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 Desktop

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 @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": "praxis-app/praxis-models:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

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

OpenClaw

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"

hooliganbeer commited on Jul 31

Commit

29579d0

verified ·

1 Parent(s): 307c8bf

Add Qwen3-1.7B Q4_K_M GGUF (byte-for-byte redistribution of unsloth's quantization)

Browse files

Files changed (3) hide show

.gitattributes +1 -0
README.md +15 -0
mt-qwen3-1.7b/Qwen3-1.7B-Q4_K_M.gguf +3 -0

.gitattributes CHANGED Viewed

@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text

 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+mt-qwen3-1.7b/Qwen3-1.7B-Q4_K_M.gguf filter=lfs diff=lfs merge=lfs -text

README.md CHANGED Viewed

@@ -47,6 +47,21 @@ the check we use to confirm these are unmodified copies.
 Languages: Chinese, English, Japanese, Korean, Cantonese.
 ## Verifying
 ```bash

 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`](https://huggingface.co/unsloth/Qwen3-1.7B-GGUF)
+(revision `d7f544eead698dbd1f15126ef60b45a1e1933222`, file `Qwen3-1.7B-Q4_K_M.gguf`) —
+GGUF quantization of [Qwen3-1.7B](https://huggingface.co/Qwen/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
 ```bash

mt-qwen3-1.7b/Qwen3-1.7B-Q4_K_M.gguf ADDED Viewed

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+size 1107409472