Instructions to use Indexnusrefather/Llama-3.2-3B-Instruct-roleplay-tuned with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use Indexnusrefather/Llama-3.2-3B-Instruct-roleplay-tuned 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 Indexnusrefather/Llama-3.2-3B-Instruct-roleplay-tuned:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf Indexnusrefather/Llama-3.2-3B-Instruct-roleplay-tuned:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf Indexnusrefather/Llama-3.2-3B-Instruct-roleplay-tuned:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf Indexnusrefather/Llama-3.2-3B-Instruct-roleplay-tuned: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 Indexnusrefather/Llama-3.2-3B-Instruct-roleplay-tuned:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf Indexnusrefather/Llama-3.2-3B-Instruct-roleplay-tuned: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 Indexnusrefather/Llama-3.2-3B-Instruct-roleplay-tuned:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf Indexnusrefather/Llama-3.2-3B-Instruct-roleplay-tuned:Q4_K_M

Use Docker

docker model run hf.co/Indexnusrefather/Llama-3.2-3B-Instruct-roleplay-tuned:Q4_K_M

LM Studio
Jan

vLLM

How to use Indexnusrefather/Llama-3.2-3B-Instruct-roleplay-tuned with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Indexnusrefather/Llama-3.2-3B-Instruct-roleplay-tuned"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Indexnusrefather/Llama-3.2-3B-Instruct-roleplay-tuned",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Indexnusrefather/Llama-3.2-3B-Instruct-roleplay-tuned:Q4_K_M

Ollama
How to use Indexnusrefather/Llama-3.2-3B-Instruct-roleplay-tuned with Ollama:
```
ollama run hf.co/Indexnusrefather/Llama-3.2-3B-Instruct-roleplay-tuned:Q4_K_M
```

Unsloth Studio

How to use Indexnusrefather/Llama-3.2-3B-Instruct-roleplay-tuned 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 Indexnusrefather/Llama-3.2-3B-Instruct-roleplay-tuned 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 Indexnusrefather/Llama-3.2-3B-Instruct-roleplay-tuned to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for Indexnusrefather/Llama-3.2-3B-Instruct-roleplay-tuned to start chatting

How to use Indexnusrefather/Llama-3.2-3B-Instruct-roleplay-tuned with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf Indexnusrefather/Llama-3.2-3B-Instruct-roleplay-tuned: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": "Indexnusrefather/Llama-3.2-3B-Instruct-roleplay-tuned:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use Indexnusrefather/Llama-3.2-3B-Instruct-roleplay-tuned with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf Indexnusrefather/Llama-3.2-3B-Instruct-roleplay-tuned: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 Indexnusrefather/Llama-3.2-3B-Instruct-roleplay-tuned:Q4_K_M

Run Hermes

hermes

Atomic Chat new

OpenClaw new

How to use Indexnusrefather/Llama-3.2-3B-Instruct-roleplay-tuned with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf Indexnusrefather/Llama-3.2-3B-Instruct-roleplay-tuned: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 "Indexnusrefather/Llama-3.2-3B-Instruct-roleplay-tuned: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 Indexnusrefather/Llama-3.2-3B-Instruct-roleplay-tuned with Docker Model Runner:
```
docker model run hf.co/Indexnusrefather/Llama-3.2-3B-Instruct-roleplay-tuned:Q4_K_M
```

Lemonade

How to use Indexnusrefather/Llama-3.2-3B-Instruct-roleplay-tuned with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull Indexnusrefather/Llama-3.2-3B-Instruct-roleplay-tuned:Q4_K_M

Run and chat with the model

lemonade run user.Llama-3.2-3B-Instruct-roleplay-tuned-Q4_K_M

List all available models

lemonade list

What is it?

Its a roleplay tuned Llama 3.2 3B instruct, trained on 5M~ tokens of high quality roleplay data, it preserves original model's foundation and capabilities while at the same time refining its writing style. Just as any other model created by me, this one is my attempt at making local roleplay possible for any hardware.

How good is it?

Advantages:

Noticeably better writing
Noticeably less slop
Small size
Improved punctuation

Disadvantages:

Its still a 3b model, even if I did my best to make it better at roleplay

A word on the quants for this model:

BF16- Mostly overkill, however, highest quality
Q8_0- Amazing quality, near lossless
Q6_K- High quality, fast
Q5_K_M- Mid to high quality, small and fast
Q4_K_M- Mid quality, very small and very fast

Quants are located in the repo, along with safetensors

Downloads last month: 793

Safetensors

Model size

3B params

Tensor type

BF16

Model tree for Indexnusrefather/Llama-3.2-3B-Instruct-roleplay-tuned

Base model

meta-llama/Llama-3.2-3B-Instruct

Adapter

(803)

this model

Adapters

2 models

Indexnusrefather
/

Llama-3.2-3B-Instruct-roleplay-tuned

What is it?

How good is it?

Advantages:

Disadvantages:

A word on the quants for this model:

Model tree for Indexnusrefather/Llama-3.2-3B-Instruct-roleplay-tuned

Dataset used to train Indexnusrefather/Llama-3.2-3B-Instruct-roleplay-tuned