Instructions to use B34st/Snowball with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use B34st/Snowball with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="B34st/Snowball",
	filename="Llama-3.1-8B-Lexi-Uncensored_V2_Q4.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 B34st/Snowball with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf B34st/Snowball
# Run inference directly in the terminal:
llama-cli -hf B34st/Snowball

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf B34st/Snowball
# Run inference directly in the terminal:
llama-cli -hf B34st/Snowball

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 B34st/Snowball
# Run inference directly in the terminal:
./llama-cli -hf B34st/Snowball

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 B34st/Snowball
# Run inference directly in the terminal:
./build/bin/llama-cli -hf B34st/Snowball

Use Docker

docker model run hf.co/B34st/Snowball

LM Studio
Jan
Ollama
How to use B34st/Snowball with Ollama:
```
ollama run hf.co/B34st/Snowball
```

Unsloth Studio new

How to use B34st/Snowball 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 B34st/Snowball 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 B34st/Snowball to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for B34st/Snowball to start chatting

Pi new

How to use B34st/Snowball with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf B34st/Snowball

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": "B34st/Snowball"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use B34st/Snowball with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf B34st/Snowball

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 B34st/Snowball

Run Hermes

hermes

Docker Model Runner
How to use B34st/Snowball with Docker Model Runner:
```
docker model run hf.co/B34st/Snowball
```

Lemonade

How to use B34st/Snowball with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull B34st/Snowball

Run and chat with the model

lemonade run user.Snowball-{{QUANT_TAG}}

List all available models

lemonade list

VERSION 2 Update Notes:

More compliant
Smarter
For best response, use this system prompt (feel free to expand upon it as you wish):

Think step by step with a logical reasoning and intellectual sense before you provide any response.

For more uncensored and compliant response, you can expand the system message differently, or simply enter a dot "." as system message.
IMPORTANT: Upon further investigation, the Q4 seems to have refusal issues sometimes. There seems to be some of the fine-tune loss happening due to the quantization. I will look into it for V3. Until then, I suggest you run F16 or Q8 if possible.

GENERAL INFO:

This model is based on Llama-3.1-8b-Instruct, and is governed by META LLAMA 3.1 COMMUNITY LICENSE AGREEMENT

Lexi is uncensored, which makes the model compliant. You are advised to implement your own alignment layer before exposing the model as a service. It will be highly compliant with any requests, even unethical ones.

You are responsible for any content you create using this model. Please use it responsibly.

Lexi is licensed according to Meta's Llama license. I grant permission for any use, including commercial, that falls within accordance with Meta's Llama-3.1 license.

IMPORTANT:

Use the same template as the official Llama 3.1 8B instruct. System tokens must be present during inference, even if you set an empty system message. If you are unsure, just add a short system message as you wish.

FEEDBACK:

If you find any issues or have suggestions for improvements, feel free to leave a review and I will look into it for upcoming improvements and next version.

Open LLM Leaderboard Evaluation Results

Detailed results can be found here

Metric	Value
Avg.	27.93
IFEval (0-Shot)	77.92
BBH (3-Shot)	29.69
MATH Lvl 5 (4-Shot)	16.92
GPQA (0-shot)	4.36
MuSR (0-shot)	7.77
MMLU-PRO (5-shot)	30.90

Downloads last month: 1

GGUF

Model size

8B params

Architecture

llama

Hardware compatibility

We're not able to determine the quantization variants.

View all variants

Inference Providers NEW

This model isn't deployed by any Inference Provider. 🙋 Ask for provider support

Evaluation results

strict accuracy on IFEval (0-Shot)
Open LLM Leaderboard

77.920
normalized accuracy on BBH (3-Shot)
Open LLM Leaderboard

29.690
exact match on MATH Lvl 5 (4-Shot)
Open LLM Leaderboard

16.920
acc_norm on GPQA (0-shot)
Open LLM Leaderboard

4.360
acc_norm on MuSR (0-shot)
Open LLM Leaderboard

7.770
accuracy on MMLU-PRO (5-shot)
test set Open LLM Leaderboard

30.900