Instructions to use QuantFactory/VersatiLlama-Llama-3.2-3B-Instruct-Abliterated-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use QuantFactory/VersatiLlama-Llama-3.2-3B-Instruct-Abliterated-GGUF with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="QuantFactory/VersatiLlama-Llama-3.2-3B-Instruct-Abliterated-GGUF")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("QuantFactory/VersatiLlama-Llama-3.2-3B-Instruct-Abliterated-GGUF", dtype="auto")

llama-cpp-python

How to use QuantFactory/VersatiLlama-Llama-3.2-3B-Instruct-Abliterated-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="QuantFactory/VersatiLlama-Llama-3.2-3B-Instruct-Abliterated-GGUF",
	filename="VersatiLlama-Llama-3.2-3B-Instruct-Abliterated.Q2_K.gguf",
)

llm.create_chat_completion(
	messages = [
		{
			"role": "user",
			"content": "What is the capital of France?"
		}
	]
)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use QuantFactory/VersatiLlama-Llama-3.2-3B-Instruct-Abliterated-GGUF with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf QuantFactory/VersatiLlama-Llama-3.2-3B-Instruct-Abliterated-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf QuantFactory/VersatiLlama-Llama-3.2-3B-Instruct-Abliterated-GGUF:Q4_K_M

Install from WinGet (Windows)

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

Use Docker

docker model run hf.co/QuantFactory/VersatiLlama-Llama-3.2-3B-Instruct-Abliterated-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use QuantFactory/VersatiLlama-Llama-3.2-3B-Instruct-Abliterated-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "QuantFactory/VersatiLlama-Llama-3.2-3B-Instruct-Abliterated-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": "QuantFactory/VersatiLlama-Llama-3.2-3B-Instruct-Abliterated-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/QuantFactory/VersatiLlama-Llama-3.2-3B-Instruct-Abliterated-GGUF:Q4_K_M

SGLang

How to use QuantFactory/VersatiLlama-Llama-3.2-3B-Instruct-Abliterated-GGUF with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "QuantFactory/VersatiLlama-Llama-3.2-3B-Instruct-Abliterated-GGUF" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "QuantFactory/VersatiLlama-Llama-3.2-3B-Instruct-Abliterated-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "QuantFactory/VersatiLlama-Llama-3.2-3B-Instruct-Abliterated-GGUF" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "QuantFactory/VersatiLlama-Llama-3.2-3B-Instruct-Abliterated-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Ollama
How to use QuantFactory/VersatiLlama-Llama-3.2-3B-Instruct-Abliterated-GGUF with Ollama:
```
ollama run hf.co/QuantFactory/VersatiLlama-Llama-3.2-3B-Instruct-Abliterated-GGUF:Q4_K_M
```

Unsloth Studio

How to use QuantFactory/VersatiLlama-Llama-3.2-3B-Instruct-Abliterated-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 QuantFactory/VersatiLlama-Llama-3.2-3B-Instruct-Abliterated-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 QuantFactory/VersatiLlama-Llama-3.2-3B-Instruct-Abliterated-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

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

How to use QuantFactory/VersatiLlama-Llama-3.2-3B-Instruct-Abliterated-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf QuantFactory/VersatiLlama-Llama-3.2-3B-Instruct-Abliterated-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": "QuantFactory/VersatiLlama-Llama-3.2-3B-Instruct-Abliterated-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use QuantFactory/VersatiLlama-Llama-3.2-3B-Instruct-Abliterated-GGUF with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf QuantFactory/VersatiLlama-Llama-3.2-3B-Instruct-Abliterated-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 QuantFactory/VersatiLlama-Llama-3.2-3B-Instruct-Abliterated-GGUF:Q4_K_M

Run Hermes

hermes

Atomic Chat new
Docker Model Runner
How to use QuantFactory/VersatiLlama-Llama-3.2-3B-Instruct-Abliterated-GGUF with Docker Model Runner:
```
docker model run hf.co/QuantFactory/VersatiLlama-Llama-3.2-3B-Instruct-Abliterated-GGUF:Q4_K_M
```

Lemonade

How to use QuantFactory/VersatiLlama-Llama-3.2-3B-Instruct-Abliterated-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull QuantFactory/VersatiLlama-Llama-3.2-3B-Instruct-Abliterated-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.VersatiLlama-Llama-3.2-3B-Instruct-Abliterated-GGUF-Q4_K_M

List all available models

lemonade list

QuantFactory/VersatiLlama-Llama-3.2-3B-Instruct-Abliterated-GGUF

This is quantized version of Devarui379/VersatiLlama-Llama-3.2-3B-Instruct-Abliterated created using llama.cpp

Original Model Card

Model Card for Model ID

VersatiLlama-Llama-3.2-3B-Instruct-Abliterated

Model Description

Small but Smart

Fine-Tuned on Vast dataset of Conversations

Able to Generate Human like text with high performance within its size.

It is Very Versatile when compared for it's size and Parameters and offers capability almost as good as Llama 3.1 8B Instruct

Feel free to Check it out!!

Check the quantized model here: Devarui379/VersatiLlama-Llama-3.2-3B-Instruct-Abliterated-Imatrix-GGUF

[This model was trained for 5hrs on GPU T4 15gb vram]

Developed by: Meta AI
Fine-Tuned by: Devarui379
Model type: Transformers
Language(s) (NLP): English
License: cc-by-4.0

Model Sources [optional]

base model:meta-llama/Llama-3.2-3B-Instruct

Repository: Devarui379/VersatiLlama-Llama-3.2-3B-Instruct-Abliterated
Demo: Use LM Studio with the Quantized version

Uses

Use desired System prompt when using in LM Studio The optimal chat template seems to be Jinja but feel free to test it out as you want!

Technical Specifications

Model Architecture and Objective

Llama 3.2

Hardware

NVIDIA TESLA T4 GPU 15GB VRAM

Downloads last month: 1,563

GGUF

Model size

3B params

Architecture

llama

Hardware compatibility

2-bit

3-bit

4-bit

5-bit

6-bit

8-bit

Model tree for QuantFactory/VersatiLlama-Llama-3.2-3B-Instruct-Abliterated-GGUF

Base model

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

Quantized

(466)

this model