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

Libraries

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

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

pipe = pipeline("text-generation", model="google/gemma-7b")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("google/gemma-7b")
model = AutoModelForCausalLM.from_pretrained("google/gemma-7b")

llama-cpp-python

How to use google/gemma-7b with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="google/gemma-7b",
	filename="gemma-7b.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-7b 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-7b
# Run inference directly in the terminal:
llama-cli -hf google/gemma-7b

Install from WinGet (Windows)

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

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
# Run inference directly in the terminal:
./llama-cli -hf google/gemma-7b

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
# Run inference directly in the terminal:
./build/bin/llama-cli -hf google/gemma-7b

Use Docker

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

LM Studio
Jan

vLLM

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

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "google/gemma-7b"
# 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-7b",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

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

SGLang

How to use google/gemma-7b 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" \
    --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-7b",
		"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-7b" \
        --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-7b",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

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

Unsloth Studio new

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

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

Lemonade

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

Pull the model

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

Run and chat with the model

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

List all available models

lemonade list

USE GEMMA TO TRANSLATION

#37

by tomsum - opened Feb 23, 2024

Discussion

tomsum

Feb 23, 2024

May I ask if you have any valuable experience in commenting on gemma’s translations?

ybelkada

Feb 23, 2024

Hi @tomsum
Thanks for the issue, personally I did not tried gemma for translation, but my gut feeling is that with a bit of prompt engineering you'll be able to achieve interesting results. You can also try out the instruction fine-tuned model. I will let the model authors add some additional details if they have experienced anything interesting with translation using this model

suryabhupa

Google org Feb 24, 2024

Hey there -- are there any prompts or workflows that you've tried, or would like to see work better? Please let us know!

kishorekashyap

Mar 4, 2024

Hi,

I've tried some translation with Gemma-7b. I've used very straightforward prompt--
"translate english to assamese.
english sentence: farming is the primary occupation of man.
assamese sentence: কৃষি মানুহৰ প্ৰধান জীৱিকা।
english sentence: the human society cannot think of food without farming.
assamese sentence: কৃষি অবিহনে মানৱ সমাজে খাদ্যৰ কথা ভাবিব নোৱাৰে।
english sentence:
indigenous fruits of assam such as leteku, ponyal, kuji thekera, rupahi thekera, thereju, mirika tenga, bamboo etc., which have high edible and medicinal value.
assamese sentence:
অসমৰ থলুৱা ফল যেনে লেতেকু, পনিয়ল, কুঁজি থেকেৰা, ৰূপহী থেকেৰা, ঠেৰেজু, মিৰিকা টেঙা ইত্যাদিৰ খাদ্য আৰু ঔষধি গুণ অতি বেছি ।
english sentence: agricultural production is low in assam each time due to the severe damage caused by flood.
assamese sentence: "

Result=অসমত কৃষি উৎপাদন ক্ৰমাগতভাৱে কম হোৱাৰ কাৰণ ব্ৰহ্মপুত্ৰ আৰু আন নদীৰ বহুতো কালি ক্ষতি কৰা ।
The translation direction was English to Assamese (a low-resource Indo-Aryan Indic language). The result was not so good. But the results in reverse direction was a little bit encouraging

suryabhupa

Google org Mar 4, 2024

ah thanks for the feedback; we're thinking about ways to improve our multilingual performance. are there multilingual datasets or results that you find compelling that we should consider including for future work?

kishorekashyap

Mar 4, 2024

ah thanks for the feedback; we're thinking about ways to improve our multilingual performance. are there multilingual datasets or results that you find compelling that we should consider including for future work?

Hi Surya,
First of all, thank you all for the wonderful work. I'am currently doing my doctoral studies in MNMT for low resource indic languages. We are exploring ways to leverage LLMs for our translation task. For multilingual dataset, as I'm aware of in the context of NMT and Indic languages, datasets from ai4bharat is a great one. NLLB datasets are another great resource.
As per my experiments, I found gemma to be best for translation for Assamese to English direction till now. I've tried LLAMA 2 7B, BLOOM, ChatGPT 3.5 too.

suryabhupa

Google org Mar 7, 2024

that's great to hear, especially if you find multilingual competitive with chatgpt 3.5! thanks for your hard work here, and for the pointers :)

Upload images, audio, and videos by dragging in the text input, pasting, or clicking here.

Tap or paste here to upload images

· Sign up or log in to comment