Instructions to use google/gemma-7b-it with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use google/gemma-7b-it with Transformers:

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

pipe = pipeline("text-generation", model="google/gemma-7b-it")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("google/gemma-7b-it")
model = AutoModelForCausalLM.from_pretrained("google/gemma-7b-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-7b-it with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="google/gemma-7b-it",
	filename="gemma-7b-it.gguf",
)

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

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use google/gemma-7b-it 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 google/gemma-7b-it
# Run inference directly in the terminal:
llama cli -hf google/gemma-7b-it

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf google/gemma-7b-it
# Run inference directly in the terminal:
llama cli -hf google/gemma-7b-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-7b-it
# Run inference directly in the terminal:
./llama-cli -hf google/gemma-7b-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-7b-it
# Run inference directly in the terminal:
./build/bin/llama-cli -hf google/gemma-7b-it

Use Docker

docker model run hf.co/google/gemma-7b-it

LM Studio
Jan

vLLM

How to use google/gemma-7b-it with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "google/gemma-7b-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-7b-it",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/google/gemma-7b-it

SGLang

How to use google/gemma-7b-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-7b-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-7b-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-7b-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-7b-it",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Ollama
How to use google/gemma-7b-it with Ollama:
```
ollama run hf.co/google/gemma-7b-it
```

Unsloth Studio

How to use google/gemma-7b-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-7b-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-7b-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-7b-it to start chatting

Atomic Chat new
Docker Model Runner
How to use google/gemma-7b-it with Docker Model Runner:
```
docker model run hf.co/google/gemma-7b-it
```

Lemonade

How to use google/gemma-7b-it with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull google/gemma-7b-it

Run and chat with the model

lemonade run user.gemma-7b-it-{{QUANT_TAG}}

List all available models

lemonade list

Alignment Issues

#37

by deleted - opened Feb 22, 2024

Discussion

deleted

Feb 22, 2024

Gemma chat versions score far lower on the leaderboard than their foundational models, which isn't surprising since they perform horribly in my testing. Far worse than Mistral 7b.

The primary reason is clear. The alignment is GROSSLY overdone. Not everyone using an LLM is a 3 year old child. Adults use LLMs to, and ask basic questions about a wide variety of things that aren't the least bit illegal or amoral. By training your LLM with 1000s of 'As an AI model I can't answer that' responses you not only cripple the LLM in millions of legitimate use cases, the excessive alignment misfires and bleeds into completely unrelated areas, making the LLM perform much worse in every context.

Even your foundational models are stripped of basic knowledge that isn't remotely illegal or amoral, and which most adults already know, plus can be found in any basic encyclopedia. What's the point of that? Why would anyone turn to your AI models for reference when countless perfectly legal and moral bits of information is stripped from it? Info that can be found in Wikipedia or the first Google result. Even Gemini Pro/Ultra have astonishing blind spots, making them very unreliable sources of help and information despite their otherwise impressive abilities that are sometimes superior to GPT4's.

Again, not everyone who uses AI is a 3 year old child. Please re-consider your obsession with extreme censorship/alignment/moralizing/... Frankly, it's embarrassing.

unixguru2k

Feb 22, 2024

ReXommendation

Feb 26, 2024

•

edited Feb 26, 2024

Gemma chat versions score far lower on the leaderboard than their foundational models, which isn't surprising since they perform horribly in my testing. Far worse than Mistral 7b.

The primary reason is clear. The alignment is GROSSLY overdone. Not everyone using an LLM is a 3 year old child. Adults use LLMs to, and ask basic questions about a wide variety of things that aren't the least bit illegal or amoral. By training your LLM with 1000s of 'As an AI model I can't answer that' responses you not only cripple the LLM in millions of legitimate use cases, the excessive alignment misfires and bleeds into completely unrelated areas, making the LLM perform much worse in every context.

Even your foundational models are stripped of basic knowledge that isn't remotely illegal or amoral, and which most adults already know, plus can be found in any basic encyclopedia. What's the point of that? Why would anyone turn to your AI models for reference when countless perfectly legal and moral bits of information is stripped from it? Info that can be found in Wikipedia or the first Google result. Even Gemini Pro/Ultra have astonishing blind spots, making them very unreliable sources of help and information despite their otherwise impressive abilities that are sometimes superior to GPT4's.

Again, not everyone who uses AI is a 3 year old child. Please re-consider your obsession with extreme censorship/alignment/moralizing/... Frankly, it's embarrassing.

Well you're expecting a model from Google to not be head deep in DEI alignment non-sense. Even Google search is biased lol.

lkv

Google org Aug 28, 2025

Hi ,
Thank you for sharing your perspective — your feedback is clear, and we appreciate the time you took to write such a detailed critique.

If you're looking for more flexibility or fewer guardrails, we recommend using the foundation models like gemma-3-7b or gemma-3-12b. These are released without alignment, so you're free to fine-tune or prompt them however you like. That’s the best way to avoid the “as an AI…” type responses you're seeing in the chat variants.

Thank you.

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