GLM-ASR-Nano-2512 — GGUF
GGUF conversions and quantisations of zai-org/GLM-ASR-Nano-2512 for use with CrispStrobe/CrispASR.
Available variants
| File | Quant | Size | Notes |
|---|---|---|---|
glm-asr-nano.gguf | F16 | 4.3 GB | Full precision |
glm-asr-nano-q8_0.gguf | Q8_0 | 2.3 GB | High quality |
glm-asr-nano-q4_k.gguf | Q4_K | 1.3 GB | Best size/quality tradeoff |
All variants produce correct transcription on test audio.
2026-07 update — BPE merges baked in + long-form single-pass
All files were re-published with the tokenizer's BPE merges in the GGUF
metadata (tokenizer.ggml.merges, +2 MB). CrispASR ≥ this date uses them to
encode the transcription prompt exactly like the HF blueprint — earlier
GGUF+runtime combinations silently sent no instruction at all, which is
what caused repetition loops on noisy audio and empty output on long clips
(CrispASR #218).
Old GGUFs still work with the new runtime (it falls back to a baked default
prompt), but custom --ask / --language instructions need these files.
Long audio: --chunk-seconds 0 now decodes up to 655 s in one pass
(30 s encoder windows, one LLM prompt — the blueprint's layout), matching
the transformers reference verbatim on the #218 test clip. Note the model
skips leading non-speech audio in single-pass mode (blueprint behaviour);
the default 30 s-chunked mode covers more of such clips.
Model details
- Architecture: Whisper encoder (1280d, 32L, partial RoPE) + 4-frame projector + Llama LLM (2048d, 28L, GQA 16/4)
- Parameters: 1.5B
- Languages: Mandarin (+ Chinese dialects), English, Cantonese (model card metadata declares
en,zh; prose adds Cantonese 粤语 + other Chinese dialects). Not a general multilingual model — no Japanese/Korean/European-language support. - License: MIT
- Outperforms OpenAI Whisper V3 on benchmarks (lowest avg error rate 4.10)
Usage with CrispASR
git clone https://github.com/CrispStrobe/CrispASR && cd CrispASR
cmake -S . -B build && cmake --build build -j8
# Auto-detect backend from GGUF
./build/bin/crispasr -m glm-asr-nano-q4_k.gguf -f audio.wav
# Explicit backend
./build/bin/crispasr --backend glm-asr -m glm-asr-nano-q4_k.gguf -f audio.wav -osrt
Conversion
python models/convert-glm-asr-to-gguf.py --input zai-org/GLM-ASR-Nano-2512 --output glm-asr-nano.gguf
crispasr-quantize glm-asr-nano.gguf glm-asr-nano-q4_k.gguf q4_k
Provenance and EU AI Act Art. 53 note
- Upstream model: zai-org/GLM-ASR-Nano-2512 — published by
zai-org. - Upstream licence:
mit. This repository redistributes under the same terms; it grants no rights the upstream licence does not. - What was done here: format conversion and/or quantisation only (GGUF/GGML). No training, no fine-tuning, no merging, no distillation, no change to architecture, vocabulary or capability. Only the numeric representation of the upstream weights differs.
- Training data: documented — where it is documented at all — by the upstream provider; see the upstream model card. No training data was used, added or selected by this repository. No training-content summary was found on the upstream model card at the time of writing; that documentation gap is upstream's and is not filled here.
- Provider status: under Regulation (EU) 2024/1689 the upstream authors remain the provider of this model. Converting the serialisation format does not make this repository the provider of a new general-purpose AI model, and no such claim is made. Questions about training content, copyright policy or model capability belong upstream.