MODEL PROFILE

tada-tts-3b-ml-GGUF

cstr · tada-tts-3b-ml-GGUF

TADA-3B-ML is a multilingual text-to-speech model built on Meta Llama 3.2 3B with a flow-matching (FM) speech decoder and custom Hume codec. Key property: **1:1 token alignment** — every text token maps to exactly one speech vector, eliminating transcript hallucination. Supports 10 languages (en, es, ja, zh, de, fr, it, pt, ko, ar). 24 kHz mono output. The Q4_K file uses a TADA-aware quantization policy (tail=14): large transformer block matrices are quantized, while the last 14 token-embedding rows and all `tada.*` flow-matching tensors are kept at F16. This preserves the timing and acoustic conditioning paths where quantization noise matters most. For near-lossless quality use `tada-tts-3b-ml-q8_0.gguf`; for the F16 reference quality use `tada-tts-3b-ml-f16.gguf`.

Speech synthesislicense:llama3.2
Context windowNot disclosed
Input → OutputText → Audio
Model weightsNot verified
Profile updated2026/11/2 19:15:39
OVERVIEW

Model overview

TADA-3B-ML is a multilingual text-to-speech model built on Meta Llama 3.2 3B with a flow-matching (FM) speech decoder and custom Hume codec. Key property: **1:1 token alignment** — every text token maps to exactly one speech vector, eliminating transcript hallucination. Supports 10 languages (en, es, ja, zh, de, fr, it, pt, ko, ar). 24 kHz mono output. The Q4_K file uses a TADA-aware quantization policy (tail=14): large transformer block matrices are quantized, while the last 14 token-embedding rows and all `tada.*` flow-matching tensors are kept at F16. This preserves the timing and acoustic conditioning paths where quantization noise matters most. For near-lossless quality use `tada-tts-3b-ml-q8_0.gguf`; for the F16 reference quality use `tada-tts-3b-ml-f16.gguf`.

Key features

特点摘要待依据来源补齐,不凭模型名称推断能力。

Limitations & considerations

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PRICING & COST

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INDEPENDENT BENCHMARKS

Public benchmarks & run configuration

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Frequently asked questions

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Sources & traceability

许可证:llama3.2。模型介绍与第三方评测均以原来源的具体版本和方法为准。