Instructions to use mkenfenheuer/evora-qwen3-1.7b-v1-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use mkenfenheuer/evora-qwen3-1.7b-v1-GGUF 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 mkenfenheuer/evora-qwen3-1.7b-v1-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf mkenfenheuer/evora-qwen3-1.7b-v1-GGUF:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf mkenfenheuer/evora-qwen3-1.7b-v1-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf mkenfenheuer/evora-qwen3-1.7b-v1-GGUF: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 mkenfenheuer/evora-qwen3-1.7b-v1-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf mkenfenheuer/evora-qwen3-1.7b-v1-GGUF: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 mkenfenheuer/evora-qwen3-1.7b-v1-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf mkenfenheuer/evora-qwen3-1.7b-v1-GGUF:Q4_K_M

Use Docker

docker model run hf.co/mkenfenheuer/evora-qwen3-1.7b-v1-GGUF:Q4_K_M

LM Studio
Jan
Ollama
How to use mkenfenheuer/evora-qwen3-1.7b-v1-GGUF with Ollama:
```
ollama run hf.co/mkenfenheuer/evora-qwen3-1.7b-v1-GGUF:Q4_K_M
```
Unsloth Desktop

How to use mkenfenheuer/evora-qwen3-1.7b-v1-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf mkenfenheuer/evora-qwen3-1.7b-v1-GGUF: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": "mkenfenheuer/evora-qwen3-1.7b-v1-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use mkenfenheuer/evora-qwen3-1.7b-v1-GGUF with Docker Model Runner:
```
docker model run hf.co/mkenfenheuer/evora-qwen3-1.7b-v1-GGUF:Q4_K_M
```

Lemonade

How to use mkenfenheuer/evora-qwen3-1.7b-v1-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull mkenfenheuer/evora-qwen3-1.7b-v1-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.evora-qwen3-1.7b-v1-GGUF-Q4_K_M

List all available models

lemonade list

Hermes Agent

How to use mkenfenheuer/evora-qwen3-1.7b-v1-GGUF with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf mkenfenheuer/evora-qwen3-1.7b-v1-GGUF: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 mkenfenheuer/evora-qwen3-1.7b-v1-GGUF:Q4_K_M

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use mkenfenheuer/evora-qwen3-1.7b-v1-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf mkenfenheuer/evora-qwen3-1.7b-v1-GGUF: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 "mkenfenheuer/evora-qwen3-1.7b-v1-GGUF: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"

Evora on-device text model (Qwen3-1.7B, v1)

The on-device fallback for four Evora text tasks: meal_plan.stub, meal_plan.details, coaching.rate_item, meal.identify.text.

File	Size
`evora-qwen3-1.7b-v1.Q4_K_M.gguf`	1.0 GB

Prompt

System prompt (the model was trained on exactly this; the task and language vary):

You are Evora's on-device model. Task: {task}. Language: de (German). Reply with the JSON object only.

User message: the same JSON payload the Evora API sends to its server model for that task. The answer is one compact JSON object in the Evora API's production schema.

Qwen3 must be prompted with thinking off (enable_thinking: false in the chat template, --jinja in llama.cpp); apply a JSON grammar only after that, or the grammar forces the model's reasoning into a string.

Training

Distilled from gpt-5.4 answering Evora's production prompts, in AI Studio. LoRA on the language model, merged, converted with llama.cpp. English and German.

Data: 5,168 rows (inputs shaped like the app's real payloads; item ratings on Open Food Facts products, ODbL). Held-out: 100% valid JSON in the production schema; item grades within one step of gpt-5.4 91% of the time.

Downloads last month: 73

GGUF

Model size

2B params

Architecture

qwen3

Hardware compatibility

4-bit

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Model tree for mkenfenheuer/evora-qwen3-1.7b-v1-GGUF

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