Instructions to use saikanov/gemma2-9B-cpt-sahabatai-instruct-v1-Q4-K-M-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 saikanov/gemma2-9B-cpt-sahabatai-instruct-v1-Q4-K-M-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 saikanov/gemma2-9B-cpt-sahabatai-instruct-v1-Q4-K-M-GGUF:Q4_K_M # Run inference directly in the terminal: llama cli -hf saikanov/gemma2-9B-cpt-sahabatai-instruct-v1-Q4-K-M-GGUF:Q4_K_M
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf saikanov/gemma2-9B-cpt-sahabatai-instruct-v1-Q4-K-M-GGUF:Q4_K_M # Run inference directly in the terminal: llama cli -hf saikanov/gemma2-9B-cpt-sahabatai-instruct-v1-Q4-K-M-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 saikanov/gemma2-9B-cpt-sahabatai-instruct-v1-Q4-K-M-GGUF:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf saikanov/gemma2-9B-cpt-sahabatai-instruct-v1-Q4-K-M-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 saikanov/gemma2-9B-cpt-sahabatai-instruct-v1-Q4-K-M-GGUF:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf saikanov/gemma2-9B-cpt-sahabatai-instruct-v1-Q4-K-M-GGUF:Q4_K_M
Use Docker
docker model run hf.co/saikanov/gemma2-9B-cpt-sahabatai-instruct-v1-Q4-K-M-GGUF:Q4_K_M
- LM Studio
- Jan
- Ollama
How to use saikanov/gemma2-9B-cpt-sahabatai-instruct-v1-Q4-K-M-GGUF with Ollama:
ollama run hf.co/saikanov/gemma2-9B-cpt-sahabatai-instruct-v1-Q4-K-M-GGUF:Q4_K_M
- Unsloth Desktop
- Docker Model Runner
How to use saikanov/gemma2-9B-cpt-sahabatai-instruct-v1-Q4-K-M-GGUF with Docker Model Runner:
docker model run hf.co/saikanov/gemma2-9B-cpt-sahabatai-instruct-v1-Q4-K-M-GGUF:Q4_K_M
- Lemonade
How to use saikanov/gemma2-9B-cpt-sahabatai-instruct-v1-Q4-K-M-GGUF with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull saikanov/gemma2-9B-cpt-sahabatai-instruct-v1-Q4-K-M-GGUF:Q4_K_M
Run and chat with the model
lemonade run user.gemma2-9B-cpt-sahabatai-instruct-v1-Q4-K-M-GGUF-Q4_K_M
List all available models
lemonade list
- Atomic Chat
Gemma-2-9B-CPT-SahabatAI-Instruct GGUF
This is a GGUF quantized version of Gemma 2 9B, fine-tuned with custom instructions by SahabatAI and optimized for CPU inference using Q4_K_M quantization.
Model Details
- Base Model: Gemma 2 9B
- Instruction Format: SahabatAI Instruct v1
- Quantization: GGUF Q4_K_M (4-bit with Medium precision for Key/Value cache)
- Original Size: 18GB
- Quantized Size: ~5GB
- Context Length: 8192 tokens
- License: Gemma Terms of Use
Description
This model is a quantized version of Gemma 2 9B, fine-tuned with custom instruction format by SahabatAI. The Q4_K_M quantization provides a good balance between model size, speed, and quality. The instruction format is optimized for general-purpose tasks while maintaining model coherence and reliability.
Usage
oobabooga's text-generation-webui Setup
- Install text-generation-webui:
git clone https://github.com/oobabooga/text-generation-webui
cd text-generation-webui
run start-* depends on your OS
- Download Model:
mkdir models
cd models
# Download gemma2-9B-cpt-sahabatai-instruct-v1-Q4_K_M.gguf from Hugging Face
Recommended Generation Parameters
temperature: 0.7
top_p: 0.9
top_k: 40
repetition_penalty: 1.1
max_new_tokens: 2048
Example Outputs
<|system|>You are a helpful AI assistant.</|system|>
<|user|>What is the capital of Indonesia?</|user|>
<|assistant|>Jakarta is the capital city of Indonesia. It is located on the northwestern coast of Java, the most populous island in Indonesia. Jakarta serves as the country's economic, cultural, and political center.
<|user|>Write a simple Python function to calculate factorial.</|user|>
<|assistant|>Here's a simple recursive function to calculate factorial:
def factorial(n):
if n == 0 or n == 1:
return 1
return n * factorial(n-1)
License
This model is subject to the Gemma Terms of Use. Please refer to Google's Gemma licensing terms for commercial usage.
Acknowledgments
- SahabatAI for fine-tuning the model
- Google for the Gemma 2 base model
- llama.cpp for GGUF conversion tools
- oobabooga for text-generation-webui
Support
For issues and questions:
- Open an issue in this repository
- Discord: [Your Discord Link]
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