Instructions to use QuantFactory/Gemma-2-2b-Chinese-it-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use QuantFactory/Gemma-2-2b-Chinese-it-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 QuantFactory/Gemma-2-2b-Chinese-it-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf QuantFactory/Gemma-2-2b-Chinese-it-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 QuantFactory/Gemma-2-2b-Chinese-it-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf QuantFactory/Gemma-2-2b-Chinese-it-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/Gemma-2-2b-Chinese-it-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf QuantFactory/Gemma-2-2b-Chinese-it-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/Gemma-2-2b-Chinese-it-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf QuantFactory/Gemma-2-2b-Chinese-it-GGUF:Q4_K_M

Use Docker

docker model run hf.co/QuantFactory/Gemma-2-2b-Chinese-it-GGUF:Q4_K_M

Llama new
LM Studio
Jan

vLLM

How to use QuantFactory/Gemma-2-2b-Chinese-it-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "QuantFactory/Gemma-2-2b-Chinese-it-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/Gemma-2-2b-Chinese-it-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/QuantFactory/Gemma-2-2b-Chinese-it-GGUF:Q4_K_M

Ollama
How to use QuantFactory/Gemma-2-2b-Chinese-it-GGUF with Ollama:
```
ollama run hf.co/QuantFactory/Gemma-2-2b-Chinese-it-GGUF:Q4_K_M
```
Unsloth Desktop
Docker Model Runner
How to use QuantFactory/Gemma-2-2b-Chinese-it-GGUF with Docker Model Runner:
```
docker model run hf.co/QuantFactory/Gemma-2-2b-Chinese-it-GGUF:Q4_K_M
```

Lemonade

How to use QuantFactory/Gemma-2-2b-Chinese-it-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull QuantFactory/Gemma-2-2b-Chinese-it-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.Gemma-2-2b-Chinese-it-GGUF-Q4_K_M

List all available models

lemonade list

Atomic Chat

QuantFactory/Gemma-2-2b-Chinese-it-GGUF

This is quantized version of stvlynn/Gemma-2-2b-Chinese-it created using llama.cpp

Original Model Card

Gemma-2-2b-Chinese-it (Gemma-2-2b-中文)

Intro

Gemma-2-2b-Chinese-it used approximately 6.4k rows of ruozhiba dataset to fine-tune Gemma-2-2b-it.

Gemma-2-2b-中文使用了约6.4k弱智吧数据对Gemma-2-2b-it进行微调

Demo

Usage

see Google's doc:

google/gemma-2-2b-it

If you have any questions or suggestions, feel free to contact me.

Twitter @stv_lynn

Telegram @stvlynn

email i@stv.pm

Downloads last month: 845

GGUF

Model size

3B params

Architecture

gemma2

Hardware compatibility

2-bit

3-bit

4-bit

5-bit

6-bit

8-bit

Model tree for QuantFactory/Gemma-2-2b-Chinese-it-GGUF

Base model

google/gemma-2-2b

Finetuned

google/gemma-2-2b-it

Quantized

(210)

this model

QuantFactory
/

Gemma-2-2b-Chinese-it-GGUF