Instructions to use Forceless/DeepPresenter-9B-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

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

Add pipeline tag and improve model card documentation

by nielsr HF Staff - opened Mar 15

base: refs/heads/main

←

from: refs/pr/1

Discussion Files changed

+29

-5

Files changed (1) hide show

README.md +29 -5

README.md CHANGED Viewed

@@ -1,30 +1,54 @@
 ---
-license: mit
 base_model:
 - Forceless/DeepPresenter-9B
 ---
 > 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:
 ```bash
 uvx pptagent onboard
 uvx pptagent generate "Single Page with Title: Hello World" -o hello.pptx
 ```
 ## Performance
-![image](https://cdn-uploads.huggingface.co/production/uploads/64380d17819f3ab20d17595b/PZgEPAPir2ygRvxtqtXnx.png)

 ---
 base_model:
 - Forceless/DeepPresenter-9B
+license: mit
+pipeline_tag: text-generation
 ---
 > 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](https://github.com/icip-cas/PPTAgent)
+**Paper**: [DeepPresenter: Environment-Grounded Reflection for Agentic Presentation Generation](https://huggingface.co/papers/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.
+DeepPresenter is an agentic framework that adapts to diverse user intents and enables effective feedback-driven refinement. Unlike existing agents that rely on self-reflection over internal signals, DeepPresenter uses environment-grounded reflection to condition the generation process on perceptual artifact states (e.g., rendered slides), allowing it to identify and correct presentation-specific issues during execution.
 ## Usage
+DeepPresenter-9B is intended to be used with the **PPTAgent framework** for full presentation generation. You can use the command-line interface provided by the project:
 ```bash
+# Install `uv` for package management
+curl -LsSf https://astral.sh/uv/install.sh | sh
+# Interactive configuration (first time)
 uvx pptagent onboard
+# Generate presentation
 uvx pptagent generate "Single Page with Title: Hello World" -o hello.pptx
 ```
 ## Performance
+![image](https://cdn-uploads.huggingface.co/production/uploads/64380d17819f3ab20d17595b/PZgEPAPir2ygRvxtqtXnx.png)
+## Citation
+If you find this project helpful, please use the following to cite it:
+```bibtex
+@misc{zheng2026deeppresenterenvironmentgroundedreflectionagentic,
+      title={DeepPresenter: Environment-Grounded Reflection for Agentic Presentation Generation},
+      author={Hao Zheng and Guozhao Mo and Xinru Yan and Qianhao Yuan and Wenkai Zhang and Xuanang Chen and Yaojie Lu and Hongyu Lin and Xianpei Han and Le Sun},
+      year={2026},
+      eprint={2602.22839},
+      archivePrefix={arXiv},
+      primaryClass={cs.AI},
+      url={https://arxiv.org/abs/2602.22839},
+}
+```