Instructions to use cuticle999/gemma-2-27b-gguf with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use cuticle999/gemma-2-27b-gguf with llama.cpp:
Install (macOS, Linux)
curl -LsSf https://llama.app/install.sh | sh # Start a local OpenAI-compatible server with a web UI: llama serve -hf cuticle999/gemma-2-27b-gguf:Q4_K_M_QUANTIZED # Run inference directly in the terminal: llama cli -hf cuticle999/gemma-2-27b-gguf:Q4_K_M_QUANTIZED
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf cuticle999/gemma-2-27b-gguf:Q4_K_M_QUANTIZED # Run inference directly in the terminal: llama cli -hf cuticle999/gemma-2-27b-gguf:Q4_K_M_QUANTIZED
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 cuticle999/gemma-2-27b-gguf:Q4_K_M_QUANTIZED # Run inference directly in the terminal: ./llama-cli -hf cuticle999/gemma-2-27b-gguf:Q4_K_M_QUANTIZED
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 cuticle999/gemma-2-27b-gguf:Q4_K_M_QUANTIZED # Run inference directly in the terminal: ./build/bin/llama-cli -hf cuticle999/gemma-2-27b-gguf:Q4_K_M_QUANTIZED
Use Docker
docker model run hf.co/cuticle999/gemma-2-27b-gguf:Q4_K_M_QUANTIZED
- LM Studio
- Jan
- Ollama
How to use cuticle999/gemma-2-27b-gguf with Ollama:
ollama run hf.co/cuticle999/gemma-2-27b-gguf:Q4_K_M_QUANTIZED
- Unsloth Studio
How to use cuticle999/gemma-2-27b-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 cuticle999/gemma-2-27b-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 cuticle999/gemma-2-27b-gguf to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for cuticle999/gemma-2-27b-gguf to start chatting
- Docker Model Runner
How to use cuticle999/gemma-2-27b-gguf with Docker Model Runner:
docker model run hf.co/cuticle999/gemma-2-27b-gguf:Q4_K_M_QUANTIZED
- Lemonade
How to use cuticle999/gemma-2-27b-gguf with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull cuticle999/gemma-2-27b-gguf:Q4_K_M_QUANTIZED
Run and chat with the model
lemonade run user.gemma-2-27b-gguf-Q4_K_M_QUANTIZED
List all available models
lemonade list
- Atomic Chat
勉強の一環で下記のモデルをllama.cppを使用してggufに変換したものです。
https://huggingface.co/google/gemma-2-27b
https://github.com/ggerganov/llama.cpp
colabでの量子化方法例
!pip install gguf torch numpy sentencepiece huggingface_hub
!git clone https://github.com/ggerganov/llama.cpp
%cd llama.cpp
!cmake -B build -DGGML_CUDA=ON
!cmake --build build --config Release -j 8
from huggingface_hub import snapshot_download
# モデルはお好みで変えてください。
model_path = snapshot_download(repo_id="google/gemma-2-27b")
# f16に一度変換します。一度変換しないとq4_k_mにできないです。
!python convert_hf_to_gguf.py {model_path} \
--outfile gemma-24b-f16.gguf \
--outtype f16
!./build/bin/llama-quantize \
gemma-24b-q4_k_m.gguf \
gemma-24b-q4_k_m_quantized.gguf \
Q4_K_M
推論例
!./build/llama-cli \
-m /content/gemma-24b-f16.gguf \
-n 200 \
--n-gpu-layers 35 \
--threads 8 \
-p "こんにちは!"
ライセンス
https://www.llama.com/llama3_1/license/
https://github.com/ggerganov/llama.cpp?tab=MIT-1-ov-file
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Base model
google/gemma-2-27b