Instructions to use Forceless/DeepPresenter-9B-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- llama-cpp-python
How to use Forceless/DeepPresenter-9B-GGUF with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="Forceless/DeepPresenter-9B-GGUF", filename="DeepPresenter-9B-GGUF-Q4_K_M.gguf", )
llm.create_chat_completion( messages = "No input example has been defined for this model task." )
- Notebooks
- Google Colab
- Kaggle
- Local Apps
- llama.cpp
How to use Forceless/DeepPresenter-9B-GGUF with llama.cpp:
Install from brew
brew install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf Forceless/DeepPresenter-9B-GGUF:Q4_K_M # Run inference directly in the terminal: llama-cli -hf Forceless/DeepPresenter-9B-GGUF:Q4_K_M
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf Forceless/DeepPresenter-9B-GGUF:Q4_K_M # Run inference directly in the terminal: llama-cli -hf Forceless/DeepPresenter-9B-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 Forceless/DeepPresenter-9B-GGUF:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf Forceless/DeepPresenter-9B-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 Forceless/DeepPresenter-9B-GGUF:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf Forceless/DeepPresenter-9B-GGUF:Q4_K_M
Use Docker
docker model run hf.co/Forceless/DeepPresenter-9B-GGUF:Q4_K_M
- LM Studio
- Jan
- Ollama
How to use Forceless/DeepPresenter-9B-GGUF with Ollama:
ollama run hf.co/Forceless/DeepPresenter-9B-GGUF:Q4_K_M
- Unsloth Studio new
How to use Forceless/DeepPresenter-9B-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 Forceless/DeepPresenter-9B-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 Forceless/DeepPresenter-9B-GGUF to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for Forceless/DeepPresenter-9B-GGUF to start chatting
- Pi new
How to use Forceless/DeepPresenter-9B-GGUF with Pi:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama-server -hf Forceless/DeepPresenter-9B-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": "Forceless/DeepPresenter-9B-GGUF:Q4_K_M" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Hermes Agent new
How to use Forceless/DeepPresenter-9B-GGUF with Hermes Agent:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama-server -hf Forceless/DeepPresenter-9B-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 Forceless/DeepPresenter-9B-GGUF:Q4_K_M
Run Hermes
hermes
- Docker Model Runner
How to use Forceless/DeepPresenter-9B-GGUF with Docker Model Runner:
docker model run hf.co/Forceless/DeepPresenter-9B-GGUF:Q4_K_M
- Lemonade
How to use Forceless/DeepPresenter-9B-GGUF with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull Forceless/DeepPresenter-9B-GGUF:Q4_K_M
Run and chat with the model
lemonade run user.DeepPresenter-9B-GGUF-Q4_K_M
List all available models
lemonade list
This is a quantized version of the model for lightweight deployment on edge devices such as Macs.
DeepPresenter-9B
Project: https://github.com/icip-cas/PPTAgent
Paper: https://arxiv.org/abs/2602.22839
Model Overview
DeepPresenter-9B is a 9B-parameter language model designed for automatic presentation generation. It serves as the core model in the DeepPresenter framework, enabling agentic workflows that generate structured slide presentations from natural language instructions.
Usage
DeepPresenter-9B is intended to be used with the PPTAgent framework for full presentation generation:
uvx pptagent onboard
uvx pptagent generate "Single Page with Title: Hello World" -o hello.pptx
Performance
- Downloads last month
- 567
4-bit
16-bit
