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

Libraries

How to use boapro/WRT_II with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="boapro/WRT_II",
	filename="WRT_Llama-3.1-2-8B-Q4_K_S.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 boapro/WRT_II with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf boapro/WRT_II:Q4_K_S
# Run inference directly in the terminal:
llama-cli -hf boapro/WRT_II:Q4_K_S

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf boapro/WRT_II:Q4_K_S
# Run inference directly in the terminal:
llama-cli -hf boapro/WRT_II:Q4_K_S

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 boapro/WRT_II:Q4_K_S
# Run inference directly in the terminal:
./llama-cli -hf boapro/WRT_II:Q4_K_S

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 boapro/WRT_II:Q4_K_S
# Run inference directly in the terminal:
./build/bin/llama-cli -hf boapro/WRT_II:Q4_K_S

Use Docker

docker model run hf.co/boapro/WRT_II:Q4_K_S

LM Studio
Jan

vLLM

How to use boapro/WRT_II with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/boapro/WRT_II:Q4_K_S

Ollama
How to use boapro/WRT_II with Ollama:
```
ollama run hf.co/boapro/WRT_II:Q4_K_S
```

Unsloth Studio new

How to use boapro/WRT_II 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 boapro/WRT_II 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 boapro/WRT_II to start chatting

Using HuggingFace Spaces for Unsloth

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

Docker Model Runner
How to use boapro/WRT_II with Docker Model Runner:
```
docker model run hf.co/boapro/WRT_II:Q4_K_S
```

Lemonade

How to use boapro/WRT_II with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull boapro/WRT_II:Q4_K_S

Run and chat with the model

lemonade run user.WRT_II-Q4_K_S

List all available models

lemonade list

Llamacpp imatrix Quantizations of meta-llama/Llama-3.1-8B

Using llama.cpp release b3878 for quantization.

Original model: https://huggingface.co/meta-llama/Llama-3.1-8B

Run it in LM Studio

Prompt format

<|begin_of_text|><|start_header_id|>system<|end_header_id|>

{system_prompt}<|eot_id|><|start_header_id|>user<|end_header_id|>

{prompt}<|eot_id|><|start_header_id|>assistant<|end_header_id|>

Downloading using huggingface-cli

First, make sure you have hugginface-cli installed:

pip install -U "huggingface_hub[cli]"

Then, you can target the specific file you want:

If the model is bigger than 50GB, it will have been split into multiple files. In order to download them all to a local folder, run:

You can either specify a new local-dir (boapro/WRT_II) or download them all in place (./)

Q4_0_X_X

If you're using an ARM chip, the Q4_0_X_X quants will have a substantial speedup. Check out Q4_0_4_4 speed comparisons on the original pull request

To check which one would work best for your ARM chip, you can check AArch64 SoC features (thanks EloyOn!).

If you want to get more into the weeds, you can check out this extremely useful feature chart:

llama.cpp feature matrix

Downloads last month: 5

GGUF

Model size

8B params

Architecture

llama

Hardware compatibility

4-bit

Model tree for boapro/WRT_II

Base model

meta-llama/Llama-3.1-8B

Quantized

(323)

this model

boapro
/

WRT_II

Llamacpp imatrix Quantizations of meta-llama/Llama-3.1-8B

Prompt format

Downloading using huggingface-cli

Q4_0_X_X

Model tree for boapro/WRT_II

Datasets used to train boapro/WRT_II