Instructions to use mudler/parakeet-cpp-gguf with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- NeMo
How to use mudler/parakeet-cpp-gguf with NeMo:
import nemo.collections.asr as nemo_asr asr_model = nemo_asr.models.ASRModel.from_pretrained("mudler/parakeet-cpp-gguf") transcriptions = asr_model.transcribe(["file.wav"]) - Notebooks
- Google Colab
- Kaggle
Update combined parakeet.cpp model card
Browse files
README.md
ADDED
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---
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license: cc-by-4.0
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library_name: parakeet.cpp
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tags:
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- automatic-speech-recognition
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- asr
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- parakeet
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- gguf
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- ggml
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- cpp-inference
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- nemo
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pipeline_tag: automatic-speech-recognition
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base_model:
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- nvidia/parakeet-tdt_ctc-110m
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- nvidia/parakeet_realtime_eou_120m-v1
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- nvidia/parakeet-ctc-0.6b
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- nvidia/parakeet-rnnt-0.6b
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- nvidia/parakeet-tdt-0.6b-v2
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- nvidia/parakeet-tdt-0.6b-v3
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- nvidia/parakeet-ctc-1.1b
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- nvidia/parakeet-rnnt-1.1b
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- nvidia/parakeet-tdt-1.1b
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- nvidia/parakeet-tdt_ctc-1.1b
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---
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# Parakeet GGUF — models for parakeet.cpp
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GGUF-format weights for [parakeet.cpp](https://github.com/mudler/parakeet.cpp), a C++/ggml port of NVIDIA NeMo Parakeet that matches the upstream PyTorch models on CPU. This single repo collects **every supported model × quantization** as a flat set of `.gguf` files — download just the one you need.
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**F16 is the recommended default** — same accuracy as F32, ~1.7× smaller, and typically the fastest on modern CPUs via ggml's F32×F16 matmul fast path.
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## Models
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### tdt_ctc-110m
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Source: [nvidia/parakeet-tdt_ctc-110m](https://huggingface.co/nvidia/parakeet-tdt_ctc-110m) · Hybrid TDT+CTC (FastConformer) · heads: TDT + CTC
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| File | Variant | Size | WER vs NeMo |
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|---|---|---:|---:|
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| `tdt_ctc-110m-f16.gguf` ← **recommended** | F16 | 267.5 MB | 0.0000 |
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| `tdt_ctc-110m-q8_0.gguf` | Q8_0 | 177.8 MB | 0.0000 |
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| `tdt_ctc-110m-q6_k.gguf` | Q6_K | 155.9 MB | not measured |
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| `tdt_ctc-110m-q5_k.gguf` | Q5_K | 143.3 MB | not measured |
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| `tdt_ctc-110m-q4_k.gguf` | Q4_K | 131.4 MB | 0.0000 |
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### realtime_eou_120m-v1
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Source: [nvidia/parakeet_realtime_eou_120m-v1](https://huggingface.co/nvidia/parakeet_realtime_eou_120m-v1) · Cache-aware streaming RNNT (FastConformer, EOU/EOB) · heads: RNNT (streaming)
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| File | Variant | Size | WER vs NeMo |
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|---|---|---:|---:|
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| `realtime_eou_120m-v1-f16.gguf` ← **recommended** | F16 | 266.5 MB | not measured |
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| `realtime_eou_120m-v1-q8_0.gguf` | Q8_0 | 176.0 MB | not measured |
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| `realtime_eou_120m-v1-q6_k.gguf` | Q6_K | 153.9 MB | not measured |
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| `realtime_eou_120m-v1-q5_k.gguf` | Q5_K | 141.2 MB | not measured |
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| `realtime_eou_120m-v1-q4_k.gguf` | Q4_K | 129.1 MB | not measured |
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### ctc-0.6b
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Source: [nvidia/parakeet-ctc-0.6b](https://huggingface.co/nvidia/parakeet-ctc-0.6b) · CTC (FastConformer) · heads: CTC
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| File | Variant | Size | WER vs NeMo |
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|---|---|---:|---:|
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| `ctc-0.6b-f16.gguf` ← **recommended** | F16 | 1373.4 MB | 0.0000 |
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| `ctc-0.6b-q8_0.gguf` | Q8_0 | 875.4 MB | 0.0000 |
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| `ctc-0.6b-q6_k.gguf` | Q6_K | 746.8 MB | not measured |
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| `ctc-0.6b-q5_k.gguf` | Q5_K | 676.3 MB | not measured |
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| `ctc-0.6b-q4_k.gguf` | Q4_K | 609.9 MB | not measured |
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### rnnt-0.6b
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Source: [nvidia/parakeet-rnnt-0.6b](https://huggingface.co/nvidia/parakeet-rnnt-0.6b) · RNNT transducer (FastConformer) · heads: RNNT
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| File | Variant | Size | WER vs NeMo |
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|---|---|---:|---:|
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| `rnnt-0.6b-f16.gguf` ← **recommended** | F16 | 1402.8 MB | 0.0000 |
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| `rnnt-0.6b-q8_0.gguf` | Q8_0 | 903.9 MB | 0.0000 |
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| `rnnt-0.6b-q6_k.gguf` | Q6_K | 776.3 MB | not measured |
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| `rnnt-0.6b-q5_k.gguf` | Q5_K | 705.7 MB | not measured |
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| `rnnt-0.6b-q4_k.gguf` | Q4_K | 639.2 MB | not measured |
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### tdt-0.6b-v2
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Source: [nvidia/parakeet-tdt-0.6b-v2](https://huggingface.co/nvidia/parakeet-tdt-0.6b-v2) · TDT transducer (FastConformer) · heads: TDT
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| File | Variant | Size | WER vs NeMo |
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|---|---|---:|---:|
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| `tdt-0.6b-v2-f16.gguf` ← **recommended** | F16 | 1404.2 MB | 0.0000 |
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| `tdt-0.6b-v2-q8_0.gguf` | Q8_0 | 903.8 MB | 0.0000 |
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| `tdt-0.6b-v2-q6_k.gguf` | Q6_K | 775.9 MB | not measured |
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| `tdt-0.6b-v2-q5_k.gguf` | Q5_K | 705.0 MB | not measured |
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| `tdt-0.6b-v2-q4_k.gguf` | Q4_K | 638.4 MB | not measured |
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### tdt-0.6b-v3
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Source: [nvidia/parakeet-tdt-0.6b-v3](https://huggingface.co/nvidia/parakeet-tdt-0.6b-v3) · TDT transducer (FastConformer) · heads: TDT
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| File | Variant | Size | WER vs NeMo |
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|---|---|---:|---:|
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| `tdt-0.6b-v3-f16.gguf` ← **recommended** | F16 | 1441.0 MB | 0.0000 |
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| `tdt-0.6b-v3-q8_0.gguf` | Q8_0 | 940.7 MB | 0.0000 |
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| `tdt-0.6b-v3-q6_k.gguf` | Q6_K | 812.7 MB | not measured |
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| `tdt-0.6b-v3-q5_k.gguf` | Q5_K | 741.9 MB | not measured |
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| `tdt-0.6b-v3-q4_k.gguf` | Q4_K | 675.2 MB | not measured |
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### ctc-1.1b
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Source: [nvidia/parakeet-ctc-1.1b](https://huggingface.co/nvidia/parakeet-ctc-1.1b) · CTC (FastConformer) · heads: CTC
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| File | Variant | Size | WER vs NeMo |
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|---|---|---:|---:|
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| `ctc-1.1b-f16.gguf` ← **recommended** | F16 | 2395.8 MB | 0.0000 |
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| `ctc-1.1b-q8_0.gguf` | Q8_0 | 1526.3 MB | 0.0000 |
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| `ctc-1.1b-q6_k.gguf` | Q6_K | 1301.7 MB | not measured |
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| `ctc-1.1b-q5_k.gguf` | Q5_K | 1178.5 MB | not measured |
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| `ctc-1.1b-q4_k.gguf` | Q4_K | 1062.6 MB | not measured |
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### rnnt-1.1b
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Source: [nvidia/parakeet-rnnt-1.1b](https://huggingface.co/nvidia/parakeet-rnnt-1.1b) · RNNT transducer (FastConformer) · heads: RNNT
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| File | Variant | Size | WER vs NeMo |
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|---|---|---:|---:|
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| `rnnt-1.1b-f16.gguf` ← **recommended** | F16 | 2425.2 MB | 0.0000 |
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| `rnnt-1.1b-q8_0.gguf` | Q8_0 | 1554.7 MB | 0.0000 |
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| `rnnt-1.1b-q6_k.gguf` | Q6_K | 1331.2 MB | not measured |
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| `rnnt-1.1b-q5_k.gguf` | Q5_K | 1207.9 MB | not measured |
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| `rnnt-1.1b-q4_k.gguf` | Q4_K | 1091.9 MB | not measured |
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### tdt-1.1b
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Source: [nvidia/parakeet-tdt-1.1b](https://huggingface.co/nvidia/parakeet-tdt-1.1b) · TDT transducer (FastConformer) · heads: TDT
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| File | Variant | Size | WER vs NeMo |
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|---|---|---:|---:|
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| `tdt-1.1b-f16.gguf` ← **recommended** | F16 | 2425.3 MB | 0.0000 |
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| `tdt-1.1b-q8_0.gguf` | Q8_0 | 1554.8 MB | 0.0000 |
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| `tdt-1.1b-q6_k.gguf` | Q6_K | 1331.2 MB | not measured |
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| `tdt-1.1b-q5_k.gguf` | Q5_K | 1207.9 MB | not measured |
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| `tdt-1.1b-q4_k.gguf` | Q4_K | 1091.9 MB | not measured |
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### tdt_ctc-1.1b
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Source: [nvidia/parakeet-tdt_ctc-1.1b](https://huggingface.co/nvidia/parakeet-tdt_ctc-1.1b) · Hybrid TDT+CTC (FastConformer) · heads: TDT + CTC
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| File | Variant | Size | WER vs NeMo |
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|---|---|---:|---:|
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| `tdt_ctc-1.1b-f16.gguf` ← **recommended** | F16 | 2429.5 MB | 0.0000 |
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| `tdt_ctc-1.1b-q8_0.gguf` | Q8_0 | 1559.0 MB | 0.0000 |
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| `tdt_ctc-1.1b-q6_k.gguf` | Q6_K | 1335.4 MB | not measured |
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| `tdt_ctc-1.1b-q5_k.gguf` | Q5_K | 1212.1 MB | not measured |
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| `tdt_ctc-1.1b-q4_k.gguf` | Q4_K | 1096.1 MB | not measured |
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> WER (word error rate) is computed against the upstream NeMo reference on `tests/fixtures/speech.wav` (LibriSpeech `2086-149220-0033`, ~7.4 s, English). 0.0 = byte-for-byte identical transcript. See [parity.md](https://github.com/mudler/parakeet.cpp/blob/main/docs/parity.md) and [quantization.md](https://github.com/mudler/parakeet.cpp/blob/main/docs/quantization.md).
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## Quantization notes
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Quantization is applied **only** to the large linear weights fed directly into `ggml_mul_mat` (encoder FFN + attention projections, subsampling output projection, joint enc/pred projections). All other tensors (mel filterbank, LSTM prediction net, conv kernels, batch_norm stats, norms, biases, embeddings) stay F32.
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## Usage
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```bash
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# 1. Clone + build parakeet.cpp
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git clone https://github.com/mudler/parakeet.cpp
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cd parakeet.cpp
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cmake -B build -DPARAKEET_BUILD_CLI=ON && cmake --build build -j
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# 2. Download one quant (F16 recommended)
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huggingface-cli download mudler/parakeet-cpp-gguf tdt_ctc-110m-f16.gguf --local-dir models/
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# 3. Transcribe
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build/examples/cli/parakeet-cli transcribe \
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--model models/tdt_ctc-110m-f16.gguf \
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--input audio.wav
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```
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## License
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The GGUF weights are derived from the NVIDIA NeMo Parakeet checkpoints, released under the [CC-BY-4.0](https://creativecommons.org/licenses/by/4.0/) license. The parakeet.cpp runtime is MIT-licensed.
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