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beshkenadze/cohere-transcribe-03-2026-mlx-4bit

sourceHugging Faceapache-2.0updated 6mo agoView on Hugging Face
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Model Card

cohere-transcribe-03-2026-mlx-4bit

Quantized MLX weights for beshkenadze/cohere-transcribe-03-2026-mlx-fp16.

Variant

  • —Precision: 4-bit
  • —Quantization mode: affine
  • —Group size: 64

Files

  • —model.safetensors
  • —config.json
  • —tokenizer.model
  • —tokenizer_config.json
  • —preprocessor_config.json
  • —special_tokens_map.json
  • —key_map.json
  • —conversion_summary.json

Repo-sample benchmark

Sample: Tests/media/conversational_a.wav

  • —Generation TPS: 394.6
  • —Peak memory: 1.96 GB
  • —Output: Coffee's story likely begins in Ethiopia, where legend tells of a goat herder named Khaldi, who noticed his goats became energetic after eating red berries from a particular bush; curious, he tried them himself and felt invigorated.

Parity note

This checkpoint has been re-validated against the current Swift and Python MLX runtimes.

Verified semantic parity on an English fixture:

This is a test recording in English. I am speaking clearly at a normal speed. Please transcribe this sentence exactly as I said.

Matched across:

  • —Swift MLX fp16
  • —Swift MLX 8-bit
  • —Swift MLX 4-bit
  • —Python MLX fp16
  • —Python MLX 4-bit
  • —official CUDA reference path (transformers native Cohere ASR)

Quality note

Fastest and smallest, but introduces a lexical regression on the repo sample (Kaldi → Khaldi).

Notes

  • —Generated from the Swift-compatible fp16 checkpoint beshkenadze/cohere-transcribe-03-2026-mlx-fp16.
  • —This repository contains inference artifacts only. Refer to the upstream Cohere model card and license for original model details.