Instructions to use Promptengineering/whryte-models with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use Promptengineering/whryte-models with llama.cpp:
Install (macOS, Linux)
curl -LsSf https://llama.app/install.sh | sh # Start a local OpenAI-compatible server with a web UI: llama serve -hf Promptengineering/whryte-models:Q4_K_M # Run inference directly in the terminal: llama cli -hf Promptengineering/whryte-models:Q4_K_M
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf Promptengineering/whryte-models:Q4_K_M # Run inference directly in the terminal: llama cli -hf Promptengineering/whryte-models:Q4_K_M
Use pre-built binary
# Download pre-built binary from: # https://github.com/ggerganov/llama.cpp/releases # Start a local OpenAI-compatible server with a web UI: ./llama-server -hf Promptengineering/whryte-models:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf Promptengineering/whryte-models:Q4_K_M
Build from source code
git clone https://github.com/ggerganov/llama.cpp.git cd llama.cpp cmake -B build cmake --build build -j --target llama-server llama-cli # Start a local OpenAI-compatible server with a web UI: ./build/bin/llama-server -hf Promptengineering/whryte-models:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf Promptengineering/whryte-models:Q4_K_M
Use Docker
docker model run hf.co/Promptengineering/whryte-models:Q4_K_M
- LM Studio
- Jan
- Ollama
How to use Promptengineering/whryte-models with Ollama:
ollama run hf.co/Promptengineering/whryte-models:Q4_K_M
- Unsloth Desktop
- Pi
How to use Promptengineering/whryte-models with Pi:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf Promptengineering/whryte-models:Q4_K_M
Configure the model in Pi
# Install Pi: npm install -g @earendil-works/pi-coding-agent # Add to ~/.pi/agent/models.json: { "providers": { "llama-cpp": { "baseUrl": "http://localhost:8080/v1", "api": "openai-completions", "apiKey": "none", "models": [ { "id": "Promptengineering/whryte-models:Q4_K_M" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Docker Model Runner
How to use Promptengineering/whryte-models with Docker Model Runner:
docker model run hf.co/Promptengineering/whryte-models:Q4_K_M
- Lemonade
How to use Promptengineering/whryte-models with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull Promptengineering/whryte-models:Q4_K_M
Run and chat with the model
lemonade run user.whryte-models-Q4_K_M
List all available models
lemonade list
- Hermes Agent
How to use Promptengineering/whryte-models with Hermes Agent:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf Promptengineering/whryte-models:Q4_K_M
Configure Hermes
# Install Hermes: curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash hermes setup # Point Hermes at the local server: hermes config set model.provider custom hermes config set model.base_url http://127.0.0.1:8080/v1 hermes config set model.default Promptengineering/whryte-models:Q4_K_M
Run Hermes
hermes
- Atomic Chat
- OpenClaw
How to use Promptengineering/whryte-models with OpenClaw:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf Promptengineering/whryte-models:Q4_K_M
Configure OpenClaw
# Install OpenClaw: npm install -g openclaw@latest # Register the local server and set it as the default model: openclaw onboard --non-interactive --mode local \ --auth-choice custom-api-key \ --custom-base-url http://127.0.0.1:8080/v1 \ --custom-model-id "Promptengineering/whryte-models:Q4_K_M" \ --custom-provider-id llama-cpp \ --custom-compatibility openai \ --custom-text-input \ --accept-risk \ --skip-health
Run OpenClaw
openclaw agent --local --agent main --message "Hello from Hugging Face"
Mirror Whryte model artifacts (see LICENSES.md for origins)
Browse files- .gitattributes +3 -0
- LICENSES.md +36 -0
- OpenMDW-1.1-LICENSE.txt +24 -0
- README.md +20 -0
- diarization/3dspeaker_speech_eres2net_base_sv_zh-cn_3dspeaker_16k.onnx +3 -0
- diarization/sherpa-onnx-pyannote-segmentation-3-0.tar.bz2 +3 -0
- llm/Qwen2.5-0.5B-Instruct-Q4_K_M.gguf +3 -0
- llm/Qwen2.5-1.5B-Instruct-Q4_K_M.gguf +3 -0
- llm/Qwen3-4B-Instruct-2507-Q4_K_M.gguf +3 -0
- nemotron-en/sherpa-onnx-nemotron-speech-streaming-en-0.6b-1120ms-int8-2026-04-25.tar.bz2 +3 -0
- nemotron-en/sherpa-onnx-nemotron-speech-streaming-en-0.6b-160ms-int8-2026-04-25.tar.bz2 +3 -0
- nemotron-en/sherpa-onnx-nemotron-speech-streaming-en-0.6b-560ms-int8-2026-04-25.tar.bz2 +3 -0
- nemotron-en/sherpa-onnx-nemotron-speech-streaming-en-0.6b-80ms-int8-2026-04-25.tar.bz2 +3 -0
- nemotron35/nemotron-3.5-asr-streaming-0.6b-int8.tar.gz +3 -0
- parakeet/sherpa-onnx-nemo-parakeet-tdt-0.6b-v3-int8.tar.bz2 +3 -0
.gitattributes
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# Licenses and Attribution
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This repository redistributes model weights created by others. Nothing here is an original
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work of the repository owner. Per-file origins and licenses:
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## parakeet/ — CC-BY-4.0
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`sherpa-onnx-nemo-parakeet-tdt-0.6b-v3-int8.tar.bz2`
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- Original model: [nvidia/parakeet-tdt-0.6b-v3](https://huggingface.co/nvidia/parakeet-tdt-0.6b-v3) by **NVIDIA Corporation**, licensed [CC-BY-4.0](https://creativecommons.org/licenses/by/4.0/).
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- ONNX int8 export by the [k2-fsa/sherpa-onnx](https://github.com/k2-fsa/sherpa-onnx) project (mirrored from its GitHub releases).
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- Changes from the original: conversion to ONNX format and int8 quantization (by sherpa-onnx, not by this repository).
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## nemotron-en/ — OpenMDW-1.1
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`sherpa-onnx-nemotron-speech-streaming-en-0.6b-{80,160,560,1120}ms-int8-2026-04-25.tar.bz2`
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- Original model: [nvidia/nemotron-speech-streaming-en-0.6b](https://huggingface.co/nvidia/nemotron-speech-streaming-en-0.6b) by **NVIDIA Corporation**, licensed OpenMDW-1.1 (full text in `OpenMDW-1.1-LICENSE.txt`).
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- ONNX int8 exports by the [k2-fsa/sherpa-onnx](https://github.com/k2-fsa/sherpa-onnx) project (mirrored from its GitHub releases).
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## nemotron35/ — OpenMDW-1.1
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`nemotron-3.5-asr-streaming-0.6b-int8.tar.gz`
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- Original model: [nvidia/nemotron-3.5-asr-streaming-0.6b](https://huggingface.co/nvidia/nemotron-3.5-asr-streaming-0.6b) by **NVIDIA Corporation**, licensed OpenMDW-1.1 (full text in `OpenMDW-1.1-LICENSE.txt`).
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- ONNX export via [altunenes/parakeet-rs](https://github.com/altunenes/parakeet-rs) export scripts; int8 quantization mirrored from [smcleod/nemotron-3.5-asr-streaming-0.6b-int8](https://huggingface.co/smcleod/nemotron-3.5-asr-streaming-0.6b-int8).
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## llm/ — Apache-2.0
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- `Qwen3-4B-Instruct-2507-Q4_K_M.gguf`: original model [Qwen/Qwen3-4B-Instruct-2507](https://huggingface.co/Qwen/Qwen3-4B-Instruct-2507) by **Alibaba Cloud / Qwen team**, [Apache-2.0](https://www.apache.org/licenses/LICENSE-2.0). GGUF Q4_K_M quantization by [bartowski](https://huggingface.co/bartowski/Qwen_Qwen3-4B-Instruct-2507-GGUF).
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- `Qwen2.5-1.5B-Instruct-Q4_K_M.gguf`, `Qwen2.5-0.5B-Instruct-Q4_K_M.gguf`: original models by **Alibaba Cloud / Qwen team**, Apache-2.0. GGUF quantizations by [bartowski](https://huggingface.co/bartowski).
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## diarization/
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- `sherpa-onnx-pyannote-segmentation-3-0.tar.bz2`: original model [pyannote/segmentation-3.0](https://huggingface.co/pyannote/segmentation-3.0) by **Hervé Bredin / pyannote**, [MIT](https://opensource.org/license/mit). ONNX export by sherpa-onnx.
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- `3dspeaker_speech_eres2net_base_sv_zh-cn_3dspeaker_16k.onnx`: original model from the [3D-Speaker](https://github.com/modelscope/3D-Speaker) project by **Alibaba**, [Apache-2.0](https://www.apache.org/licenses/LICENSE-2.0). ONNX export by sherpa-onnx.
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OpenMDW License Agreement, version 1.1 (OpenMDW-1.1)
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By exercising rights granted to you under this agreement, you accept and agree to its terms.
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As used in this agreement, "Model Materials" means the materials provided to you under this agreement, consisting of: (1) one or more machine learning models (including architecture and parameters); and (2) all related artifacts (including associated data, documentation and software) that are provided to you hereunder.
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Subject to your compliance with this agreement, permission is hereby granted, free of charge, to deal in the Model Materials without restriction, including under all copyright, patent, database, and trade secret rights included or embodied therein.
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If you distribute any portion of the Model Materials, you shall retain in your distribution (1) a copy of this agreement, and (2) all copyright notices and other notices of origin included in the Model Materials that are applicable to your distribution.
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If you file, maintain, or voluntarily participate in a lawsuit against any person or entity asserting that the Model Materials directly or indirectly infringe any patent or copyright, then all rights and grants made to you hereunder are terminated, unless that lawsuit was in response to a corresponding lawsuit first brought against you.
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This agreement does not impose any restrictions or obligations with respect to any use, modification, or sharing of any outputs generated by using the Model Materials.
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THE MODEL MATERIALS ARE PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE, TITLE, NONINFRINGEMENT, ACCURACY, OR THE ABSENCE OF LATENT OR OTHER DEFECTS OR ERRORS, WHETHER OR NOT DISCOVERABLE, ALL TO THE GREATEST EXTENT PERMISSIBLE UNDER APPLICABLE LAW.
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YOU ARE SOLELY RESPONSIBLE FOR (1) CLEARING RIGHTS OF OTHER PERSONS THAT MAY APPLY TO THE MODEL MATERIALS OR ANY USE THEREOF, INCLUDING WITHOUT LIMITATION ANY PERSON'S COPYRIGHTS OR OTHER RIGHTS INCLUDED OR EMBODIED IN THE MODEL MATERIALS; (2) OBTAINING ANY NECESSARY CONSENTS, PERMISSIONS OR OTHER RIGHTS REQUIRED FOR ANY USE OF THE MODEL MATERIALS; OR (3) PERFORMING ANY DUE DILIGENCE OR UNDERTAKING ANY OTHER INVESTIGATIONS INTO THE MODEL MATERIALS OR ANYTHING INCORPORATED OR EMBODIED THEREIN.
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IN NO EVENT SHALL THE PROVIDERS OF THE MODEL MATERIALS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE MODEL MATERIALS, THE USE THEREOF OR OTHER DEALINGS THEREIN.
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Copyright The Linux Foundation and its contributors. | Site contents licensed under CC-BY-4.0
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Terms
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github
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---
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license: other
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license_name: multiple
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license_link: https://huggingface.co/Promptengineering/whryte-models/blob/main/LICENSES.md
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---
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# Whryte Models
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Model artifacts downloaded by the [Whryte](https://whryte.com) desktop dictation app for
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Windows. This repository mirrors upstream releases so the app has a stable, owner-controlled
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download source. **These are not original works** — see [LICENSES.md](LICENSES.md) for the
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license and origin of every file.
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| Path | Model | Used for | License |
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|---|---|---|---|
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| `parakeet/` | NVIDIA Parakeet TDT 0.6B v3 int8 (sherpa-onnx export) | Batch dictation, file transcription | CC-BY-4.0 |
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| `nemotron-en/` | NVIDIA Nemotron Speech Streaming EN 0.6B int8, 4 chunk sizes (sherpa-onnx exports) | Live dictation (English) | OpenMDW-1.1 |
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| `nemotron35/` | NVIDIA Nemotron 3.5 ASR Streaming Multilingual 0.6B int8 (community ONNX export) | Live dictation (multilingual) | OpenMDW-1.1 |
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| `llm/` | Qwen3-4B-Instruct-2507, Qwen2.5-1.5B/0.5B-Instruct (GGUF Q4_K_M) | Transcript enhancement | Apache-2.0 |
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| `diarization/` | pyannote segmentation-3.0, 3D-Speaker ERes2Net (sherpa-onnx exports) | Speaker identification | MIT / Apache-2.0 |
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diarization/3dspeaker_speech_eres2net_base_sv_zh-cn_3dspeaker_16k.onnx
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nemotron-en/sherpa-onnx-nemotron-speech-streaming-en-0.6b-1120ms-int8-2026-04-25.tar.bz2
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nemotron-en/sherpa-onnx-nemotron-speech-streaming-en-0.6b-160ms-int8-2026-04-25.tar.bz2
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nemotron-en/sherpa-onnx-nemotron-speech-streaming-en-0.6b-560ms-int8-2026-04-25.tar.bz2
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nemotron-en/sherpa-onnx-nemotron-speech-streaming-en-0.6b-80ms-int8-2026-04-25.tar.bz2
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| 1 |
+
version https://git-lfs.github.com/spec/v1
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oid sha256:d1d57d86212528fa03dfdbb88979f1dd637814dec6db31257a603739c73bd9d2
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| 3 |
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size 452641212
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parakeet/sherpa-onnx-nemo-parakeet-tdt-0.6b-v3-int8.tar.bz2
ADDED
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@@ -0,0 +1,3 @@
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| 1 |
+
version https://git-lfs.github.com/spec/v1
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| 2 |
+
oid sha256:5793d0fd397c5778d2cf2126994d58e9d56b1be7c04d13c7a15bb1b4eafb16bf
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| 3 |
+
size 487170055
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