Instructions to use kh0pp/agentflow-planner-7b-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 kh0pp/agentflow-planner-7b-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 kh0pp/agentflow-planner-7b-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf kh0pp/agentflow-planner-7b-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 kh0pp/agentflow-planner-7b-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf kh0pp/agentflow-planner-7b-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 kh0pp/agentflow-planner-7b-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf kh0pp/agentflow-planner-7b-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 kh0pp/agentflow-planner-7b-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf kh0pp/agentflow-planner-7b-GGUF:Q4_K_M

Use Docker

docker model run hf.co/kh0pp/agentflow-planner-7b-GGUF:Q4_K_M

LM Studio
Jan
Ollama
How to use kh0pp/agentflow-planner-7b-GGUF with Ollama:
```
ollama run hf.co/kh0pp/agentflow-planner-7b-GGUF:Q4_K_M
```

Unsloth Studio

How to use kh0pp/agentflow-planner-7b-GGUF 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 kh0pp/agentflow-planner-7b-GGUF 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 kh0pp/agentflow-planner-7b-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for kh0pp/agentflow-planner-7b-GGUF to start chatting

How to use kh0pp/agentflow-planner-7b-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf kh0pp/agentflow-planner-7b-GGUF: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": "kh0pp/agentflow-planner-7b-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

OpenClaw new

How to use kh0pp/agentflow-planner-7b-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf kh0pp/agentflow-planner-7b-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 "kh0pp/agentflow-planner-7b-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"

Docker Model Runner
How to use kh0pp/agentflow-planner-7b-GGUF with Docker Model Runner:
```
docker model run hf.co/kh0pp/agentflow-planner-7b-GGUF:Q4_K_M
```

Lemonade

How to use kh0pp/agentflow-planner-7b-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull kh0pp/agentflow-planner-7b-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.agentflow-planner-7b-GGUF-Q4_K_M

List all available models

lemonade list

Hermes Agent

How to use kh0pp/agentflow-planner-7b-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 kh0pp/agentflow-planner-7b-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 kh0pp/agentflow-planner-7b-GGUF:Q4_K_M

Run Hermes

hermes

Atomic Chat

agentflow-planner-7b-GGUF / Modelfile

kh0pp

Upload Modelfile with huggingface_hub

67a9d43 verified 10 months ago

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938 Bytes

	# Modelfile for AgentFlow Planner 7B
	# Based on Qwen2.5-7B-Instruct, specialized for planning and agentic reasoning
	# Source: https://huggingface.co/AgentFlow/agentflow-planner-7b

	FROM AgentFlow/agentflow-planner-7b

	# Set parameters optimized for planning tasks
	PARAMETER temperature 0.7
	PARAMETER top_p 0.9
	PARAMETER top_k 40
	PARAMETER num_ctx 32768
	PARAMETER repeat_penalty 1.1

	# System prompt optimized for agentic planning
	TEMPLATE """{{ if .System }}<\|im_start\|>system
	{{ .System }}<\|im_end\|>
	{{ end }}{{ if .Prompt }}<\|im_start\|>user
	{{ .Prompt }}<\|im_end\|>
	{{ end }}<\|im_start\|>assistant
	{{ .Response }}<\|im_end\|>
	"""

	SYSTEM """You are an advanced AI agent specialized in planning and reasoning. You excel at breaking down complex tasks into manageable steps, analyzing dependencies, and creating effective execution plans. You can use tools and integrate information from multiple sources to solve problems systematically."""