Instructions to use chenghenry/gemma-2-27b-it-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use chenghenry/gemma-2-27b-it-GGUF with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("chenghenry/gemma-2-27b-it-GGUF", dtype="auto") - llama-cpp-python
How to use chenghenry/gemma-2-27b-it-GGUF with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="chenghenry/gemma-2-27b-it-GGUF", filename="gemma-2-27b-it-IQ3_M.gguf", )
llm.create_chat_completion( messages = "No input example has been defined for this model task." )
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use chenghenry/gemma-2-27b-it-GGUF with llama.cpp:
Install from brew
brew install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf chenghenry/gemma-2-27b-it-GGUF:IQ3_M # Run inference directly in the terminal: llama-cli -hf chenghenry/gemma-2-27b-it-GGUF:IQ3_M
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf chenghenry/gemma-2-27b-it-GGUF:IQ3_M # Run inference directly in the terminal: llama-cli -hf chenghenry/gemma-2-27b-it-GGUF:IQ3_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 chenghenry/gemma-2-27b-it-GGUF:IQ3_M # Run inference directly in the terminal: ./llama-cli -hf chenghenry/gemma-2-27b-it-GGUF:IQ3_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 chenghenry/gemma-2-27b-it-GGUF:IQ3_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf chenghenry/gemma-2-27b-it-GGUF:IQ3_M
Use Docker
docker model run hf.co/chenghenry/gemma-2-27b-it-GGUF:IQ3_M
- LM Studio
- Jan
- Ollama
How to use chenghenry/gemma-2-27b-it-GGUF with Ollama:
ollama run hf.co/chenghenry/gemma-2-27b-it-GGUF:IQ3_M
- Unsloth Studio
How to use chenghenry/gemma-2-27b-it-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 chenghenry/gemma-2-27b-it-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 chenghenry/gemma-2-27b-it-GGUF to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for chenghenry/gemma-2-27b-it-GGUF to start chatting
- Docker Model Runner
How to use chenghenry/gemma-2-27b-it-GGUF with Docker Model Runner:
docker model run hf.co/chenghenry/gemma-2-27b-it-GGUF:IQ3_M
- Lemonade
How to use chenghenry/gemma-2-27b-it-GGUF with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull chenghenry/gemma-2-27b-it-GGUF:IQ3_M
Run and chat with the model
lemonade run user.gemma-2-27b-it-GGUF-IQ3_M
List all available models
lemonade list
Model
- Quantized Gemma 2 27B Instruction Tuned with IQ3_M
- Fit a single T4 (16GB)
Usage (llama-cli with GPU):
llama-cli -m ./gemma-2-27b-it-IQ3_M.gguf -ngl 42 --temp 0 --repeat-penalty 1.0 --color -p "Why is the sky blue?"
Usage (llama-cli with CPU):
llama-cli -m ./gemma-2-27b-it-IQ3_M.gguf --temp 0 --repeat-penalty 1.0 --color -p "Why is the sky blue?"
Usage (llama-cpp-python via Hugging Face Hub):
from llama_cpp import Llama
llm = Llama.from_pretrained(
repo_id="chenghenry/gemma-2-27b-it-GGUF ",
filename="gemma-2-27b-it-IQ3_M.gguf",
n_ctx=8192,
n_batch=2048,
n_gpu_layers=100,
verbose=False,
chat_format="gemma"
)
prompt = "Why is the sky blue?"
messages = [{"role": "user", "content": prompt}]
response = llm.create_chat_completion(
messages=messages,
repeat_penalty=1.0,
temperature=0)
print(response["choices"][0]["message"]["content"])
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