Add model card with architecture, languages, and usage

README.md

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+---
+license: openmdw-1.1
+language:
+  - ar
+  - bg
+  - cs
+  - da
+  - de
+  - el
+  - en
+  - es
+  - et
+  - fi
+  - fr
+  - he
+  - hi
+  - hr
+  - hu
+  - it
+  - ja
+  - ko
+  - lt
+  - lv
+  - nb
+  - nl
+  - nn
+  - pl
+  - pt
+  - ro
+  - ru
+  - sk
+  - sl
+  - sv
+  - th
+  - tr
+  - uk
+  - vi
+  - zh
+tags:
+  - asr
+  - speech-recognition
+  - gguf
+  - streaming
+  - fastconformer
+  - rnnt
+  - multilingual
+  - crispasr
+pipeline_tag: automatic-speech-recognition
+base_model: nvidia/nemotron-3.5-asr-streaming-0.6b
+---
+
+# Nemotron-3.5-ASR-Streaming-0.6B GGUF
+
+GGUF conversion of [nvidia/nemotron-3.5-asr-streaming-0.6b](https://huggingface.co/nvidia/nemotron-3.5-asr-streaming-0.6b) for use with [CrispASR](https://github.com/CrispStrobe/CrispASR).
+
+## Model details
+
+**Architecture:** Cache-Aware Streaming FastConformer encoder (24 layers, d=1024, 8 heads) + RNN-T decoder (2-layer LSTM, hidden=640) + joint network (640 → 13088 vocab).
+
+**Languages:** 39 languages, selected via `prompt_kernel` MLP conditioning:
+
+| Code | Language | Code | Language | Code | Language |
+|------|----------|------|----------|------|----------|
+| ar-AR | Arabic | fr-CA | French (CA) | nn-NO | Norwegian (NN) |
+| bg-BG | Bulgarian | fr-FR | French (FR) | pl-PL | Polish |
+| cs-CZ | Czech | he-IL | Hebrew | pt-BR | Portuguese (BR) |
+| da-DK | Danish | hi-IN | Hindi | pt-PT | Portuguese (PT) |
+| de-DE | German | hr-HR | Croatian | ro-RO | Romanian |
+| el-GR | Greek | hu-HU | Hungarian | ru-RU | Russian |
+| en-GB | English (GB) | it-IT | Italian | sk-SK | Slovak |
+| en-US | English (US) | ja-JP | Japanese | sl-SL | Slovenian |
+| es-ES | Spanish (ES) | ko-KR | Korean | sv-SE | Swedish |
+| es-US | Spanish (US) | lt-LT | Lithuanian | th-TH | Thai |
+| et-EE | Estonian | lv-LV | Latvian | tr-TR | Turkish |
+| fi-FI | Finnish | nb-NO | Norwegian (NB) | uk-UA | Ukrainian |
+|  |  | nl-NL | Dutch | vi-VN | Vietnamese |
+|  |  |  |  | zh-CN | Chinese |
+
+**Key properties:**
+- Sample rate: 16 kHz
+- 128 mel filterbank features, n_fft=512, hop=160 (10ms), win=400 (25ms)
+- 8× time downsampling (causal) → 80ms frame duration
+- Streaming: cache-aware attention with `att_context_size=[[56,3],[56,0],[56,6],[56,13]]`
+- Vocab: 13087 SentencePiece tokens + 1 blank (pure RNN-T, no TDT durations)
+- License: [OpenMDW-1.1](https://github.com/linux-foundation/open-model-developer-weight-license/blob/main/LICENSE.md) (permissive)
+
+## Files
+
+| File | Size | Description |
+|------|------|-------------|
+| `nemotron-3.5-asr-streaming-0.6b-f16.gguf` | ~1.2 GB | F16 weights (full precision) |
+| `nemotron-3.5-asr-streaming-ref.gguf` | ~1.2 GB | Reference GGUF (for parity testing) |
+
+## Usage with CrispASR
+
+```bash
+# Download
+huggingface-cli download cstr/nemotron-3.5-asr-streaming-GGUF \
+  nemotron-3.5-asr-streaming-0.6b-f16.gguf --local-dir models/
+
+# Transcribe (English, default)
+crispasr --backend nemotron \
+  -m models/nemotron-3.5-asr-streaming-0.6b-f16.gguf \
+  -f audio.wav
+
+# Transcribe in German
+crispasr --backend nemotron \
+  -m models/nemotron-3.5-asr-streaming-0.6b-f16.gguf \
+  -f audio.wav --language de-DE
+
+# Streaming mode
+crispasr --backend nemotron \
+  -m models/nemotron-3.5-asr-streaming-0.6b-f16.gguf \
+  -f audio.wav --stream
+```
+
+## Conversion
+
+Converted from the original NeMo `.nemo` checkpoint using:
+
+```bash
+python models/convert-nemotron-to-gguf.py \
+  --nemo nvidia/nemotron-3.5-asr-streaming-0.6b \
+  --output nemotron-3.5-asr-streaming-0.6b-f16.gguf
+```
+
+Quantized variants can be produced with:
+
+```bash
+crispasr-quantize models/nemotron-3.5-asr-streaming-0.6b-f16.gguf \
+  models/nemotron-3.5-asr-streaming-0.6b-q4_k.gguf Q4_K
+```
+
+## Architecture
+
+```
+Audio (16kHz) → Mel (128 bins, 10ms hop)
+  → Pre-encode (3× causal Conv2d, 8× downsample, Linear 4352→1024)
+  → 24× Cache-Aware FastConformer block:
+      FFN1(½) → MHA(rel_pos, cache-aware) → DWConv(k=9, causal, LN) → FFN2(½) → LN
+  → Prompt kernel (MLP: concat(enc[1024], lang_onehot[128]) → 2048 → 1024)
+  → RNN-T decoder:
+      Prediction: Embed(13088, 640) + 2-layer LSTM(640)
+      Joint: enc(1024→640) + pred(640→640) → ReLU → Linear(640→13088)
+  → Greedy / beam search decode
+```
+
+## Original model
+
+- **Paper:** [NVIDIA NeMo documentation](https://docs.nvidia.com/nemo-framework/user-guide/latest/nemotoolkit/asr/intro.html)
+- **Source:** [nvidia/nemotron-3.5-asr-streaming-0.6b](https://huggingface.co/nvidia/nemotron-3.5-asr-streaming-0.6b)
+- **License:** OpenMDW-1.1