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

Libraries

How to use google/gemma-2b with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="google/gemma-2b")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("google/gemma-2b")
model = AutoModelForCausalLM.from_pretrained("google/gemma-2b")

llama-cpp-python

How to use google/gemma-2b with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="google/gemma-2b",
	filename="gemma-2b.gguf",
)

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

Inference
Notebooks
Google Colab
Kaggle
Local Apps

llama.cpp

How to use google/gemma-2b with llama.cpp:

Install from brew

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

Install from WinGet (Windows)

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

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/gemma-2b
# Run inference directly in the terminal:
./llama-cli -hf google/gemma-2b

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/gemma-2b
# Run inference directly in the terminal:
./build/bin/llama-cli -hf google/gemma-2b

Use Docker

docker model run hf.co/google/gemma-2b

LM Studio
Jan

vLLM

How to use google/gemma-2b with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/google/gemma-2b

SGLang

How to use google/gemma-2b with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "google/gemma-2b" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "google/gemma-2b",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "google/gemma-2b" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "google/gemma-2b",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Ollama
How to use google/gemma-2b with Ollama:
```
ollama run hf.co/google/gemma-2b
```

Unsloth Studio new

How to use google/gemma-2b 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/gemma-2b 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/gemma-2b to start chatting

Using HuggingFace Spaces for Unsloth

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

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

Lemonade

How to use google/gemma-2b with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull google/gemma-2b

Run and chat with the model

lemonade run user.gemma-2b-{{QUANT_TAG}}

List all available models

lemonade list

torch.cuda.OutOfMemoryError

#26

by shiwanglai - opened Feb 28, 2024

Discussion

shiwanglai

Feb 28, 2024

torch.cuda.OutOfMemoryError: CUDA out of memory. Tried to allocate 128.00 MiB (GPU 0; 23.65 GiB total capacity; 5.93 GiB already allocated; 122.56 MiB free; 5.93 GiB reserved in total by PyTorch) If reserved memory is >> allocated memory try setting max_split_size_mb to avoid fragmentation. See documentation for Memory Management and PYTORCH_CUDA_ALLOC_CONF

ybelkada

Feb 28, 2024

Hi @shiwanglai
thanks for the issue ! can you share the snippet you're using ?

vince62s

Mar 6, 2024

•

edited Mar 6, 2024

@ybelkada
~/nlp/lm-evaluation-harness$ python lm_eval/main.py --model=hf --model_args pretrained=google/gemma-2b,load_in_4bit=True --tasks wikitext --batch_size 1
is going OOM not sure what's going on.
same
~/nlp/lm-evaluation-harness$ python lm_eval/main.py --model=hf --model_args pretrained=google/gemma-2b --tasks wikitext --batch_size 1

same with gemma-7b:

File "/home/vincent/miniconda3/envs/pt2.1.0/lib/python3.11/site-packages/transformers/models/gemma/modeling_gemma.py", line 1088, in forward
logits = logits.float()
^^^^^^^^^^^^^^
torch.cuda.OutOfMemoryError: CUDA out of memory. Tried to allocate 7.81 GiB. GPU 0 has a total capacity of 23.67 GiB of which 6.05 GiB is free. Including non-PyTorch memory, this process has 15.88 GiB memory in use. Of the allocated memory 13.06 GiB is allocated by PyTorch, and 2.52 GiB is reserved by PyTorch but unallocated. If reserved but unallocated memory is large try setting PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True to avoid fragmentation. See documentation for Memory Management (https://pytorch.org/docs/stable/notes/cuda.html#environment-variables)

vince62s

Mar 6, 2024

I reduced the max_length but there is still issues with gemma-7b (and gemma-2b is much higher than phi-2)

hf (pretrained=google/gemma-7b,max_length=512), gen_kwargs: (None), limit: None, num_fewshot: None, batch_size: 1

Tasks	Version	Filter	n-shot	Metric	Value		Stderr
wikitext	2	none	None	word_perplexity	42455038.3994	±	N/A
		none	None	byte_perplexity	26.6969	±	N/A
		none	None	bits_per_byte	4.7386	±	N/A

hf (pretrained=google/gemma-7b-it,max_length=512), gen_kwargs: (None), limit: None, num_fewshot: None, batch_size: 1

Tasks	Version	Filter	n-shot	Metric	Value		Stderr
wikitext	2	none	None	word_perplexity	1795.5652	±	N/A
		none	None	byte_perplexity	4.0602	±	N/A
		none	None	bits_per_byte	2.0216	±	N/A

hf (pretrained=google/gemma-7b,max_length=256), gen_kwargs: (None), limit: None, num_fewshot: None, batch_size: 1

Tasks	Version	Filter	n-shot	Metric	Value		Stderr
wikitext	2	none	None	word_perplexity	41037962.2523	±	N/A
		none	None	byte_perplexity	26.5280	±	N/A
		none	None	bits_per_byte	4.7294	±	N/A

hf (pretrained=google/gemma-2b,max_length=512), gen_kwargs: (None), limit: None, num_fewshot: None, batch_size: 1

Tasks	Version	Filter	n-shot	Metric	Value		Stderr
wikitext	2	none	None	word_perplexity	55.9289	±	N/A
		none	None	byte_perplexity	2.1223	±	N/A
		none	None	bits_per_byte	1.0857	±	N/A

hf (pretrained=google/gemma-2b-it,max_length=512), gen_kwargs: (None), limit: None, num_fewshot: None, batch_size: 1

Tasks	Version	Filter	n-shot	Metric	Value		Stderr
wikitext	2	none	None	word_perplexity	242.5852	±	N/A
		none	None	byte_perplexity	2.7924	±	N/A
		none	None	bits_per_byte	1.4815	±	N/A

glorgao

Aug 28, 2024

Actually, I faced OOM problem when using the DPO trainer for funetuning Gemma-2-2b-it, with 40G memory GPU and batchsize=2. Interesting.

GopiUppari

Google org Jan 23, 2025

Hi,

Apologies for the delay.

I have successfully reproduced the issue. To resolve it, please enable gradient checkpointing and reduce the batch size and sequence length. For more details, kindly refer to this gist file.

Thank you.

vpakarinen

Jan 23, 2025

•

edited Jan 23, 2025

Yeah, that's annoying error.

GopiUppari

Google org Mar 6, 2025

Hi @vpakarinen ,

Please follow the provided gist file for solving the error, if you still facing an issue. Could you please let us know.

Thank you.

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