Libraries PEFT How to use shredder-31/Llamma-3_QG_V.2.0 with PEFT:
from peft import PeftModel
from transformers import AutoModelForCausalLM
base_model = AutoModelForCausalLM.from_pretrained("unsloth/llama-3-8b-bnb-4bit")
model = PeftModel.from_pretrained(base_model, "shredder-31/Llamma-3_QG_V.2.0") llama-cpp-python How to use shredder-31/Llamma-3_QG_V.2.0 with llama-cpp-python:
# !pip install llama-cpp-python
from llama_cpp import Llama
llm = Llama.from_pretrained(
repo_id="shredder-31/Llamma-3_QG_V.2.0",
filename="Llamma-3_QG_V.2.0-unsloth.F16.gguf",
)
output = llm(
"Once upon a time,",
max_tokens=512,
echo=True
)
print(output) Notebooks Google Colab Kaggle Local Apps Settings llama.cpp How to use shredder-31/Llamma-3_QG_V.2.0 with llama.cpp:
Install from brew brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf shredder-31/Llamma-3_QG_V.2.0:F16
# Run inference directly in the terminal:
llama-cli -hf shredder-31/Llamma-3_QG_V.2.0:F16 Install from WinGet (Windows) winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf shredder-31/Llamma-3_QG_V.2.0:F16
# Run inference directly in the terminal:
llama-cli -hf shredder-31/Llamma-3_QG_V.2.0:F16 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 shredder-31/Llamma-3_QG_V.2.0:F16
# Run inference directly in the terminal:
./llama-cli -hf shredder-31/Llamma-3_QG_V.2.0:F16 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 shredder-31/Llamma-3_QG_V.2.0:F16
# Run inference directly in the terminal:
./build/bin/llama-cli -hf shredder-31/Llamma-3_QG_V.2.0:F16 Use Docker docker model run hf.co/shredder-31/Llamma-3_QG_V.2.0:F16 LM Studio Jan vLLM How to use shredder-31/Llamma-3_QG_V.2.0 with vLLM:
Install from pip and serve model # Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "shredder-31/Llamma-3_QG_V.2.0"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
-H "Content-Type: application/json" \
--data '{
"model": "shredder-31/Llamma-3_QG_V.2.0",
"prompt": "Once upon a time,",
"max_tokens": 512,
"temperature": 0.5
}' Use Docker docker model run hf.co/shredder-31/Llamma-3_QG_V.2.0:F16 Ollama How to use shredder-31/Llamma-3_QG_V.2.0 with Ollama:
ollama run hf.co/shredder-31/Llamma-3_QG_V.2.0:F16 Unsloth Studio How to use shredder-31/Llamma-3_QG_V.2.0 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 shredder-31/Llamma-3_QG_V.2.0 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 shredder-31/Llamma-3_QG_V.2.0 to start chatting Using HuggingFace Spaces for Unsloth # No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for shredder-31/Llamma-3_QG_V.2.0 to start chatting Docker Model Runner How to use shredder-31/Llamma-3_QG_V.2.0 with Docker Model Runner:
docker model run hf.co/shredder-31/Llamma-3_QG_V.2.0:F16 Lemonade How to use shredder-31/Llamma-3_QG_V.2.0 with Lemonade:
Pull the model # Download Lemonade from https://lemonade-server.ai/
lemonade pull shredder-31/Llamma-3_QG_V.2.0:F16 Run and chat with the model lemonade run user.Llamma-3_QG_V.2.0-F16 List all available models lemonade list 
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("unsloth/llama-3-8b-bnb-4bit") model = PeftModel.from_pretrained(base_model, "shredder-31/Llamma-3_QG_V.2.0")