Instructions to use theprint/Llama-3-8B-Lexi-Smaug-Uncensored with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use theprint/Llama-3-8B-Lexi-Smaug-Uncensored with Transformers:

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("theprint/Llama-3-8B-Lexi-Smaug-Uncensored", dtype="auto")

llama-cpp-python

How to use theprint/Llama-3-8B-Lexi-Smaug-Uncensored with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="theprint/Llama-3-8B-Lexi-Smaug-Uncensored",
	filename="Llama-3-8B-Lexi-Smaug-Uncensored.BF16.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 theprint/Llama-3-8B-Lexi-Smaug-Uncensored 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 theprint/Llama-3-8B-Lexi-Smaug-Uncensored:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf theprint/Llama-3-8B-Lexi-Smaug-Uncensored:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf theprint/Llama-3-8B-Lexi-Smaug-Uncensored:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf theprint/Llama-3-8B-Lexi-Smaug-Uncensored: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 theprint/Llama-3-8B-Lexi-Smaug-Uncensored:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf theprint/Llama-3-8B-Lexi-Smaug-Uncensored: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 theprint/Llama-3-8B-Lexi-Smaug-Uncensored:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf theprint/Llama-3-8B-Lexi-Smaug-Uncensored:Q4_K_M

Use Docker

docker model run hf.co/theprint/Llama-3-8B-Lexi-Smaug-Uncensored:Q4_K_M

LM Studio
Jan
Ollama
How to use theprint/Llama-3-8B-Lexi-Smaug-Uncensored with Ollama:
```
ollama run hf.co/theprint/Llama-3-8B-Lexi-Smaug-Uncensored:Q4_K_M
```

Unsloth Studio

How to use theprint/Llama-3-8B-Lexi-Smaug-Uncensored 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 theprint/Llama-3-8B-Lexi-Smaug-Uncensored 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 theprint/Llama-3-8B-Lexi-Smaug-Uncensored to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for theprint/Llama-3-8B-Lexi-Smaug-Uncensored to start chatting

Atomic Chat new
Docker Model Runner
How to use theprint/Llama-3-8B-Lexi-Smaug-Uncensored with Docker Model Runner:
```
docker model run hf.co/theprint/Llama-3-8B-Lexi-Smaug-Uncensored:Q4_K_M
```

Lemonade

How to use theprint/Llama-3-8B-Lexi-Smaug-Uncensored with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull theprint/Llama-3-8B-Lexi-Smaug-Uncensored:Q4_K_M

Run and chat with the model

lemonade run user.Llama-3-8B-Lexi-Smaug-Uncensored-Q4_K_M

List all available models

lemonade list

Configuration Parsing Warning:Invalid JSON for config file config.json

Llama-3-8B-Lexi-Smaug-Uncensored

Llama-3-8B-Lexi-Smaug-Uncensored is a merge of the following models using LazyMergekit:

👀 Looking for GGUF?

Static quants are available at https://huggingface.co/mradermacher/Llama-3-8B-Lexi-Smaug-Uncensored-GGUF

Weighted/imatrix quants are available at https://huggingface.co/mradermacher/Llama-3-8B-Lexi-Smaug-Uncensored-i1-GGUF

🧩 Configuration

slices:
  - sources:
      - model: Orenguteng/Llama-3-8B-Lexi-Uncensored
        layer_range: [0, 32]
      - model: abacusai/Llama-3-Smaug-8B
        layer_range: [0, 32]
merge_method: slerp
base_model: Orenguteng/Llama-3-8B-Lexi-Uncensored
parameters:
  t:
    - filter: self_attn
      value: [0, 0.5, 0.3, 0.7, 1]
    - filter: mlp
      value: [1, 0.5, 0.7, 0.3, 0]
    - value: 0.5
dtype: bfloat16

💻 Usage

!pip install -qU transformers accelerate

from transformers import AutoTokenizer
import transformers
import torch

model = "theprint/Llama-3-8B-Lexi-Smaug-Uncensored"
messages = [{"role": "user", "content": "What is a large language model?"}]

tokenizer = AutoTokenizer.from_pretrained(model)
prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
pipeline = transformers.pipeline(
    "text-generation",
    model=model,
    torch_dtype=torch.float16,
    device_map="auto",
)

outputs = pipeline(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95)
print(outputs[0]["generated_text"])