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Quantized GGUF versions of **task-arithmetic merged** ACE-Step v1.5 XL DiT models, ready for use with C++ inference engines. These are **DiT (Diffusion Transformer)** models created by merging the official ACE-Step v1.5 XL checkpoints using task arithmetic. Task arithmetic blends the learned "task vectors" of two fine-tuned models at a controlled interpolation ratio, producing models that inherit qualities from both parents. - **SFT × Turbo** blends combine SFT's strong lyric adherence and musical structure with Turbo's faster convergence and energy - **Base × Turbo** blends bring Base's raw generative range together with Turbo's efficiency - Different ratios let you dial the trade-off to taste — lower λ for more structure, higher λ for more speed

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语音合成
输入 文本→ 输出 音频
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MXFP4 quantized DiT models for ACE-Step music generation, in GGUF format. **Compatible with acestep.cpp and HOT-Step-CPP.** MXFP4 (Microscaling FP4) is a 4-bit floating point format from the Open Compute Project MX Specification. Unlike integer quantization (Q4_K_M etc.), MXFP4 stores weights as E2M1 floating point values with shared E8M0 block scales.

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A fine-tuned **AutoencoderOobleck** decoder with an intent to improve audio fidelity for the ACE-Step 1.5 music generation pipeline. Drop-in compatible with all existing ACE-Step DiT checkpoints. ACE-Step 1.5 uses a VAE (Variational Autoencoder) to convert between audio waveforms and the latent space that the DiT diffusion model operates in. The original VAE decoder attenuates high-frequency content, resulting in audio with reduced clarity and detail above 6kHz. ScragVAE retrains the decoder half of the VAE to better reconstruct upper harmonics, transient detail, and spectral "air" — while keeping the encoder frozen so all existing DiT models remain fully compatible.

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音频识别与处理
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基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。

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GGUF conversion of MiniMaxAI/MiniMax-Music3 for **HOT-Step CPP**, a fully local desktop app for AI music generation with a native C++/GGML engine. As far as we know this is the first GGUF conversion of this model. These files are downloaded automatically by HOT-Step's Model Manager when the MiniMax-Music3 backend is selected. They are **not** usable with llama.cpp alone: `mm3-lm-*.gguf` is structurally a Qwen3 GGUF, but music generation requires the full five-module pipeline (LM → RVQ depth decoder → condition encoder → flow-matching DiT → vocoder) implemented in HOT-Step's engine. Since 2026-08-14 the repo carries **one file per pipeline component**, so each can be picked at its own quantisation and swapped without re-downloading the others:

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GGUF conversion of OpenMOSS-Team/MOSS-Music-8B-Instruct for **HOT-Step CPP**, a fully local desktop app for AI music generation with a native C++/GGML engine. As far as we know this is the first GGUF conversion of this model. Unlike everything else HOT-Step ships, this model **analyses** audio rather than generating it. It listens to a track and writes a caption describing what it hears — used in HOT-Step's Training Studio to caption your own dataset locally, with no API key and nothing leaving the machine. These files are downloaded automatically by HOT-Step's Model Manager. They are **not** usable with llama.cpp alone: `moss-lm-*.gguf` is structurally a Qwen3 GGUF, but captioning requires the audio tower in `moss-aud-f16.gguf` and the deepstack injection wiring implemented in HOT-Step's engine.

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音乐生成开放权重
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YuE2-GGUF

scragnog

GGUF conversions of m-a-p/YuE2-3B and m-a-p/YuE2-Vae for HOT-Step CPP, a local C++/GGML music generation engine. YuE2 is an autoregressive-planner + flow-matching music model from the M-A-P team; these files let it run through HOT-Step's GGML inference stack instead of the original PyTorch checkpoint. - Size: 3,960,938,800 bytes. - SHA256: `96fe199377309001ed8cd26a944baeee8cc31a20ba7c36d1d3c0a7e1f4149db6`. - No changes to the upstream checkpoint; the upstream CC BY-NC 4.0 licence applies. See LICENSE. All model weights here are derived from the original YuE2 release. Credit and thanks to the YuE2 authors (M-A-P) for the underlying research and checkpoints:

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COST EXPLORER

好模型，也要算好成本。

费用分析

调整月度 Token 用量，直观比较不同模型的预估 API 开支。

1 M = 100 万 Token · 美元计价
仅比较同一渠道的基础文本 Token 报价

使用已采集且单位可比的 USD / 百万 Token 渠道报价。报价为采集时快照；不计缓存优惠、阶梯价、推理额外输出与税费，请以来源的当前报价为准。

MODEL HANDBOOK

读懂模型，再做选择

选型与常见问题
API 价格应该怎么比较？+

先区分输入、输出和缓存价格，再使用同一任务和用量估算总费用。图像、音频和视频可能按张、时长或分辨率计费，不能直接与 Token 单价比较。

开放权重，就代表可以免费商用吗？+

不一定。必须逐一核对具体模型版本的许可证、商用限制及使用政策。自己部署还需要考虑显存、推理资源和运维费用。

排行榜第一，就是最适合我的模型吗？+

榜单取决于测试任务、版本与评测方法。建议用实际业务样本比较准确性、时延、稳定性和成本，再选择模型。

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