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

Libraries

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

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

pipe = pipeline("text-generation", model="google/gemma-2b-it")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("google/gemma-2b-it")
model = AutoModelForCausalLM.from_pretrained("google/gemma-2b-it")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

llama-cpp-python

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

# !pip install llama-cpp-python

from llama_cpp import Llama

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

llm.create_chat_completion(
	messages = [
		{
			"role": "user",
			"content": "What is the capital of France?"
		}
	]
)

Inference
Notebooks
Google Colab
Kaggle
Local Apps

llama.cpp

How to use google/gemma-2b-it 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-it
# Run inference directly in the terminal:
llama-cli -hf google/gemma-2b-it

Install from WinGet (Windows)

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

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

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

Use Docker

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

LM Studio
Jan

vLLM

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

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "google/gemma-2b-it"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "google/gemma-2b-it",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

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

SGLang

How to use google/gemma-2b-it 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-it" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "google/gemma-2b-it",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

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-it" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "google/gemma-2b-it",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

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

Unsloth Studio new

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

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

Lemonade

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

Pull the model

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

Run and chat with the model

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

List all available models

lemonade list

poor text generation diversity

#43

by matteoperiani - opened May 9, 2024

Discussion

matteoperiani

May 9, 2024

I was testing this model on generation response given a task.
My task is to generate a sentence which contains a desired grammar topic.
For istance, if i will ask to the model to generate a sentence with contains the present simple I would expect something like "The cat is on the table."

What I've found is that, even though the model little struggle on understand the request sometimes, it generate very similar examples even with multiple request.
This had surprised me, becasue I also test with sampling or contrastive search decoding, high temperature and so on.

There's some one that can give some consideration about it. Why a model like this has a lack of creativity?

Thank you, in advance, for your time.

canyon289

May 10, 2024

Hey!
Thank you for the feedback for Gemma. Ive tried this prompt on ollama and I'm getting different generations, what I'm wondering is if the framework is setting a random seed. Are you seeing this with other frameworks as well?

matteoperiani

May 10, 2024

•

edited May 10, 2024

Hello, thank you for your answer.
Yes, when I did my test I made sure the seed was not set.
anyway, your output are not sign of lack og creativity?
All the sentence starts with “The”, additionally the first 2 have “is chaising the” and generally all the sentence ave the same structure .
With a temperature of 1, these outputs should not be more different ?

Edit:
I did som test by generating with the same prompt 10 times and what I get is very confusing..
prompt:
"Write a sentence with a present simple inside."
Responses:
The sun is shining brightly today, casting long shadows on the warm grass.
The sun is shining brightly today, casting warm rays onto the flowers in my garden.
The sun is shining today, casting warm beams on the parkland below.
The sun is shining today, making it a perfect day for outdoor activities.
The sun is shining brightly today, making it perfect for going outside and playing in the park.
The sun is shining brightly outside today.
The sun is shining brightly today, making it an ideal day for exploring the park.
The sun is shining brightly today, making it a perfect day to go for a walk in the park.
The sun is shining brightly today, creating a beautiful setting for the picnic.
The sun is shining brightly today, making it the perfect day for an outdoor picnic in the park.

Here the code..

sineeli

Google org May 10, 2024

•

edited May 10, 2024

Hi @matteoperiani

When tried to prompt 5 sentences at a time then we can see some variations:

But if we prompt continuously they repeat mostly start with same The word

May be because there was no previous context when we are do a single sentence at a time and there are no variations and when prompted again from started it's picking up most probable words and then returning the sentences.

matteoperiani

May 14, 2024

Yep, I think the problem is the same. Anyway, I did different trials with higher temperature and found that for values about 2/3 it start to generate more diverse output even with a single request per time.
I think that SLLMs lack of creativity is an huge drawback, since I found the same issue even with other models.

May be useful, during the fine tuning, add some constraints to limit this? Could be a limitation of data used during train?
I hope that one day, even models with few billions of parameter can develop skills like the bigger ones, it would be very interesting to have a personal and local GPT-4 power .

Good luck for your works!!!
Cheers.

matteoperiani changed discussion status to closed May 14, 2024

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