kh0pp/agentflow-planner-7b-GGUF

AgentFlow Planner 7B - GGUF

Quantized GGUF versions of AgentFlow/agentflow-planner-7b for efficient local inference.

📋 Model Details

AgentFlow Planner 7B is a specialized language model fine-tuned from Qwen2.5-7B-Instruct, designed specifically for planning and agentic reasoning tasks. This model excels at breaking down complex tasks into manageable steps, analyzing dependencies, and creating effective execution plans.

Base Model Information

• Base: Qwen2.5-7B-Instruct
• Parameters: 7.62 billion
• Context Length: 32,768 tokens
• License: MIT
• Specialization: Planning, multi-step reasoning, tool integration
• Original Repository: AgentFlow/agentflow-planner-7b
• Research: AgentFlow GitHub

About AgentFlow

AgentFlow is an advanced AI framework with four specialized modules:

• Planner (this model): Strategic task decomposition and planning
• Executor: Action execution
• Verifier: Result validation
• Generator: Output synthesis

The Planner model has been shown to outperform larger models like GPT-4o on certain planning benchmarks.

📦 Available Quantizations

All quantizations were created using llama.cpp's latest quantization methods.

Filename	Quant	Size	Use Case	Memory Required
agentflow-planner-7b-f16.gguf	F16	15.0 GB	Full precision, best quality	~17 GB
agentflow-planner-7b-Q8_0.gguf	Q8_0	7.6 GB	Near-full quality, faster	~10 GB
agentflow-planner-7b-Q5_K_M.gguf	Q5_K_M	5.1 GB	High quality	~7 GB
agentflow-planner-7b-Q4_K_M.gguf	Q4_K_M	4.4 GB	⭐ Recommended - Best balance	~6 GB

Quantization Recommendations

• Q4_K_M: Best for most users - excellent quality/speed/size balance
• Q5_K_M: When you need slightly higher quality and have more VRAM
• Q8_0: Maximum quality while still being smaller than F16
• F16: Research or when you need absolute best quality

🚀 Usage

Ollama (Recommended)

Quick Start:

# Download the Q4_K_M model
huggingface-cli download kh0pp/agentflow-planner-7b-GGUF agentflow-planner-7b-Q4_K_M.gguf --local-dir .

# Create Modelfile
cat > Modelfile << 'EOF'
FROM ./agentflow-planner-7b-Q4_K_M.gguf

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|>
"""

PARAMETER temperature 0.7
PARAMETER top_p 0.9
PARAMETER top_k 40
PARAMETER num_ctx 32768
PARAMETER repeat_penalty 1.1

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."""
EOF

# Create and run
ollama create agentflow-planner:7b -f Modelfile
ollama run agentflow-planner:7b

llama.cpp

# Download the model
huggingface-cli download kh0pp/agentflow-planner-7b-GGUF agentflow-planner-7b-Q4_K_M.gguf --local-dir .

# Run with llama.cpp
./llama-cli -m agentflow-planner-7b-Q4_K_M.gguf \
  -p "Create a detailed plan for building a web application" \
  -n 512 -c 4096

LM Studio

1. Download any GGUF file from this repository
2. Load it in LM Studio
3. Use the Qwen2 chat template
4. Recommended settings:
   • Temperature: 0.7
   • Top P: 0.9
   • Context: 32768

Python (llama-cpp-python)

from llama_cpp import Llama

llm = Llama(
    model_path="agentflow-planner-7b-Q4_K_M.gguf",
    n_ctx=32768,
    n_gpu_layers=-1,  # Use GPU acceleration
)

response = llm.create_chat_completion(
    messages=[
        {"role": "system", "content": "You are an advanced AI agent specialized in planning and reasoning."},
        {"role": "user", "content": "Create a detailed project plan for developing a mobile app"}
    ],
    temperature=0.7,
    max_tokens=512,
)

print(response['choices'][0]['message']['content'])

💡 Example Use Cases

This model excels at:

• Project Planning: Breaking down complex projects into phases and tasks
• Code Architecture: Designing system architectures and implementation strategies
• Research Planning: Creating research methodologies and experiment designs
• Workflow Optimization: Analyzing and improving processes
• Multi-Step Problem Solving: Decomposing complex problems into solvable steps
• Tool Integration: Planning how to use multiple tools to accomplish goals

🔧 Technical Details

• Quantization Method: llama.cpp Q4_K_M, Q5_K_M, Q8_0, F16
• Original Format: SafeTensors (7 files, ~30GB)
• Conversion Tool: llama.cpp convert_hf_to_gguf.py
• Tested With: Ollama 0.1.9+, llama.cpp (latest), LM Studio 0.2.9+

📊 Performance Notes

• Q4_K_M provides the best balance for most use cases with minimal quality loss
• Q5_K_M offers slightly better quality at the cost of ~15% larger file size
• Q8_0 provides near-original quality, useful for critical planning tasks
• F16 is the full precision version, recommended only for research or quality comparison

🙏 Credits

• Original Model: AgentFlow Team
• Base Model: Qwen Team
• Quantization: kh0pp
• Tools: llama.cpp by @ggerganov and contributors

📄 License

MIT License - Same as the original AgentFlow Planner model.

🔗 Links

• Original Model: https://huggingface.co/AgentFlow/agentflow-planner-7b
• AgentFlow Research: https://github.com/lupantech/AgentFlow
• llama.cpp: https://github.com/ggerganov/llama.cpp
• Ollama: https://ollama.ai

---

First GGUF quantization of AgentFlow Planner 7B. If you find this useful, consider starring the original model repository!