Instructions to use Promptengineering/whryte-models with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Promptengineering/whryte-models with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="Promptengineering/whryte-models",
	filename="llm/Qwen2.5-0.5B-Instruct-Q4_K_M.gguf",
)

llm.create_chat_completion(
	messages = "No input example has been defined for this model task."
)

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 Studio

How to use Promptengineering/whryte-models with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for Promptengineering/whryte-models to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for Promptengineering/whryte-models to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for Promptengineering/whryte-models to start chatting

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 @mariozechner/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

Hermes Agent new

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 new
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

whryte-models / README.md

Promptengineering

Mirror Whryte model artifacts (see LICENSES.md for origins)

5d2ab08 verified 15 days ago

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	---
	license: other
	license_name: multiple
	license_link: https://huggingface.co/Promptengineering/whryte-models/blob/main/LICENSES.md
	---

	# Whryte Models

	Model artifacts downloaded by the [Whryte](https://whryte.com) desktop dictation app for
	Windows. This repository mirrors upstream releases so the app has a stable, owner-controlled
	download source. These are not original works — see [LICENSES.md](LICENSES.md) for the
	license and origin of every file.

	\| Path \| Model \| Used for \| License \|
	\|---\|---\|---\|---\|
	\| `parakeet/` \| NVIDIA Parakeet TDT 0.6B v3 int8 (sherpa-onnx export) \| Batch dictation, file transcription \| CC-BY-4.0 \|
	\| `nemotron-en/` \| NVIDIA Nemotron Speech Streaming EN 0.6B int8, 4 chunk sizes (sherpa-onnx exports) \| Live dictation (English) \| OpenMDW-1.1 \|
	\| `nemotron35/` \| NVIDIA Nemotron 3.5 ASR Streaming Multilingual 0.6B int8 (community ONNX export) \| Live dictation (multilingual) \| OpenMDW-1.1 \|
	\| `llm/` \| Qwen3-4B-Instruct-2507, Qwen2.5-1.5B/0.5B-Instruct (GGUF Q4_K_M) \| Transcript enhancement \| Apache-2.0 \|
	\| `diarization/` \| pyannote segmentation-3.0, 3D-Speaker ERes2Net (sherpa-onnx exports) \| Speaker identification \| MIT / Apache-2.0 \|