docs: document GigaAM Multilingual Large CTC on the GigaAM model page

docs/models/gigaam.md

@@ -8,6 +8,11 @@ trained either end-to-end on cased+punctuated text or on
 lowercased no-punctuation text with a 33-entry character vocabulary.
 The four shipped variants are the four cells of that 2×2.
 
+A fifth variant, `gigaam-multilingual-large-ctc`, comes from
+[`ai-sage/GigaAM-Multilingual`](https://huggingface.co/ai-sage/GigaAM-Multilingual),
+transcribes five languages, and is not published yet; see
+[GigaAM Multilingual Large CTC](#gigaam-multilingual-large-ctc).
+
 For the architecture deep-dive, validation contract, and porting notes,
 see the family doc at
 [`docs/porting/families/gigaam.md`](../porting/families/gigaam.md).
@@ -26,7 +31,11 @@ see the family doc at
   output is normalized for downstream ASR scoring pipelines that expect
   this convention. Higher raw WER than the e2e variants because errors
   on case/punctuation are no longer absorbed by tokenization.
-- The encoder is structurally identical across the four variants but
+- **Russian, English, Kazakh, Kyrgyz, or Uzbek.**
+  `gigaam-multilingual-large-ctc` — lowercase charwise output with no
+  punctuation. No pre-built GGUF; convert it yourself, see
+  [GigaAM Multilingual Large CTC](#gigaam-multilingual-large-ctc).
+- The encoder is structurally identical across the four v3 variants but
   the weights are per-variant fine-tuned — you cannot swap heads at
   runtime.
 
@@ -45,10 +54,51 @@ for the full quant matrix.
 | `gigaam-v3-ctc`      |   221M | ru        |    272 MB | FLEURS ru (WER) | 8.42% | token timestamps | [gigaam-v3-ctc.md](gigaam-v3-ctc.md) |
 <!-- /catalog -->
 
-Pre-built GGUFs for every variant and quant are hosted under
+Pre-built GGUFs for every v3 variant and quant are hosted under
 [`handy-computer` on Hugging Face](https://huggingface.co/handy-computer);
 each per-variant doc has direct download links.
 
+## GigaAM Multilingual Large CTC
+
+`gigaam-multilingual-large-ctc` is the `large_ctc` branch of
+[`ai-sage/GigaAM-Multilingual`](https://huggingface.co/ai-sage/GigaAM-Multilingual).
+It transcribes Russian, English, Kazakh, Kyrgyz, and Uzbek into lowercase
+text with no punctuation. The encoder has 24 layers at d_model=1024, larger
+than the v3 encoder, and the CTC head decodes a 70-character vocabulary.
+
+The variant is not in the table above: no GGUF is published, and its WER
+and benchmarks have not been measured. Conversion and numerical validation
+at F32 pass; the [family doc](../porting/families/gigaam.md#gigaam-multilingual-large-ctc)
+has the validation results.
+
+Convert, quantize, and run:
+
+```bash
+uv run --project scripts/envs/gigaam \
+  scripts/convert-gigaam.py ai-sage/GigaAM-Multilingual \
+  --repo-id gigaam-multilingual-large-ctc
+
+build/bin/transcribe-quantize \
+  models/gigaam-multilingual-large-ctc/gigaam-multilingual-large-ctc-F32.gguf \
+  models/gigaam-multilingual-large-ctc/gigaam-multilingual-large-ctc-Q8_0.gguf \
+  --quant Q8_0
+
+build/bin/transcribe-cli \
+  -m models/gigaam-multilingual-large-ctc/gigaam-multilingual-large-ctc-Q8_0.gguf \
+  samples/fleurs-en.wav
+```
+
+The converter downloads a 2.3 GB snapshot at the commit pinned in
+`scripts/lib/gigaam_hf.py` and writes a 2.3 GB (2341378624 bytes) F32 GGUF.
+It accepts only the repo id `ai-sage/GigaAM-Multilingual`, not a local
+directory.
+
+The model takes no language input. `--language` accepts `ru`, `en`, `kk`,
+`ky`, and `uz` and leaves the transcript unchanged; any other code fails
+with `run: unsupported language`. On the clips checked with Metal, Q5_K_M
+and larger quantizations match the reference transcript; Q4_K_M changes one
+letter on one Kazakh clip.
+
 ## Input limits
 
 GigaAM is trained for utterances up to about **25 seconds**. Longer audio is
@@ -57,6 +107,10 @@ stitched into one sequence for a single greedy decode. There is no practical
 length limit, and the encoder workspace does not grow with input length. Audio
 up to 25 s is encoded in one pass. See the [input-length contract](../input-limits.md).
 
+The runtime applies the same 25 s window to `gigaam-multilingual-large-ctc`.
+The port records no training length for this variant, and no clip longer
+than 25 s has been compared with the reference.
+
 ## Quick start
 
 Pick a variant and run:
@@ -88,3 +142,7 @@ streaming, VAD, speaker diarization, languages other than Russian.
 Long-form input is windowed internally rather than through upstream's
 `transcribe_longform` PyAnnote-VAD chunking. See the family doc for the full
 runtime contract.
+
+`gigaam-multilingual-large-ctc` has the same capabilities and gaps, except
+that it transcribes the five languages listed in
+[its section](#gigaam-multilingual-large-ctc).

docs/tools/conversion.md

@@ -38,7 +38,9 @@ source format:
   `voxtral_realtime`, `granite`, `granite_nar`, `moonshine`,
   `moonshine_streaming`, `qwen3_asr`, `medasr`, `cohere`
 - **FunASR**: `sensevoice`, `funasr_nano`
-- **Author package**: `gigaam` (the upstream `gigaam` pip package)
+- **Author package**: `gigaam` (the upstream `gigaam` pip package; GigaAM
+  Multilingual is built from that package's classes and a pinned Hugging Face
+  snapshot)
 
 Each converter is a single-file script with inline documentation of its
 tensor catalog, hparam map, and layout transforms. No base class. See