How to use from
llama.cpp
Install from brew
brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf screenpipe/pipe-agent-v6-GGUF
# Run inference directly in the terminal:
llama-cli -hf screenpipe/pipe-agent-v6-GGUF
Install from WinGet (Windows)
winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf screenpipe/pipe-agent-v6-GGUF
# Run inference directly in the terminal:
llama-cli -hf screenpipe/pipe-agent-v6-GGUF
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 screenpipe/pipe-agent-v6-GGUF
# Run inference directly in the terminal:
./llama-cli -hf screenpipe/pipe-agent-v6-GGUF
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 screenpipe/pipe-agent-v6-GGUF
# Run inference directly in the terminal:
./build/bin/llama-cli -hf screenpipe/pipe-agent-v6-GGUF
Use Docker
docker model run hf.co/screenpipe/pipe-agent-v6-GGUF
Quick Links

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Check out the documentation for more information.

pipe-agent-v6 (GGUF)

Screenpipe pipe agent v6 โ€” fine-tuned Qwen3.5-9B with vision enabled.

What's new in v6

  • Multimodal: can process screenshots/images alongside text (model_type: qwen3_5, not qwen3_5_text)
  • Same tool-calling performance as v5 for text tasks
  • Vision tower preserved from pretrained Qwen3.5-9B (frozen during LoRA training)
  • Eval loss: 0.3210

Files

  • pipe-agent-v6-q4km.gguf โ€” text model (Q4_K_M)
  • pipe-agent-v6-mmproj-f16.gguf โ€” vision projector (F16)
  • Modelfile โ€” ollama config

Usage with Ollama

ollama create pipe-agent:v6 -f Modelfile
ollama run pipe-agent:v6
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GGUF
Model size
9B params
Architecture
qwen35
Hardware compatibility
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