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README.md CHANGED
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- ---
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- license: mit
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- ---
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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+ ---
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+ library_name: transformers
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+ license: mit
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+ pipeline_tag: text-to-audio
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+ tags:
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+ - feature-extraction
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+ - audio
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+ - music
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+ - text2music
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+ - custom_code
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+ ---
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+
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+ <h1 align="center">ACE-Step 1.5 XL — Turbo (4B DiT)</h1>
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+ <p align="center">
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+ <a href="https://ace-step.github.io/ace-step-v1.5.github.io/">Project</a> |
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+ <a href="https://huggingface.co/collections/ACE-Step/ace-step-15">Hugging Face</a> |
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+ <a href="https://huggingface.co/spaces/ACE-Step/Ace-Step-v1.5">Space Demo</a> |
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+ <a href="https://discord.gg/PeWDxrkdj7">Discord</a> |
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+ <a href="https://arxiv.org/abs/2602.00744">Tech Report</a>
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+ </p>
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+
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+ > ⚠️ **Early Release:** This turbo model is still under evaluation. Quality may improve in future updates.
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+
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+ ## Model Details
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+
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+ This is the **XL (4B) Turbo** variant of ACE-Step 1.5 — a distillation-accelerated model that generates high-quality audio in just 8 steps. Combines the speed of turbo with the quality of the 4B architecture.
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+
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+ ### XL Architecture
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+
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+ | Parameter | Value |
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+ |-----------|-------|
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+ | DiT Decoder hidden_size | 2560 |
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+ | DiT Decoder layers | 32 |
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+ | DiT Decoder attention heads | 32 |
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+ | Encoder hidden_size | 2048 |
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+ | Encoder layers | 8 |
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+ | Total params | ~4B |
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+ | Weights size (bf16) | ~18.8 GB |
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+ | Inference steps | 8 (no CFG, distilled) |
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+
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+ ### GPU Requirements
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+
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+ | VRAM | Support |
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+ |------|---------|
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+ | ≥12 GB | With CPU offload + INT8 quantization |
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+ | ≥16 GB | With CPU offload |
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+ | ≥20 GB | Without offload (recommended) |
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+ | ≥24 GB | Full quality (XL + 4B LM) |
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+
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+ All LM models (0.6B / 1.7B / 4B) are fully compatible with XL.
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+
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+ ### Key Features
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+
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+ - **💰 Commercial-Ready:** Trained on legally compliant datasets. Generated music can be used for commercial purposes.
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+ - **📚 Safe Training Data:** Licensed music, royalty-free/public domain, and synthetic (MIDI-to-Audio) data.
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+ - **⚡ Fast:** 8-step inference — the fastest XL variant.
57
+ - **🔮 Higher Quality:** 4B parameters provide richer audio quality than 2B turbo.
58
+
59
+ ## Quick Start
60
+
61
+ ```bash
62
+ # Install ACE-Step
63
+ git clone https://github.com/ace-step/ACE-Step-1.5.git
64
+ cd ACE-Step-1.5
65
+ pip install -e .
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+
67
+ # Download this model
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+ huggingface-cli download ACE-Step/acestep-v15-xl-turbo --local-dir ./checkpoints/acestep-v15-xl-turbo
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+
70
+ # Run with Gradio UI
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+ python acestep --config-path acestep-v15-xl-turbo
72
+ ```
73
+
74
+ ## Model Zoo
75
+
76
+ ### XL (4B) DiT Models
77
+
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+ | DiT Model | CFG | Steps | Quality | Diversity | Tasks | Hugging Face |
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+ |-----------|:---:|:-----:|:-------:|:---------:|-------|--------------|
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+ | `acestep-v15-xl-base` | ✅ | 50 | High | High | All (extract, lego, complete) | [Link](https://huggingface.co/ACE-Step/acestep-v15-xl-base) |
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+ | `acestep-v15-xl-sft` | ✅ | 50 | Very High | Medium | Standard | [Link](https://huggingface.co/ACE-Step/acestep-v15-xl-sft) |
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+ | **`acestep-v15-xl-turbo`** | ❌ | 8 | Very High | Medium | Standard | This repo |
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+
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+ ### LM Models (all compatible with XL)
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+
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+ | LM Model | Params | Audio Understanding | Composition | Hugging Face |
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+ |----------|:------:|:-------------------:|:-----------:|--------------|
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+ | `acestep-5Hz-lm-0.6B` | 0.6B | Medium | Medium | [Link](https://huggingface.co/ACE-Step/acestep-5Hz-lm-0.6B) |
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+ | `acestep-5Hz-lm-1.7B` | 1.7B | Medium | Medium | Included in main |
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+ | `acestep-5Hz-lm-4B` | 4B | Strong | Strong | [Link](https://huggingface.co/ACE-Step/acestep-5Hz-lm-4B) |
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+
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+ ## Acknowledgements
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+
94
+ This project is co-led by ACE Studio and StepFun.
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+
96
+ ## Citation
97
+
98
+ ```BibTeX
99
+ @misc{gong2026acestep,
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+ title={ACE-Step 1.5: Pushing the Boundaries of Open-Source Music Generation},
101
+ author={Junmin Gong, Yulin Song, Wenxiao Zhao, Sen Wang, Shengyuan Xu, Jing Guo},
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+ howpublished={\url{https://github.com/ace-step/ACE-Step-1.5}},
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+ year={2026},
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+ note={GitHub repository}
105
+ }
106
+ ```
config.json ADDED
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+ {
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+ "architectures": [
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+ "AceStepConditionGenerationModel"
4
+ ],
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+ "auto_map": {
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+ "AutoConfig": "configuration_acestep_v15.AceStepConfig",
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+ "AutoModel": "modeling_acestep_v15_xl_turbo.AceStepConditionGenerationModel"
8
+ },
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+ "attention_bias": false,
10
+ "attention_dropout": 0.0,
11
+ "audio_acoustic_hidden_dim": 64,
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+ "data_proportion": 0.5,
13
+ "dtype": "bfloat16",
14
+ "encoder_hidden_size": 2048,
15
+ "encoder_intermediate_size": 6144,
16
+ "encoder_num_attention_heads": 16,
17
+ "encoder_num_key_value_heads": 8,
18
+ "fsq_dim": 2048,
19
+ "fsq_input_levels": [
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+ 8,
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+ 8,
22
+ 8,
23
+ 5,
24
+ 5,
25
+ 5
26
+ ],
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+ "fsq_input_num_quantizers": 1,
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+ "head_dim": 128,
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+ "hidden_act": "silu",
30
+ "hidden_size": 2560,
31
+ "in_channels": 192,
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+ "initializer_range": 0.02,
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+ "intermediate_size": 9728,
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+ "layer_types": [
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+ "sliding_attention",
36
+ "full_attention",
37
+ "sliding_attention",
38
+ "full_attention",
39
+ "sliding_attention",
40
+ "full_attention",
41
+ "sliding_attention",
42
+ "full_attention",
43
+ "sliding_attention",
44
+ "full_attention",
45
+ "sliding_attention",
46
+ "full_attention",
47
+ "sliding_attention",
48
+ "full_attention",
49
+ "sliding_attention",
50
+ "full_attention",
51
+ "sliding_attention",
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+ "full_attention",
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+ "sliding_attention",
54
+ "full_attention",
55
+ "sliding_attention",
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+ "full_attention",
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+ "sliding_attention",
58
+ "full_attention",
59
+ "sliding_attention",
60
+ "full_attention",
61
+ "sliding_attention",
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+ "full_attention",
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+ "sliding_attention",
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+ "full_attention",
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+ "sliding_attention",
66
+ "full_attention"
67
+ ],
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+ "max_position_embeddings": 32768,
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+ "model_type": "acestep",
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+ "model_version": "turbo",
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+ "is_turbo": true,
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+ "num_attention_heads": 32,
73
+ "num_attention_pooler_hidden_layers": 2,
74
+ "num_audio_decoder_hidden_layers": 24,
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+ "num_hidden_layers": 32,
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+ "num_key_value_heads": 8,
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+ "num_lyric_encoder_hidden_layers": 8,
78
+ "num_timbre_encoder_hidden_layers": 4,
79
+ "patch_size": 2,
80
+ "pool_window_size": 5,
81
+ "rms_norm_eps": 1e-06,
82
+ "rope_scaling": null,
83
+ "rope_theta": 1000000,
84
+ "sliding_window": 128,
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+ "text_hidden_dim": 1024,
86
+ "timbre_fix_frame": 750,
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+ "timbre_hidden_dim": 64,
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+ "timestep_mu": -0.4,
89
+ "timestep_sigma": 1.0,
90
+ "transformers_version": "4.57.0",
91
+ "use_cache": true,
92
+ "use_sliding_window": true,
93
+ "vocab_size": 64003,
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+ "lyric_alignment_layers_config": {"3": [18, 27], "4": [22], "5": [5, 6, 7], "6": [2, 12, 13], "7": [20, 21]}
95
+ }
configuration_acestep_v15.py ADDED
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+ # coding=utf-8
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+ # Copyright 2024 The Qwen team, Alibaba Group and the HuggingFace Inc. team. All rights reserved.
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+ #
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+ # Licensed under the Apache License, Version 2.0 (the "License");
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+ # you may not use this file except in compliance with the License.
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+ # You may obtain a copy of the License at
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+ #
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+ # http://www.apache.org/licenses/LICENSE-2.0
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+ #
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+ # Unless required by applicable law or agreed to in writing, software
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+ # distributed under the License is distributed on an "AS IS" BASIS,
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+ # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
13
+ # See the License for the specific language governing permissions and
14
+ # limitations under the License.
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+ """AceStep model configuration"""
16
+
17
+ from transformers.configuration_utils import PretrainedConfig, layer_type_validation
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+ from transformers.modeling_rope_utils import rope_config_validation
19
+ from transformers.utils import logging
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+
21
+
22
+ logger = logging.get_logger(__name__)
23
+
24
+
25
+ class AceStepConfig(PretrainedConfig):
26
+ r"""
27
+ This is the configuration class to store the configuration of a [`AceStepModel`]. It is used to instantiate an
28
+ AceStep model according to the specified arguments, defining the model architecture.
29
+
30
+ Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
31
+ documentation from [`PretrainedConfig`] for more information.
32
+
33
+ Args:
34
+ vocab_size (`int`, *optional*, defaults to 64003):
35
+ Vocabulary size of the AceStep model. Defines the number of different tokens that can be represented by the
36
+ `inputs_ids` passed when calling the model.
37
+ hidden_size (`int`, *optional*, defaults to 4096):
38
+ Dimension of the hidden representations.
39
+ intermediate_size (`int`, *optional*, defaults to 22016):
40
+ Dimension of the MLP representations.
41
+ num_hidden_layers (`int`, *optional*, defaults to 32):
42
+ Number of hidden layers in the Transformer encoder.
43
+ num_attention_heads (`int`, *optional*, defaults to 32):
44
+ Number of attention heads for each attention layer in the Transformer encoder.
45
+ num_key_value_heads (`int`, *optional*, defaults to 32):
46
+ This is the number of key_value heads that should be used to implement Grouped Query Attention. If
47
+ `num_key_value_heads=num_attention_heads`, the model will use Multi Head Attention (MHA), if
48
+ `num_key_value_heads=1` the model will use Multi Query Attention (MQA) otherwise GQA is used. When
49
+ converting a multi-head checkpoint to a GQA checkpoint, each group key and value head should be constructed
50
+ by meanpooling all the original heads within that group. For more details, check out [this
51
+ paper](https://huggingface.co/papers/2305.13245). If it is not specified, will default to `32`.
52
+ head_dim (`int`, *optional*, defaults to 128):
53
+ The attention head dimension.
54
+ hidden_act (`str` or `function`, *optional*, defaults to `"silu"`):
55
+ The non-linear activation function (function or string) in the decoder.
56
+ max_position_embeddings (`int`, *optional*, defaults to 32768):
57
+ The maximum sequence length that this model might ever be used with.
58
+ initializer_range (`float`, *optional*, defaults to 0.02):
59
+ The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
60
+ rms_norm_eps (`float`, *optional*, defaults to 1e-06):
61
+ The epsilon used by the rms normalization layers.
62
+ use_cache (`bool`, *optional*, defaults to `True`):
63
+ Whether or not the model should return the last key/values attentions (not used by all models). Only
64
+ relevant if `config.is_decoder=True`.
65
+ tie_word_embeddings (`bool`, *optional*, defaults to `False`):
66
+ Whether the model's input and output word embeddings should be tied.
67
+ rope_theta (`float`, *optional*, defaults to 10000.0):
68
+ The base period of the RoPE embeddings.
69
+ rope_scaling (`Dict`, *optional*):
70
+ Dictionary containing the scaling configuration for the RoPE embeddings. NOTE: if you apply new rope type
71
+ and you expect the model to work on longer `max_position_embeddings`, we recommend you to update this value
72
+ accordingly.
73
+ Expected contents:
74
+ `rope_type` (`str`):
75
+ The sub-variant of RoPE to use. Can be one of ['default', 'linear', 'dynamic', 'yarn', 'longrope',
76
+ 'llama3'], with 'default' being the original RoPE implementation.
77
+ `factor` (`float`, *optional*):
78
+ Used with all rope types except 'default'. The scaling factor to apply to the RoPE embeddings. In
79
+ most scaling types, a `factor` of x will enable the model to handle sequences of length x *
80
+ original maximum pre-trained length.
81
+ `original_max_position_embeddings` (`int`, *optional*):
82
+ Used with 'dynamic', 'longrope' and 'llama3'. The original max position embeddings used during
83
+ pretraining.
84
+ `attention_factor` (`float`, *optional*):
85
+ Used with 'yarn' and 'longrope'. The scaling factor to be applied on the attention
86
+ computation. If unspecified, it defaults to value recommended by the implementation, using the
87
+ `factor` field to infer the suggested value.
88
+ `beta_fast` (`float`, *optional*):
89
+ Only used with 'yarn'. Parameter to set the boundary for extrapolation (only) in the linear
90
+ ramp function. If unspecified, it defaults to 32.
91
+ `beta_slow` (`float`, *optional*):
92
+ Only used with 'yarn'. Parameter to set the boundary for interpolation (only) in the linear
93
+ ramp function. If unspecified, it defaults to 1.
94
+ `short_factor` (`list[float]`, *optional*):
95
+ Only used with 'longrope'. The scaling factor to be applied to short contexts (<
96
+ `original_max_position_embeddings`). Must be a list of numbers with the same length as the hidden
97
+ size divided by the number of attention heads divided by 2
98
+ `long_factor` (`list[float]`, *optional*):
99
+ Only used with 'longrope'. The scaling factor to be applied to long contexts (<
100
+ `original_max_position_embeddings`). Must be a list of numbers with the same length as the hidden
101
+ size divided by the number of attention heads divided by 2
102
+ `low_freq_factor` (`float`, *optional*):
103
+ Only used with 'llama3'. Scaling factor applied to low frequency components of the RoPE
104
+ `high_freq_factor` (`float`, *optional*):
105
+ Only used with 'llama3'. Scaling factor applied to high frequency components of the RoPE
106
+ attention_bias (`bool`, defaults to `False`, *optional*, defaults to `False`):
107
+ Whether to use a bias in the query, key, value and output projection layers during self-attention.
108
+ use_sliding_window (`bool`, *optional*, defaults to `False`):
109
+ Whether to use sliding window attention.
110
+ sliding_window (`int`, *optional*, defaults to 4096):
111
+ Sliding window attention (SWA) window size. If not specified, will default to `4096`.
112
+ layer_types (`list`, *optional*):
113
+ Attention pattern for each layer.
114
+ attention_dropout (`float`, *optional*, defaults to 0.0):
115
+ The dropout ratio for the attention probabilities.
116
+
117
+ ```python
118
+ >>> from acestep.models import AceStepConfig
119
+
120
+ >>> # Initializing an AceStep configuration
121
+ >>> configuration = AceStepConfig()
122
+
123
+ >>> # Initializing a model from the configuration
124
+ >>> model = AceStepConditionGenerationModel(configuration)
125
+
126
+ >>> # Accessing the model configuration
127
+ >>> configuration = model.config
128
+ ```"""
129
+
130
+ model_type = "acestep"
131
+ keys_to_ignore_at_inference = ["past_key_values"]
132
+
133
+ # Default tensor parallel plan for the base model
134
+ base_model_tp_plan = {
135
+ "layers.*.self_attn.q_proj": "colwise",
136
+ "layers.*.self_attn.k_proj": "colwise",
137
+ "layers.*.self_attn.v_proj": "colwise",
138
+ "layers.*.self_attn.o_proj": "rowwise",
139
+ "layers.*.mlp.gate_proj": "colwise",
140
+ "layers.*.mlp.up_proj": "colwise",
141
+ "layers.*.mlp.down_proj": "rowwise",
142
+ }
143
+ base_model_pp_plan = {
144
+ "embed_tokens": (["input_ids"], ["inputs_embeds"]),
145
+ "layers": (["hidden_states", "attention_mask"], ["hidden_states"]),
146
+ "norm": (["hidden_states"], ["hidden_states"]),
147
+ }
148
+ def __init__(
149
+ self,
150
+ vocab_size=64003,
151
+ fsq_dim=2048,
152
+ fsq_input_levels=[8, 8, 8, 5, 5, 5],
153
+ fsq_input_num_quantizers=1,
154
+ hidden_size=2048,
155
+ intermediate_size=6144,
156
+ num_hidden_layers=24,
157
+ num_attention_heads=16,
158
+ num_key_value_heads=8,
159
+ head_dim=128,
160
+ hidden_act="silu",
161
+ max_position_embeddings=32768,
162
+ initializer_range=0.02,
163
+ rms_norm_eps=1e-6,
164
+ use_cache=True,
165
+ tie_word_embeddings=True,
166
+ rope_theta=1000000,
167
+ rope_scaling=None,
168
+ attention_bias=False,
169
+ use_sliding_window=True,
170
+ sliding_window=128,
171
+ layer_types=None,
172
+ attention_dropout=0.0,
173
+ num_lyric_encoder_hidden_layers=8,
174
+ audio_acoustic_hidden_dim=64,
175
+ pool_window_size=5,
176
+ text_hidden_dim=1024,
177
+ in_channels=192,
178
+ data_proportion=0.5,
179
+ timestep_mu=-0.4,
180
+ timestep_sigma=1.0,
181
+ timbre_hidden_dim=64,
182
+ num_timbre_encoder_hidden_layers=4,
183
+ timbre_fix_frame=750,
184
+ patch_size=2,
185
+ num_attention_pooler_hidden_layers=2,
186
+ num_audio_decoder_hidden_layers=24,
187
+ lyric_alignment_layers_config=None,
188
+ model_version="turbo",
189
+ **kwargs,
190
+ ):
191
+ self.max_position_embeddings = max_position_embeddings
192
+ self.hidden_size = hidden_size
193
+ self.intermediate_size = intermediate_size
194
+ self.num_hidden_layers = num_hidden_layers
195
+ self.num_attention_heads = num_attention_heads
196
+ self.use_sliding_window = use_sliding_window
197
+ self.sliding_window = sliding_window if self.use_sliding_window else None
198
+
199
+ # Text encoder configuration
200
+ self.text_hidden_dim = text_hidden_dim
201
+
202
+ # Lyric encoder configuration
203
+ self.num_lyric_encoder_hidden_layers = num_lyric_encoder_hidden_layers
204
+ self.patch_size = patch_size
205
+
206
+ # Audio semantic token generation configuration
207
+ self.audio_acoustic_hidden_dim = audio_acoustic_hidden_dim
208
+ self.pool_window_size = pool_window_size
209
+ self.in_channels = in_channels
210
+ self.data_proportion = data_proportion
211
+ self.timestep_mu = timestep_mu
212
+ self.timestep_sigma = timestep_sigma
213
+
214
+ # FSQ (Finite Scalar Quantization) configuration
215
+ self.fsq_dim = fsq_dim
216
+ self.fsq_input_levels = fsq_input_levels
217
+ self.fsq_input_num_quantizers = fsq_input_num_quantizers
218
+
219
+ # Timbre encoder configuration
220
+ self.timbre_hidden_dim = timbre_hidden_dim
221
+ self.num_timbre_encoder_hidden_layers = num_timbre_encoder_hidden_layers
222
+ self.timbre_fix_frame = timbre_fix_frame
223
+ self.num_attention_pooler_hidden_layers = num_attention_pooler_hidden_layers
224
+ self.num_audio_decoder_hidden_layers = num_audio_decoder_hidden_layers
225
+ self.lyric_alignment_layers_config = lyric_alignment_layers_config
226
+ self.vocab_size = vocab_size
227
+
228
+ # Backward compatibility: ensure num_key_value_heads is set
229
+ if num_key_value_heads is None:
230
+ num_key_value_heads = num_attention_heads
231
+
232
+ self.num_key_value_heads = num_key_value_heads
233
+ self.head_dim = head_dim
234
+ self.hidden_act = hidden_act
235
+ self.initializer_range = initializer_range
236
+ self.rms_norm_eps = rms_norm_eps
237
+ self.use_cache = use_cache
238
+ self.rope_theta = rope_theta
239
+ self.rope_scaling = rope_scaling
240
+ self.attention_bias = attention_bias
241
+ self.attention_dropout = attention_dropout
242
+ self.model_version = model_version
243
+
244
+ # Validate rotary position embeddings parameters
245
+ # Backward compatibility: if there is a 'type' field, move it to 'rope_type'
246
+ if self.rope_scaling is not None and "type" in self.rope_scaling:
247
+ self.rope_scaling["rope_type"] = self.rope_scaling["type"]
248
+ rope_config_validation(self)
249
+
250
+ self.layer_types = layer_types
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+
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+ # Set default layer types if not specified
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+ if self.layer_types is None:
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+ self.layer_types = [
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+ "sliding_attention" if bool((i + 1) % 2) else "full_attention" for i in range(self.num_hidden_layers)
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+ ]
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+ layer_type_validation(self.layer_types)
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+
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+ super().__init__(
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+ tie_word_embeddings=tie_word_embeddings,
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+ **kwargs,
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+ )
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+
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+
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+ __all__ = ["AceStepConfig"]
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