It is a fine-tuned version of Indic Parler-TTS Pretrained, trained on a **1,806 hours** multilingual Indic and English dataset. **Indic Parler-TTS Mini** can officially speak in 20 Indic languages, making it comprehensive for regional language technologies, and in English. The **21 languages** supported are: Assamese, Bengali, Bodo, Dogri, English, Gujarati, Hindi, Kannada, Konkani, Maithili, Malayalam, Manipuri, Marathi, Nepali, Odia, Sanskrit, Santali, Sindhi, Tamil, Telugu, and Urdu. Thanks to its **better prompt tokenizer**, it can easily be extended to other languages. This tokenizer has a larger vocabulary and handles byte fallback, which simplifies multilingual training.
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A DFlash 2 draft model for prism-ml/Ternary-Bonsai-2-27B (PrismML's 2-bit, Hadamard-rotated Qwen3.8-27B). It is z-lab/Qwen3.8-27B-DFlash2 fine-tuned on the ternary model's own hidden states and its own greedy generations, so the drafter predicts what the 2-bit target will actually say rather than what the bf16 base would have said. Same architecture and config as the z-lab checkpoint (5 Qwen3-style layers, hidden 5120, 32/8 heads, sliding window 2048, `block_size 8`, taps at target layers 5/19/33/47/61, rank-256 candidate selector, top-k 16, `mask_token_id 248070`); shares the target's embeddings and `lm_head`, so there are no `lm_head`/embedding tensors here. 81 bf16 tensors, 3.85 GB. `config.json` is the z-lab file, unchanged. Runtime: NakliTechie/dflash-mlx-bonsai2 (Apple Silicon, MLX). Status: experimental; measured on one M4 Pro.
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