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

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use praxis-app/praxis-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 praxis-app/praxis-models:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf praxis-app/praxis-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 praxis-app/praxis-models:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf praxis-app/praxis-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 praxis-app/praxis-models:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf praxis-app/praxis-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 praxis-app/praxis-models:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf praxis-app/praxis-models:Q4_K_M

Use Docker

docker model run hf.co/praxis-app/praxis-models:Q4_K_M

LM Studio
Jan
Ollama
How to use praxis-app/praxis-models with Ollama:
```
ollama run hf.co/praxis-app/praxis-models:Q4_K_M
```

Unsloth Studio

How to use praxis-app/praxis-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 praxis-app/praxis-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 praxis-app/praxis-models to start chatting

Using HuggingFace Spaces for Unsloth

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

How to use praxis-app/praxis-models with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf praxis-app/praxis-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": "praxis-app/praxis-models:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

OpenClaw new

How to use praxis-app/praxis-models with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf praxis-app/praxis-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 "praxis-app/praxis-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"

Docker Model Runner
How to use praxis-app/praxis-models with Docker Model Runner:
```
docker model run hf.co/praxis-app/praxis-models:Q4_K_M
```

Lemonade

How to use praxis-app/praxis-models with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull praxis-app/praxis-models:Q4_K_M

Run and chat with the model

lemonade run user.praxis-models-Q4_K_M

List all available models

lemonade list

Hermes Agent

How to use praxis-app/praxis-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 praxis-app/praxis-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 praxis-app/praxis-models:Q4_K_M

Run Hermes

hermes

Atomic Chat

praxis-models

Model assets redistributed for the Praxis language-learning app.

This repository contains no original work. Every file here is a byte-for-byte copy of an upstream release, re-hosted only so the app has a stable, version-pinned download source. Nothing has been re-trained, re-quantized, or otherwise modified.

Why re-host at all?

The app downloads models on demand and verifies them with a SHA-256 that is compiled into the client. Pointing directly at an upstream repository would mean that any upstream change — a re-export, a file rename, a repository removal — silently breaks downloads for every already-shipped client. Re-hosting gives us a path we control; upstream is kept as a fallback mirror.

`sense-voice/` — SenseVoice ASR (int8)

File	Size (bytes)	SHA-256
`model.int8.onnx`	239,233,841	`c71f0ce00bec95b07744e116345e33d8cbbe08cef896382cf907bf4b51a2cd51`
`tokens.txt`	315,894	`f449eb28dc567533d7fa59be34e2abca8784f771850c78a47fb731a31429a1dc`

Source: csukuangfj/sherpa-onnx-sense-voice-zh-en-ja-ko-yue-2024-07-17 — ONNX export of SenseVoice by FunAudioLLM, exported for sherpa-onnx by Fangjun Kuang.

The SHA-256 of model.int8.onnx matches the upstream LFS pointer exactly, which is the check we use to confirm these are unmodified copies.

Languages: Chinese, English, Japanese, Korean, Cantonese.

`mt-qwen3-1.7b/` — Qwen3-1.7B machine translation (GGUF, Q4_K_M)

File	Size (bytes)	SHA-256
`Qwen3-1.7B-Q4_K_M.gguf`	1,107,409,472	`b139949c5bd74937ad8ed8c8cf3d9ffb1e99c866c823204dc42c0d91fa181897`

Source: unsloth/Qwen3-1.7B-GGUF (revision d7f544eead698dbd1f15126ef60b45a1e1933222, file Qwen3-1.7B-Q4_K_M.gguf) — GGUF quantization of Qwen3-1.7B by the Qwen team, quantized by Unsloth. Runs via llama.cpp.

The SHA-256 matches the upstream LFS pointer exactly.

Used for on-device translation so the app works offline and without per-request API cost.

Verifying

sha256sum model.int8.onnx tokens.txt

Any mismatch means the file was corrupted in transit or tampered with — do not use it.

License

Apache-2.0, inherited from the upstream releases. All credit for these models belongs to their original authors, cited above.

Downloads last month: 11

GGUF

Model size

2B params

Architecture

qwen3

Hardware compatibility

4-bit

praxis-app
/

praxis-models

praxis-models

Why re-host at all?

Contents

`sense-voice/` — SenseVoice ASR (int8)

`mt-qwen3-1.7b/` — Qwen3-1.7B machine translation (GGUF, Q4_K_M)

Verifying

License

praxis-models

Why re-host at all?

Contents

sense-voice/ — SenseVoice ASR (int8)

mt-qwen3-1.7b/ — Qwen3-1.7B machine translation (GGUF, Q4_K_M)

Verifying

License

`sense-voice/` — SenseVoice ASR (int8)

`mt-qwen3-1.7b/` — Qwen3-1.7B machine translation (GGUF, Q4_K_M)