Instructions to use google/codegemma-1.1-2b-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use google/codegemma-1.1-2b-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="google/codegemma-1.1-2b-GGUF",
	filename="codegemma-1.1-2b-f16.gguf",
)

output = llm(
	"Once upon a time,",
	max_tokens=512,
	echo=True
)
print(output)

Notebooks
Google Colab
Kaggle
Local Apps

llama.cpp

How to use google/codegemma-1.1-2b-GGUF with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf google/codegemma-1.1-2b-GGUF:F16
# Run inference directly in the terminal:
llama-cli -hf google/codegemma-1.1-2b-GGUF:F16

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf google/codegemma-1.1-2b-GGUF:F16
# Run inference directly in the terminal:
llama-cli -hf google/codegemma-1.1-2b-GGUF:F16

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 google/codegemma-1.1-2b-GGUF:F16
# Run inference directly in the terminal:
./llama-cli -hf google/codegemma-1.1-2b-GGUF:F16

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 google/codegemma-1.1-2b-GGUF:F16
# Run inference directly in the terminal:
./build/bin/llama-cli -hf google/codegemma-1.1-2b-GGUF:F16

Use Docker

docker model run hf.co/google/codegemma-1.1-2b-GGUF:F16

LM Studio
Jan

vLLM

How to use google/codegemma-1.1-2b-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "google/codegemma-1.1-2b-GGUF"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "google/codegemma-1.1-2b-GGUF",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/google/codegemma-1.1-2b-GGUF:F16

Ollama
How to use google/codegemma-1.1-2b-GGUF with Ollama:
```
ollama run hf.co/google/codegemma-1.1-2b-GGUF:F16
```

Unsloth Studio new

How to use google/codegemma-1.1-2b-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 google/codegemma-1.1-2b-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 google/codegemma-1.1-2b-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for google/codegemma-1.1-2b-GGUF to start chatting

Docker Model Runner
How to use google/codegemma-1.1-2b-GGUF with Docker Model Runner:
```
docker model run hf.co/google/codegemma-1.1-2b-GGUF:F16
```

Lemonade

How to use google/codegemma-1.1-2b-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull google/codegemma-1.1-2b-GGUF:F16

Run and chat with the model

lemonade run user.codegemma-1.1-2b-GGUF-F16

List all available models

lemonade list

Access CodeGemma on Hugging Face

This repository is publicly accessible, but you have to accept the conditions to access its files and content.

To access Gemma on Hugging Face, you’re required to review and agree to Google’s usage license. To do this, please ensure you’re logged-in to Hugging Face and click below. Requests are processed immediately.

CodeGemma

Model Page : CodeGemma

Resources and Technical Documentation : Technical Report : Responsible Generative AI Toolkit

Authors : Google

In llama.cpp, and other related tools such as Ollama and LM Studio, please make sure that you have these flags set correctly, especially repeat-penalty. Georgi Gerganov (llama.cpp's author) shared his experience in https://huggingface.co/google/gemma-7b-it/discussions/38#65d7b14adb51f7c160769fa1.

Description

CodeGemma is a collection of lightweight open code models built on top of Gemma. CodeGemma models are text-to-text and text-to-code decoder-only models and are available as a 7 billion pretrained variant that specializes in code completion and code generation tasks, a 7 billion parameter instruction-tuned variant for code chat and instruction following and a 2 billion parameter pretrained variant for fast code completion.

	codegemma-2b	codegemma-7b	codegemma-7b-it
Code Completion	✅	✅
Generation from natural language		✅	✅
Chat			✅
Instruction Following			✅

For detailed model card, refer to https://huggingface.co/google/codegemma-1.1-2b.

Sample Usage

$ cat non_prime
/// Write a rust function to identify non-prime numbers.
///
/// Examples:
/// >>> is_not_prime(2)
/// False
/// >>> is_not_prime(10)
/// True
pub fn is_not_prime(n: i32) -> bool {
$ main -m codegemma-1.1-2b.gguf --temp 0 --top-k 0 -f non_prime --log-disable --repeat-penalty 1.0
 /// Write a rust function to identify non-prime numbers.
///
/// Examples:
/// >>> is_not_prime(2)
/// False
/// >>> is_not_prime(10)
/// True
pub fn is_not_prime(n: i32) -> bool {
    for i in 2..n {
        if n % i == 0 {
            return true;
        }
    }
    false
}
<|file_separator|>

Coding Benchmarks

Benchmark	2B	2B (1.1)	7B	7B-IT	7B-IT (1.1)
HumanEval	31.1	37.8	44.5	56.1	60.4
MBPP	43.6	49.2	56.2	54.2	55.6
HumanEval Single Line	78.4	79.3	76.1	68.3	77.4
HumanEval Multi Line	51.4	51.0	58.4	20.1	23.7
BC HE C++	24.2	19.9	32.9	42.2	46.6
BC HE C#	10.6	26.1	22.4	26.7	54.7
BC HE Go	20.5	18.0	21.7	28.6	34.2
BC HE Java	29.2	29.8	41.0	48.4	50.3
BC HE JavaScript	21.7	28.0	39.8	46.0	48.4
BC HE Kotlin	28.0	32.3	39.8	51.6	47.8
BC HE Python	21.7	36.6	42.2	48.4	54.0
BC HE Rust	26.7	24.2	34.1	36.0	37.3
BC MBPP C++	47.1	38.9	53.8	56.7	63.5
BC MBPP C#	28.7	45.3	32.5	41.2	62.0
BC MBPP Go	45.6	38.9	43.3	46.2	53.2
BC MBPP Java	41.8	49.7	50.3	57.3	62.9
BC MBPP JavaScript	45.3	45.0	58.2	61.4	61.4
BC MBPP Kotlin	46.8	49.7	54.7	59.9	62.6
BC MBPP Python	38.6	52.9	59.1	62.0	60.2
BC MBPP Rust	45.3	47.4	52.9	53.5	52.3