Instructions to use Lord230/TaskFlowAi with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use Lord230/TaskFlowAi 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 Lord230/TaskFlowAi:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf Lord230/TaskFlowAi:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf Lord230/TaskFlowAi:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf Lord230/TaskFlowAi: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 Lord230/TaskFlowAi:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf Lord230/TaskFlowAi: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 Lord230/TaskFlowAi:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf Lord230/TaskFlowAi:Q4_K_M

Use Docker

docker model run hf.co/Lord230/TaskFlowAi:Q4_K_M

LM Studio
Jan

vLLM

How to use Lord230/TaskFlowAi with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Lord230/TaskFlowAi"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Lord230/TaskFlowAi",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Lord230/TaskFlowAi:Q4_K_M

Ollama
How to use Lord230/TaskFlowAi with Ollama:
```
ollama run hf.co/Lord230/TaskFlowAi:Q4_K_M
```
Unsloth Desktop

How to use Lord230/TaskFlowAi with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf Lord230/TaskFlowAi: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": "Lord230/TaskFlowAi:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use Lord230/TaskFlowAi with Docker Model Runner:
```
docker model run hf.co/Lord230/TaskFlowAi:Q4_K_M
```

Lemonade

How to use Lord230/TaskFlowAi with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull Lord230/TaskFlowAi:Q4_K_M

Run and chat with the model

lemonade run user.TaskFlowAi-Q4_K_M

List all available models

lemonade list

Hermes Agent

How to use Lord230/TaskFlowAi with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf Lord230/TaskFlowAi: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 Lord230/TaskFlowAi:Q4_K_M

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use Lord230/TaskFlowAi with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf Lord230/TaskFlowAi: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 "Lord230/TaskFlowAi: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"

TaskFlow AI — Qwen3 4B Instruct (Q4_K_M)

This repository contains the Q4_K_M GGUF quantization of Qwen3-4B-Instruct-2507, packaged by the official Ollama distribution as qwen3:4b-instruct-2507-q4_K_M.

This is an unmodified quantized base model. It has not been fine-tuned by TaskFlow AI. The upstream model is published under the Apache-2.0 license; see LICENSE and the upstream model card for details and attribution.

Files

Qwen3-4B-Instruct-2507-Q4_K_M.gguf — quantized model weights (about 2.5 GB).
Modelfile — settings to import the GGUF into Ollama.
LICENSE — license copied from the upstream model distribution.

Run with Ollama

Download the GGUF file and Modelfile into the same directory, then run:

ollama create taskflow-qwen3 -f Modelfile
ollama run taskflow-qwen3

Quantization

Base model: Qwen/Qwen3-4B-Instruct-2507
Format: GGUF
Quantization: Q4_K_M
Source package: qwen3:4b-instruct-2507-q4_K_M from the Ollama model library

Downloads last month: 7

GGUF

Model size

4B params

Architecture

qwen3

Hardware compatibility

4-bit

Model tree for Lord230/TaskFlowAi

Base model

Qwen/Qwen3-4B-Instruct-2507

Quantized

(315)

this model