Instructions to use AliceThirty/G4-Alice-v1.0-31B-gguf with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use AliceThirty/G4-Alice-v1.0-31B-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 AliceThirty/G4-Alice-v1.0-31B-gguf:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf AliceThirty/G4-Alice-v1.0-31B-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 AliceThirty/G4-Alice-v1.0-31B-gguf:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf AliceThirty/G4-Alice-v1.0-31B-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 AliceThirty/G4-Alice-v1.0-31B-gguf:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf AliceThirty/G4-Alice-v1.0-31B-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 AliceThirty/G4-Alice-v1.0-31B-gguf:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf AliceThirty/G4-Alice-v1.0-31B-gguf:Q4_K_M

Use Docker

docker model run hf.co/AliceThirty/G4-Alice-v1.0-31B-gguf:Q4_K_M

LM Studio
Jan

vLLM

How to use AliceThirty/G4-Alice-v1.0-31B-gguf with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "AliceThirty/G4-Alice-v1.0-31B-gguf"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "AliceThirty/G4-Alice-v1.0-31B-gguf",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/AliceThirty/G4-Alice-v1.0-31B-gguf:Q4_K_M

Ollama
How to use AliceThirty/G4-Alice-v1.0-31B-gguf with Ollama:
```
ollama run hf.co/AliceThirty/G4-Alice-v1.0-31B-gguf:Q4_K_M
```
Unsloth Desktop

How to use AliceThirty/G4-Alice-v1.0-31B-gguf with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf AliceThirty/G4-Alice-v1.0-31B-gguf:Q4_K_M

Configure the model in Pi

# Install Pi:
npm install -g @earendil-works/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "AliceThirty/G4-Alice-v1.0-31B-gguf:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use AliceThirty/G4-Alice-v1.0-31B-gguf with Docker Model Runner:
```
docker model run hf.co/AliceThirty/G4-Alice-v1.0-31B-gguf:Q4_K_M
```

Lemonade

How to use AliceThirty/G4-Alice-v1.0-31B-gguf with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull AliceThirty/G4-Alice-v1.0-31B-gguf:Q4_K_M

Run and chat with the model

lemonade run user.G4-Alice-v1.0-31B-gguf-Q4_K_M

List all available models

lemonade list

Hermes Agent

How to use AliceThirty/G4-Alice-v1.0-31B-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 AliceThirty/G4-Alice-v1.0-31B-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 AliceThirty/G4-Alice-v1.0-31B-gguf:Q4_K_M

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use AliceThirty/G4-Alice-v1.0-31B-gguf with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf AliceThirty/G4-Alice-v1.0-31B-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 "AliceThirty/G4-Alice-v1.0-31B-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"

AliceThirty commited on Jun 18

Commit

c2ea96f

verified ·

1 Parent(s): 4757a87

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+---
+datasets:
+- zerofata/Instruct-Anime
+- zerofata/Gemini-3.1-Pro-GLM5-Characters
+- zerofata/Gemini-3.1-Pro-SmallWiki
+base_model:
+- ReadyArt/Serenity-31B-GGUF
+pipeline_tag: text-generation
+---
+<div style="display: flex; align-items: flex-start; gap: 20px;">
+  <img src="https://cdn-uploads.huggingface.co/production/uploads/63283c7ecbc744f197047b4a/R8FtVqy5K9cp1uwzTlMJf.png" width="150" height="auto" />
+  <div>I like <a href="https://huggingface.co/zerofata/G4-MeroMero-31B">MeroMero</a>, but it's too censored and not HORNY enough. So I tried to merge it with <a href="https://huggingface.co/ReadyArt/Serenity-31B">Serenity</a> (which I declare is the horniest model ever made). After many many tries, I came to the conclusion that these models couldn't be merged. It always produced gibberish. So I changed my plans and decided to retrain MeroMero (as I understand their training) but with Serenity as a base. It seems to have worked well, so I am sharing my first attempt with you.
+Please tell me what you think. Is it clever? Does it have a good long term memory? Is it uninhibited? Sloppy?
+It's my first fine-tune, so any feedback is welcome!
+The fine tune was done on a single rtx 5090 thanks to unsloth, with a lora rank of 80, for one epoch, and a low learning rate not to override Serenity (but maybe I should have choosen an even lower learning rate).</div>
+</div>