ModelPublicadoExterno

Parakeet Tdt 0.6b v2 Mlx 6bit

6-bit MLX quantization of nvidia/parakeet-tdt-0.6b-v2, built from mlx-community/parakeet-tdt-0.6b-v2 via mlx.nn.quantize(bits=6, groupsize=64) (nn.Linear and nn.Embedding only; Conv1d, BatchNorm, and the TDT...

Fuentehuggingface.co/spokedotso/parakeet-tdt-0.6b-v2-mlx-6bit/tree/bb14d7a6da6b61e83eb4c20e8c27005d2484f5c7

TipoOtherModelo basenvidia/parakeet-tdt-0.6b-v2VersiónRevision bb14d7a

Fuente del modelo

Tipo: Other
Tamaño del archivo: 573 MB
Modelo base: nvidia/parakeet-tdt-0.6b-v2
Versión: vRevision bb14d7a
Actualizado: 22 sept 2026
Descargas: 12
Fuente: Hugging Face

Descripción de la fuente

Parakeet TDT 0.6b v2, MLX 6-bit

6-bit MLX quantization of nvidia/parakeet-tdt-0.6b-v2, built from mlx-community/parakeet-tdt-0.6b-v2 via mlx.nn.quantize(bits=6, group_size=64) (nn.Linear and nn.Embedding only; Conv1d, BatchNorm, and the TDT prediction LSTM stay bf16). English-only.

Benchmarks (LibriSpeech test-clean, 250 samples, M4): WER 1.87% (identical to bf16), RTF 0.022, peak MLX memory 1.13 GB.

Loading requires quantizing the module tree before loading weights; parakeet_mlx.from_pretrained alone will not load this checkpoint:

import json, mlx.nn as nn
from parakeet_mlx.utils import from_config

model = from_config(json.load(open("config.json")))
nn.quantize(model, group_size=64, bits=6)
model.load_weights("model.safetensors")

mel_filters.npy is the precomputed slaney mel filterbank (librosa.filters.mel(sr=16000, n_fft=512, n_mels=128, fmin=0, fmax=8000, norm="slaney")), shipped so inference environments can stub librosa out entirely.

Quantized for Spoke local dictation.

Fuentes

1 fuente

Hugging FacePrincipalVersión bb14d7a6da6b61e83eb4c20e8c27005d2484f5c7

huggingface.co/spokedotso/parakeet-tdt-0.6b-v2-mlx-6bit/tree/bb14d7a6da6b61e83eb4c20e8c27005d2484f5c7

Verificado 22 sept

Artefactos del modelo

1 artefacto

model.safetensors

safetensors · 573 MB · SHA-256 e2437f570fd5…d14d · Hugging Face

Descargar

Extractos de fuentes

2 extractos

Readme22 septhuggingface.co/spokedotso/parakeet-tdt-0.6b-v2-mlx-6bit

--- license: cc-by-4.0 base_model: nvidia/parakeet-tdt-0.6b-v2 tags: - mlx - automatic-speech-recognition --- # Parakeet TDT 0.6b v2, MLX 6-bit 6-bit MLX quantization of [nvidia/parakeet-tdt-0.6b-v2](https://huggingface.co/nvidia/parakeet-tdt-0.6b-v2), built from [mlx-community/parakeet-tdt-0.6b-v2](https://huggingface.co/mlx-community/parakeet-tdt-0.6b-v2) via `mlx.nn.quantize(bits=6, group_size=64)` (nn.Linear and nn.Embedding only; Conv1d, BatchNorm, and the TDT prediction LSTM stay bf16). English-only. Benchmarks (LibriSpeech test-clean, 250 samples, M4): WER 1.87% (identical to bf16), RTF 0.022, peak MLX memory 1.13 GB. Loading requires quantizing the module tree before loading weights; `parakeet_mlx.from_pretrained` alone will not load this checkpoint: ```python import json, mlx.nn as nn from parakeet_mlx.utils import from_config model = from_config(json.load(open("config.json"))) nn.quantize(model, group_size=64, bits=6) model.load_weights("model.safetensors") ``` `mel_filters.npy` is the precomputed slaney mel filterbank (`librosa.filters.mel(sr=16000, n_fft=512, n_mels=128, fmin=0, fmax=8000, norm="slaney")`), shipped so inference environments can stub librosa out entirely. Quantized for [Spoke](https://spoke.so) local dictation.

Note22 septhuggingface.co/spokedotso/parakeet-tdt-0.6b-v2-mlx-6bit

Source context: 12 downloads · 0 likes · Pipeline automatic-speech-recognition · Library mlx · Repo spokedotso/parakeet-tdt-0.6b-v2-mlx-6bit