provided. Prohibited uses of Gemma models are outlined in the [Gemma Prohibited Use Policy](https://ai.google.dev/gemma/prohibited_use_policy).
+- **Privacy violations**: Models were trained on data filtered for removal of certain personal information and other sensitive data. Developers are encouraged to adhere to privacy regulations with privacy-preserving techniques.
+
+### Benefits
+
+At the time of release, this family of models provides high-performance open encoder-decoder large language model implementations designed from the ground up for Responsible AI development compared to similarly sized models.
+
+Using the benchmark evaluation metrics described in this document, these models have shown to provide superior performance to other, comparably-sized open model alternatives.
+

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+  "unk_token": "<unk>",
+  "use_default_system_prompt": false
+}