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 Settings

llama.cpp

How to use Forceless/DeepPresenter-9B-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 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 serve -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

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

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 serve -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 serve -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

Atomic Chat new

OpenClaw new

How to use Forceless/DeepPresenter-9B-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf Forceless/DeepPresenter-9B-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 "Forceless/DeepPresenter-9B-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 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

nielsr

Mar 15

Hi! I'm Niels, part of the community science team at Hugging Face.

I've opened this PR to enhance the model card for DeepPresenter-9B. My changes include:

Adding the text-generation pipeline tag to the metadata for better discoverability.
Improving the model overview with details from the associated research paper.
Ensuring the official GitHub repository and paper are properly linked.
Adding the BibTeX citation for the paper.

This documentation helps users better understand and cite your work!

Add pipeline tag and improve model card documentatione4baaee7

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