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

File size: 2,814 Bytes

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# Licenses and Attribution

This repository redistributes model weights created by others. Nothing here is an original
work of the repository owner. Per-file origins and licenses:

## parakeet/ — CC-BY-4.0

`sherpa-onnx-nemo-parakeet-tdt-0.6b-v3-int8.tar.bz2`

- 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/).
- ONNX int8 export by the [k2-fsa/sherpa-onnx](https://github.com/k2-fsa/sherpa-onnx) project (mirrored from its GitHub releases).
- Changes from the original: conversion to ONNX format and int8 quantization (by sherpa-onnx, not by this repository).

## nemotron-en/ — OpenMDW-1.1

`sherpa-onnx-nemotron-speech-streaming-en-0.6b-{80,160,560,1120}ms-int8-2026-04-25.tar.bz2`

- 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`).
- ONNX int8 exports by the [k2-fsa/sherpa-onnx](https://github.com/k2-fsa/sherpa-onnx) project (mirrored from its GitHub releases).

## nemotron35/ — OpenMDW-1.1

`nemotron-3.5-asr-streaming-0.6b-int8.tar.gz`

- 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`).
- 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).

## llm/ — Apache-2.0

- `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).
- `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).

## diarization/

- `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.
- `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.