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astroanand/astro-code-switch-0.5_7.9B

sourceHugging Facegemmaupdated 25d agoView on Hugging Face

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astrocodeswitch0.5_7.9B

4-bit decoder-only quantization of Gemma 4 E4B, for on-device code-switched (Hindi–English) dictation. Built for Astro.

The audio encoder is left at bf16 on purpose. Only the language decoder is quantized — it is 94% of the weights, so it is the whole lever, and quantizing the encoder costs transcription quality for almost no size saving.

component	params	share	precision
`language_model` (decoder)	7.46 B	94.1%	4-bit, group 64, affine
`audio_tower` (encoder)	0.305 B	3.8%	bf16
`vision_tower` (unused for dictation)	0.167 B	2.1%	bf16
total	7.93 B

Measured results

40 clips, same eval script and prompt, run one at a time on an M5 Pro (48 GB).

variant	on disk	WER	CER	median latency	RTF
bf16 baseline	15.9 GB	3.59%	1.41%	749 ms	7.3×
this model (4-bit decoder)	4.82 GB	3.76%	2.05%	386 ms	14.8×
3-bit decoder	3.96 GB	260%	296%	373 ms	12.6×

3.3× smaller and ~2× faster for +0.17pp WER.

The 3-bit row is the useful part: quantization does not degrade gently here, it falls off a cliff. At 3 bits the model enters runaway repetition loops and is unusable. 4-bit is the frontier, not a midpoint.

Why this exists

Parakeet is faster and more accurate on English (1.35% WER, 90 ms) but cannot speak Hindi or Gujarati. Gemma 4's audio encoder is genuinely cross-lingual and holds code-switched speech intact — English stays Latin, Hindi stays Devanagari, in the same utterance:

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That property is what this model is for. The bf16 original is too heavy to keep resident (15.9 GB, 749 ms); this makes it practical on a laptop.

Usage

python

from mlx_vlm import load, generate
model, processor = load("astroanand/astro_code_switch0.5_7.9B")

Script policy (Devanagari ↔ Roman) is handled after transcription by a deterministic normalizer, not by prompting. Prompt steering was measured and does not work on this family: the 0.6B is steerable but unstable, the 1.7B ignores prompts entirely. Romanization is only safe in the Indic→Latin direction; Latin→Indic is destructive.

Provenance

Quantized from mlx-community/gemma-4-e4b-it-bf16 with mlx_vlm. Reproduction scripts (01_inspect_split.py, 02_quantize.py, 03_eval.py) and full results are in the Astro research tree. Original Gemma terms of use apply.