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

Transformer Pipeline

#26

by francescoyoubiquo - opened Feb 28, 2024

Discussion

francescoyoubiquo

Feb 28, 2024

Loading Gemma 2b.it model with this code:

model_version = 2
model_id = f"/kaggle/input/gemma/transformers/2b-it/{model_version}"
model_config = f"/kaggle/input/gemma/transformers/2b-it/{model_version}/config.json"

tokenizer_id = f"/kaggle/input/gemma/transformers/2b-it/{model_version}"
tokenizer_config = f"/kaggle/input/gemma/transformers/2b-it/{model_version}/tokenizer_config.json"

model_config = AutoConfig.from_pretrained(model_config)
model = AutoModelForCausalLM.from_pretrained(model_id, config=model_config, device_map='auto')

tokenizer_config = AutoConfig.from_pretrained(tokenizer_config)
tokenizer = AutoTokenizer.from_pretrained(tokenizer_id, config=tokenizer_config, device_map='auto', return_tensors="pt")

Executing the generation as follow:

input_text = "Write a python function to print all elements of a list."
input_ids = tokenizer(input_text, return_tensors="pt")
outputs = model.generate(**input_ids, max_new_tokens=16)
print(tokenizer.decode(outputs[0]))

Some text is generated. But creating a transformers.pipeline as follow, the only text in output is the input text.

query_pipeline = transformers.pipeline(
task="text-generation",
model=model,
tokenizer=tokenizer,
max_new_tokens=512,
device_map="auto",
framework="pt",
)

input_text = "Write a python function to print all elements of a list."
result = pipeline(
input_text ,
max_new_tokens=64,
do_sample=True,
num_return_sequences=1,
)

print(f"Result: {result}")

This is the output:
Result: [{'generated_text': 'Write a python function to print all elements of a list.'}]

This procedure is correct or there are some mistakes?

Instead, when the pipeline is applying the chat-template in this way before executing the pipeline generates some text:

chat = [
{ "role": "user", "content": input_text },
]
prompt = tokenizer.apply_chat_template(chat, tokenize=False, add_generation_prompt=True)

But the text is also generated creatina a pipeline of type "conversational" and passing a chat like this:
chat = [
{ "role": "user", "content": input_text },
]

There's a problem with the TextGenerationPipeline?

godchurch

Mar 3, 2024

Even I am struggling with this

suryabhupa

Google org Mar 4, 2024

Is this using the right chat template and control tokens under the hood?

EliteZhang

Mar 5, 2024

I had the same issue the generated_text is the same as input. I found a way to fix this.

Modify the code:

result = pipeline(
input_text ,
max_new_tokens=64,
do_sample=True,
num_return_sequences=1,
)

to:

result = pipeline(
input_text ,
max_new_tokens=64,
do_sample=True,
num_return_sequences=1,
add_special_tokens=True
)

suryabhupa

Google org Mar 7, 2024

ah good to know! cc @osanseviero in case we should specify this somewhere?

jthk-HF

Apr 6, 2024

To utilize pipeline the chat template must be used. Using pipeline without chat template does not generate any new tokens.

ybelkada

Apr 10, 2024

Interesting, cc @ArthurZ @Rocketknight1 do you think there is something we need to upstream in transformers pipeline?

janck

Apr 17, 2024

But shouldn't the text-generation pipeline produce new tokens as for all the other models?
Also for gemma-7b-it it sometimes generates tokens for me.

BalakrishnaCh

Google org Aug 27, 2025

Hi,

Apologies for the late reply, could you please confirm whether the above mentioned issue is resolved or not. If you required any further assistance please let us know. Thanks for your patience.

Thanks.

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