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?"
		}
	]
)

Inference
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

Inference with RTX 3090 got OOM

#89

by kathylee - opened Apr 12, 2024

Discussion

kathylee

Apr 12, 2024

•

edited Apr 22, 2024

Hi dear community, I try to run inference with Gemma-7b-it(downloaded to local) via transformers.pipeline with a local RTX 3090. To test, I run a for loop of inference, the first few run(18 runs) smoothly and quite fast response, but then it got OOM:

"ERROR CUDA out of memory. Tried to allocate 128.00 MiB. GPU 0 has a total capacity of 23.68 GiB of which 172.06 MiB is free. Including non-PyTorch memory, this process has 23.09 GiB memory in use. Of the allocated memory 22.61 GiB is allocated by PyTorch, and 178.51 MiB is reserved by PyTorch but unallocated. If reserved but unallocated memory is large try setting PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True to avoid fragmentation. See documentation for Memory Management (https://pytorch.org/docs/stable/notes/cuda.html#environment-variables)"

My code is as following(referring to https://github.com/huggingface/blog/blob/main/gemma.md):
Before the for loop:

     model= pipeline("text-generation",model="./models/gemma-7b-it",model_kwargs={"torch_dtype": torch.bfloat16},device="cuda",)

In the for loop:

    messages = [{"role": "user", "content": text},]
    prompt = model.tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
    start_time = monotonic()
    outputs = model(prompt, max_new_tokens=max_tokens, do_sample=True, temperature=0.7, top_k=50, top_p=0.95)

I tried to set PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True as the error message suggests but it doesn't work. And this error is reproducible. Can someone tell me please: Is this model able to run on RTX 3090? Is there anything wrong in my code? Thanks a lot!

JavierSan

Apr 21, 2024

•

edited Apr 22, 2024

I found same error with 30gb ram and one T4. After several inference it arised that same error message. No solution found so far besides adding more GPU to my machine (It runs with 2 T4 but I think this requirement is too much for a moderate size like 4-bits quantized 7b model). I tried inference with bigger models and I didnt have this inconvenience
like mistral 7 x 8b @suryabhupa could you shed some light?

suryabhupa

Google org Apr 24, 2024

Hello! I'm a lot less familiar with running the models on PyTorch + GPUs unfortunately (internally, we use Jax + TPUs exclusively). I am indeed surprised that so much memory is being allocated on just forward passes; are the params being casted correctly and no other memory for backward passes are being allocated (e.g. all the activation buffers are deleted as they're not needed)?

kathylee

May 17, 2024

Hi @suryabhupa , thank you for your reply! What do you mean by "are the params being casted correctly and no other memory for backward passes are being allocated (e.g. all the activation buffers are deleted as they're not needed)?" exactly? I think the above code is just simply running forward pass for inference without backward pass, not doing any fine tune. I'm not sure if there is additional cleaning buffer is needed, please point out if it is, thanks a lot!

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