Instructions to use DevsDoCode/LLama-3-8b-Uncensored-Q3_K_M-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use DevsDoCode/LLama-3-8b-Uncensored-Q3_K_M-GGUF with Transformers:

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

pipe = pipeline("text-generation", model="DevsDoCode/LLama-3-8b-Uncensored-Q3_K_M-GGUF")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("DevsDoCode/LLama-3-8b-Uncensored-Q3_K_M-GGUF", dtype="auto")

llama-cpp-python

How to use DevsDoCode/LLama-3-8b-Uncensored-Q3_K_M-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="DevsDoCode/LLama-3-8b-Uncensored-Q3_K_M-GGUF",
	filename="llama-3-8b-uncensored.Q3_K_M.gguf",
)

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

Notebooks
Google Colab
Kaggle
Local Apps

llama.cpp

How to use DevsDoCode/LLama-3-8b-Uncensored-Q3_K_M-GGUF with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf DevsDoCode/LLama-3-8b-Uncensored-Q3_K_M-GGUF:Q3_K_M
# Run inference directly in the terminal:
llama-cli -hf DevsDoCode/LLama-3-8b-Uncensored-Q3_K_M-GGUF:Q3_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf DevsDoCode/LLama-3-8b-Uncensored-Q3_K_M-GGUF:Q3_K_M
# Run inference directly in the terminal:
llama-cli -hf DevsDoCode/LLama-3-8b-Uncensored-Q3_K_M-GGUF:Q3_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 DevsDoCode/LLama-3-8b-Uncensored-Q3_K_M-GGUF:Q3_K_M
# Run inference directly in the terminal:
./llama-cli -hf DevsDoCode/LLama-3-8b-Uncensored-Q3_K_M-GGUF:Q3_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 DevsDoCode/LLama-3-8b-Uncensored-Q3_K_M-GGUF:Q3_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf DevsDoCode/LLama-3-8b-Uncensored-Q3_K_M-GGUF:Q3_K_M

Use Docker

docker model run hf.co/DevsDoCode/LLama-3-8b-Uncensored-Q3_K_M-GGUF:Q3_K_M

LM Studio
Jan

vLLM

How to use DevsDoCode/LLama-3-8b-Uncensored-Q3_K_M-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "DevsDoCode/LLama-3-8b-Uncensored-Q3_K_M-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": "DevsDoCode/LLama-3-8b-Uncensored-Q3_K_M-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/DevsDoCode/LLama-3-8b-Uncensored-Q3_K_M-GGUF:Q3_K_M

SGLang

How to use DevsDoCode/LLama-3-8b-Uncensored-Q3_K_M-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 "DevsDoCode/LLama-3-8b-Uncensored-Q3_K_M-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": "DevsDoCode/LLama-3-8b-Uncensored-Q3_K_M-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 "DevsDoCode/LLama-3-8b-Uncensored-Q3_K_M-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": "DevsDoCode/LLama-3-8b-Uncensored-Q3_K_M-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Ollama
How to use DevsDoCode/LLama-3-8b-Uncensored-Q3_K_M-GGUF with Ollama:
```
ollama run hf.co/DevsDoCode/LLama-3-8b-Uncensored-Q3_K_M-GGUF:Q3_K_M
```

Unsloth Studio new

How to use DevsDoCode/LLama-3-8b-Uncensored-Q3_K_M-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 DevsDoCode/LLama-3-8b-Uncensored-Q3_K_M-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 DevsDoCode/LLama-3-8b-Uncensored-Q3_K_M-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for DevsDoCode/LLama-3-8b-Uncensored-Q3_K_M-GGUF to start chatting

Docker Model Runner
How to use DevsDoCode/LLama-3-8b-Uncensored-Q3_K_M-GGUF with Docker Model Runner:
```
docker model run hf.co/DevsDoCode/LLama-3-8b-Uncensored-Q3_K_M-GGUF:Q3_K_M
```

Lemonade

How to use DevsDoCode/LLama-3-8b-Uncensored-Q3_K_M-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull DevsDoCode/LLama-3-8b-Uncensored-Q3_K_M-GGUF:Q3_K_M

Run and chat with the model

lemonade run user.LLama-3-8b-Uncensored-Q3_K_M-GGUF-Q3_K_M

List all available models

lemonade list

Crafted with ❤️ by Devs Do Code (Sree)

GGUF Technical Specifications

Delve into the intricacies of GGUF, a meticulously crafted format that builds upon the robust foundation of the GGJT model. Tailored for heightened extensibility and user-centric functionality, GGUF introduces a suite of indispensable features:

Single-file Deployment: Streamline distribution and loading effortlessly. GGUF models have been meticulously architected for seamless deployment, necessitating no external files for supplementary information.

Extensibility: Safeguard the future of your models. GGUF seamlessly accommodates the integration of new features into GGML-based executors, ensuring compatibility with existing models.

mmap Compatibility: Prioritize efficiency. GGUF models are purposefully engineered to support mmap, facilitating rapid loading and saving, thus optimizing your workflow.

User-Friendly: Simplify your coding endeavors. Load and save models effortlessly, irrespective of the programming language used, obviating the dependency on external libraries.

Full Information: A comprehensive repository in a single file. GGUF models encapsulate all requisite information for loading, eliminating the need for users to furnish additional data.

The differentiator between GGJT and GGUF lies in the deliberate adoption of a key-value structure for hyperparameters (now termed metadata). Bid farewell to untyped lists, and embrace a structured approach that seamlessly accommodates new metadata without compromising compatibility with existing models. Augment your model with supplementary information for enhanced inference and model identification.

QUANTIZATION_METHODS:

Method	Quantization	Advantages	Trade-offs
q2_k	2-bit integers	Significant model size reduction	Minimal impact on accuracy
q3_k_l	3-bit integers	Balance between model size reduction and accuracy preservation	Moderate impact on accuracy
q3_k_m	3-bit integers	Enhanced accuracy with mixed precision	Increased computational complexity
q3_k_s	3-bit integers	Improved model efficiency with structured pruning	Reduced accuracy
q4_0	4-bit integers	Significant model size reduction	Moderate impact on accuracy
q4_1	4-bit integers	Enhanced accuracy with mixed precision	Increased computational complexity
q4_k_m	4-bit integers	Optimized model size and accuracy with mixed precision and structured pruning	Reduced accuracy
q4_k_s	4-bit integers	Improved model efficiency with structured pruning	Reduced accuracy
q5_0	5-bit integers	Balance between model size reduction and accuracy preservation	Moderate impact on accuracy
q5_1	5-bit integers	Enhanced accuracy with mixed precision	Increased computational complexity
q5_k_m	5-bit integers	Optimized model size and accuracy with mixed precision and structured pruning	Reduced accuracy
q5_k_s	5-bit integers	Improved model efficiency with structured pruning	Reduced accuracy
q6_k	6-bit integers	Balance between model size reduction and accuracy preservation	Moderate impact on accuracy
q8_0	8-bit integers	Significant model size reduction	Minimal impact on accuracy

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GGUF

Model size

8B params

Architecture

llama

Hardware compatibility

3-bit