- Libraries
- Transformers
How to use QuantFactory/Llama3-8B-Instruct-Replete-Adapted-GGUF with Transformers:
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("text-generation", model="QuantFactory/Llama3-8B-Instruct-Replete-Adapted-GGUF")
messages = [
{"role": "user", "content": "Who are you?"},
]
pipe(messages) # Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("QuantFactory/Llama3-8B-Instruct-Replete-Adapted-GGUF", dtype="auto") - llama-cpp-python
How to use QuantFactory/Llama3-8B-Instruct-Replete-Adapted-GGUF with llama-cpp-python:
# !pip install llama-cpp-python
from llama_cpp import Llama
llm = Llama.from_pretrained(
repo_id="QuantFactory/Llama3-8B-Instruct-Replete-Adapted-GGUF",
filename="Llama3-8B-Instruct-Replete-Adapted.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/Llama3-8B-Instruct-Replete-Adapted-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/Llama3-8B-Instruct-Replete-Adapted-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf QuantFactory/Llama3-8B-Instruct-Replete-Adapted-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/Llama3-8B-Instruct-Replete-Adapted-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf QuantFactory/Llama3-8B-Instruct-Replete-Adapted-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/Llama3-8B-Instruct-Replete-Adapted-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf QuantFactory/Llama3-8B-Instruct-Replete-Adapted-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/Llama3-8B-Instruct-Replete-Adapted-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf QuantFactory/Llama3-8B-Instruct-Replete-Adapted-GGUF:Q4_K_M
Use Docker
docker model run hf.co/QuantFactory/Llama3-8B-Instruct-Replete-Adapted-GGUF:Q4_K_M
- LM Studio
- Jan
- vLLM
How to use QuantFactory/Llama3-8B-Instruct-Replete-Adapted-GGUF with vLLM:
Install from pip and serve model
# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "QuantFactory/Llama3-8B-Instruct-Replete-Adapted-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/Llama3-8B-Instruct-Replete-Adapted-GGUF",
"messages": [
{
"role": "user",
"content": "What is the capital of France?"
}
]
}'Use Docker
docker model run hf.co/QuantFactory/Llama3-8B-Instruct-Replete-Adapted-GGUF:Q4_K_M
- SGLang
How to use QuantFactory/Llama3-8B-Instruct-Replete-Adapted-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/Llama3-8B-Instruct-Replete-Adapted-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/Llama3-8B-Instruct-Replete-Adapted-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/Llama3-8B-Instruct-Replete-Adapted-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/Llama3-8B-Instruct-Replete-Adapted-GGUF",
"messages": [
{
"role": "user",
"content": "What is the capital of France?"
}
]
}' - Ollama
How to use QuantFactory/Llama3-8B-Instruct-Replete-Adapted-GGUF with Ollama:
ollama run hf.co/QuantFactory/Llama3-8B-Instruct-Replete-Adapted-GGUF:Q4_K_M
- Unsloth Studio
How to use QuantFactory/Llama3-8B-Instruct-Replete-Adapted-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/Llama3-8B-Instruct-Replete-Adapted-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/Llama3-8B-Instruct-Replete-Adapted-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/Llama3-8B-Instruct-Replete-Adapted-GGUF to start chatting
- Docker Model Runner
How to use QuantFactory/Llama3-8B-Instruct-Replete-Adapted-GGUF with Docker Model Runner:
docker model run hf.co/QuantFactory/Llama3-8B-Instruct-Replete-Adapted-GGUF:Q4_K_M
- Lemonade
How to use QuantFactory/Llama3-8B-Instruct-Replete-Adapted-GGUF with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/
lemonade pull QuantFactory/Llama3-8B-Instruct-Replete-Adapted-GGUF:Q4_K_M
Run and chat with the model
lemonade run user.Llama3-8B-Instruct-Replete-Adapted-GGUF-Q4_K_M
List all available models
lemonade list
