Instructions to use chatpdflocal/gemma2-instruct-gguf with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- llama-cpp-python
How to use chatpdflocal/gemma2-instruct-gguf with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="chatpdflocal/gemma2-instruct-gguf", filename="gemma-2-2B-instruct-F16.gguf", )
llm.create_chat_completion( messages = "No input example has been defined for this model task." )
- Notebooks
- Google Colab
- Kaggle
- Local Apps
- llama.cpp
How to use chatpdflocal/gemma2-instruct-gguf with llama.cpp:
Install from brew
brew install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf chatpdflocal/gemma2-instruct-gguf:Q4_K_M # Run inference directly in the terminal: llama-cli -hf chatpdflocal/gemma2-instruct-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 chatpdflocal/gemma2-instruct-gguf:Q4_K_M # Run inference directly in the terminal: llama-cli -hf chatpdflocal/gemma2-instruct-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 chatpdflocal/gemma2-instruct-gguf:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf chatpdflocal/gemma2-instruct-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 chatpdflocal/gemma2-instruct-gguf:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf chatpdflocal/gemma2-instruct-gguf:Q4_K_M
Use Docker
docker model run hf.co/chatpdflocal/gemma2-instruct-gguf:Q4_K_M
- LM Studio
- Jan
- Ollama
How to use chatpdflocal/gemma2-instruct-gguf with Ollama:
ollama run hf.co/chatpdflocal/gemma2-instruct-gguf:Q4_K_M
- Unsloth Studio new
How to use chatpdflocal/gemma2-instruct-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 chatpdflocal/gemma2-instruct-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 chatpdflocal/gemma2-instruct-gguf to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for chatpdflocal/gemma2-instruct-gguf to start chatting
- Docker Model Runner
How to use chatpdflocal/gemma2-instruct-gguf with Docker Model Runner:
docker model run hf.co/chatpdflocal/gemma2-instruct-gguf:Q4_K_M
- Lemonade
How to use chatpdflocal/gemma2-instruct-gguf with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull chatpdflocal/gemma2-instruct-gguf:Q4_K_M
Run and chat with the model
lemonade run user.gemma2-instruct-gguf-Q4_K_M
List all available models
lemonade list
llm.create_chat_completion(
messages = "No input example has been defined for this model task."
)The Gemma 2 model family, developed by Google, consists of lightweight, open-source models that demonstrate state-of-the-art performance across various tasks.
This repo covers 2b and 9b gemma2 different size gguf models, which are very applicable for deploying and using in PCs, laptops or mobiles.
Useful local intelligent documents assistant AI tools:
MyDocs is a desktop App which supports Windows and MacOS, and especially for professionals who demand absolute privacy. 100% local processing ensuring total documents sovereignty and zero-cloud dependency.
If you are using Zotero for managing and reading your personal PDFs, PapersGPT is a free plugin which can assist you to chat PDFs effectively by your local gemma2.
You can download the beautiful ChatPDFLocal MacOS app from here, load one or batch PDF files at will, and quickly experience the effect of the model through chat reading.
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# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="chatpdflocal/gemma2-instruct-gguf", filename="", )