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Parakeet Tdt 0.6b v2 Mlx 8bit

8-bit MLX quantization of nvidia/parakeet-tdt-0.6b-v2, built from mlx-community/parakeet-tdt-0.6b-v2 via mlx.nn.quantize(bits=8, groupsize=64) (nn.Linear and nn.Embedding only; Conv1d, BatchNorm, and the TDT...

Sourcehuggingface.co/spokedotso/parakeet-tdt-0.6b-v2-mlx-8bit/tree/29c2904d71424486b18b0325ef888b334f6a5983

KindOtherBase modelnvidia/parakeet-tdt-0.6b-v2VersionRevision 29c2904

Model source

Kind: Other
File size: 700 MB
Base model: nvidia/parakeet-tdt-0.6b-v2
Version: vRevision 29c2904
Updated: Sep 22, 2026
Downloads: 20
Source: Hugging Face

Source description

Parakeet TDT 0.6b v2, MLX 8-bit

8-bit MLX quantization of nvidia/parakeet-tdt-0.6b-v2, built from mlx-community/parakeet-tdt-0.6b-v2 via mlx.nn.quantize(bits=8, 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 1.91% (bf16: 1.87%), RTF 0.024, peak MLX memory 1.26 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=8)
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.

Sources

1 source

Hugging FacePrimaryVersion 29c2904d71424486b18b0325ef888b334f6a5983

huggingface.co/spokedotso/parakeet-tdt-0.6b-v2-mlx-8bit/tree/29c2904d71424486b18b0325ef888b334f6a5983

Verified Sep 22

Model artifacts

1 artifact

model.safetensors

safetensors · 700 MB · SHA-256 70b58526218a…fe72 · Hugging Face

Download

Source excerpts

2 excerpts

ReadmeSep 22huggingface.co/spokedotso/parakeet-tdt-0.6b-v2-mlx-8bit

--- license: cc-by-4.0 base_model: nvidia/parakeet-tdt-0.6b-v2 tags: - mlx - automatic-speech-recognition --- # Parakeet TDT 0.6b v2, MLX 8-bit 8-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=8, 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 1.91% (bf16: 1.87%), RTF 0.024, peak MLX memory 1.26 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=8) 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.

NoteSep 22huggingface.co/spokedotso/parakeet-tdt-0.6b-v2-mlx-8bit

Source context: 20 downloads · 0 likes · Pipeline automatic-speech-recognition · Library mlx · Repo spokedotso/parakeet-tdt-0.6b-v2-mlx-8bit