parakeet-tdt-0.6b-v3 β€” MLX 4-bit

NVIDIA Parakeet TDT 0.6B v3, in the MLX layout of mlx-community/parakeet-tdt-0.6b-v3, quantized to 4-bit for on-device speech-to-text on Apple silicon. Multilingual: 25 European languages.

What changed from the source

This is a redistribution of a modified model. Starting from the fp32 weights of mlx-community/parakeet-tdt-0.6b-v3:

  • every Linear and Embedding layer (attention and feed-forward projections, the pre-encode and joint projections, the prediction-network embedding β€” 221 modules) is quantized with mlx.nn.quantize to 4-bit, group size 64 (affine);
  • every other tensor (convolutions, LSTM, normalization) is stored as bfloat16;
  • config.json gains "quantization": {"group_size": 64, "bits": 4}.

Tensor names are unchanged from the source checkpoint, so loaders that read the mlx-community layout and honour the quantization block (quantizing the modules whose weights arrive with .scales) load it directly.

Files

  • model.safetensors β€” quantized weights (472 MB, vs 2.5 GB fp32)
  • config.json β€” the NeMo config with the quantization block
  • vocab.txt, tokenizer.model, tokenizer.vocab β€” unchanged from the source

Accuracy

Measured on 1,000 Russian clips (500 FLEURS, 500 GOLOS), greedy decode, on an M4 Mac:

build download FLEURS WER GOLOS WER speed
fp32 (mlx-community/parakeet-tdt-0.6b-v3) 2.5 GB 5.57 % 3.42 % 64Γ—
8-bit (transcriber-app/parakeet-tdt-0.6b-v3-mlx-8bit) 744 MB 5.67 % 3.46 % 90Γ—
6-bit (transcriber-app/parakeet-tdt-0.6b-v3-mlx-6bit) 608 MB 5.61 % 3.38 % 88Γ—
5-bit (not published) 540 MB 5.93 % 3.50 % 87Γ—
4-bit (transcriber-app/parakeet-tdt-0.6b-v3-mlx-4bit) 472 MB 5.95 % 3.67 % 90Γ—

4-bit costs about a third of a WER point against fp32. 6-bit is the smallest build that keeps fp32 accuracy.

Credits and licence

Other builds: 8-bit, 6-bit.

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