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

Very different results with float16. [Actually, gemma-7b-it does not work with float16]

#33

by EarthWorm001 - opened Feb 22, 2024

Discussion

EarthWorm001

Feb 22, 2024

•

edited Feb 22, 2024

I'm testing gemma on the passkey retrieval task. However, I found that, if load the model with 'float16', the model cannot generate meaningful results.

My input is:

input_text = "There is an important info hidden inside a lot of irrelevant text. Find it and memorize it. I will quiz you about the important information there.\nThe grass is green. The sky is blue. The sun is yellow. Here we go. There and back again.\nThe grass is green. The sky is blue. The sun is yellow. Here we go. There and back again.\nThe grass is green. The sky is blue. The sun is yellow. Here we go. There and back again.\nThe grass is green. The sky is blue. The sun is yellow. Here we go. There and back again.\nThe grass is green. The sky is blue. The sun is yellow. Here we go. There and back again.\nThe grass is green. The sky is blue. The sun is yellow. Here we go. There and back again.\nThe grass is green. The sky is blue. The sun is yellow. Here we go. There and back again.\nThe grass is green. The sky is blue. The sun is yellow. Here we go. There and back again.\nThe grass is green. The sky is blue. The sun is yellow. Here we go. There and back again.\nThe grass is green. The sky is blue. The sun is yellow. Here we go. There and back again.\nThe grass is green. The sky is blue. The sun is yellow. Here we go. There and back again.\nThe grass is green. The sky is blue. The sun is yellow. Here we go. There and back again.\nThe grass is green. The sky is blue. The sun is yellow. Here we go. There and back again.\nThe grass is green. The sky is blue. The sun is yellow. Here we go. There and back again.\nThe grass is green. The sky is blue. The sun is yellow. Here we go. There and back again.\nThe pass key is 91286. Remember it. 91286 is the pass key.\nThe grass is green. The sky is blue. The sun is yellow. Here we go. There and back again.\nThe grass is green. The sky is blue. The sun is yellow. Here we go. There and back again.\nThe grass is green. The sky is blue. The sun is yellow. Here we go. There and back again.\nThe grass is green. The sky is blue. The sun is yellow. Here we go. There and back again.\nThe grass is green. The sky is blue. The sun is yellow. Here we go. There and back again.\nThe grass is green. The sky is blue. The sun is yellow. Here we go. There and back again.\nThe grass is green. The sky is blue. The sun is yellow. Here we go. There and back again.\nThe grass is green. The sky is blue. The sun is yellow. Here we go. There and back again.\nThe grass is green. The sky is blue. The sun is yellow. Here we go. There and back again.\nThe grass is green. The sky is blue. The sun is yellow. Here we go. There and back again.\nThe grass is green. The sky is blue. The sun is yellow. Here we go. There and back again.\nThe grass is green. The sky is blue. The sun is yellow. Here we go. There and back again.\nThe grass is green. The sky is blue. The sun is yellow. Here we go. There and back again.\nThe grass is green. The sky is blue. The sun is yellow. Here we go. There and back again.\nThe grass is green. The sky is blue. The sun is yellow. Here we go. There and back again.\nThe grass is green. The sky is blue. The sun is yellow. Here we go. There and back again.\nThe grass is green. The sky is blue. The sun is yellow. Here we go. There and back again.\nThe grass is green. The sky is blue. The sun is yellow. Here we go. There and back again.\nThe grass is green. The sky is blue. The sun is yellow. Here we go. There and back again.\nThe grass is green. The sky is blue. The sun is yellow. Here we go. There and back again.\nThe grass is green. The sky is blue. The sun is yellow. Here we go. There and back again.\nThe grass is green. The sky is blue. The sun is yellow. Here we go. There and back again.\nThe grass is green. The sky is blue. The sun is yellow. Here we go. There and back again. What is the pass key? The pass key is "

With float32:

model = AutoModelForCausalLM.from_pretrained("google/gemma-7b-it", device_map="auto")

The output is:
"[...context..] The grass is green. The sky is blue. The sun is yellow. Here we go. There and back again. What is the pass key? The p
ass key is 91286."

While, if I use float16:

model = AutoModelForCausalLM.from_pretrained("google/gemma-7b-it", device_map="auto", torch_dtype=torch.float16)

The output is:
"[...context...] The grass is green. The sky is blue. The sun is yellow. Here we go. There and back again. What is the pass key? The p
ass key is <pad><pad><pad><pad><pad><pad> <pad><pad><pad><pad><pad><pad> <pad><pad><pad><pad><pad><pad> <pad><pad>"

My generation codes:

tokenizer = AutoTokenizer.from_pretrained("google/gemma-7b-it")
input_ids = tokenizer(input_text, return_tensors="pt").to("cuda")
outputs = model.generate(**input_ids, max_new_tokens=20)
print(tokenizer.decode(outputs[0]))

EarthWorm001

Feb 22, 2024

BTW: gemma-7b works well with float16.

tdolega

Feb 27, 2024

This comment has been hidden

suryabhupa

Google org Mar 4, 2024

Hey, Surya from the Gemma team here -- does this work when using the correct chat template?

shuyuej

Mar 25, 2024

Nope. It is not working.

bigmonster

Mar 30, 2024

•

edited Mar 30, 2024

RMSnorm is overflow

UserDAN

Apr 22, 2024

I'm having issue that is not completely similar to this is to you but it can be related to which I discussed here: https://huggingface.co/google/gemma-7b/discussions/91 I will appreciate it if you can give me your thoughts

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