Instructions to use boapro/WRT_II with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- llama-cpp-python
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:
- Downloads last month
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4-bit
Model tree for boapro/WRT_II
Base model
meta-llama/Llama-3.1-8B