Qwen3-0.6B LLM decoder
(28 layers, GQA 16/8, head_dim 128, RoPE θ=1e6, RMSNorm eps=1e-6) — the same body as Qwen3-ASR's decoder
Speech is spliced into the LLM via the ChatML prompt
<|im_start|>user 语音转写:<placeholders><|im_end|>\n<|im_start|>assistant\n
and decoded autoregressively
KV cache so per-token decode is O(1) in cache size
Architecture note — no CTC path
Upstream
config.yaml
and
funasr/models/fun_asr_nano/model.py
declare a CTC decoder + head, but the published
model.pt
ships
only
audio_encoder.* + audio_adaptor.* + llm.*
(1261 tensors total, zero
ctc_decoder.*
/
ctc.ctc_lo.*
keys). The LLM-decoder path is therefore the only viable inference path for these weights, and is what this GGUF and the CrispASR runtime implement.
Files
File
Size
Notes
funasr-nano-2512.gguf
(alias)
1.98 GB
symlink/alias of the F16
funasr-nano-2512-f16.gguf
1.98 GB
F16, full precision reference
funasr-nano-2512-q8_0.gguf
1.27 GB
Q8_0, near-lossless
funasr-nano-2512-q4_k.gguf
897 MB
Q4_K — recommended default
All three precisions produce byte-identical output on
samples/jfk.wav
:
AND SO MY FELLOW AMERICANS ASK NOT WHAT YOUR COUNTRY CAN DO FOR YOU ASK WHAT YOU CAN DO FOR YOUR COUNTRY
(Fun-ASR-Nano outputs upper-case English without punctuation; pipe
through
--punc-model fullstop-punc
or
fireredpunc
if you need
proper casing/punctuation.)
These GGUF files are a quantised / repackaged distribution of the upstream weights and inherit the FunASR Model License v1.1. Please attribute Alibaba / FunAudioLLM in downstream products.
If you use this model, please also cite the upstream FunASR work.
See the
upstream model card
for the canonical citation.
Runs of cstr funasr-nano-GGUF on huggingface.co
814
Total runs
-4
24-hour runs
3
3-day runs
-53
7-day runs
-1.5K
30-day runs
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