Instructions to use google/gemma-2-9b-it with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use google/gemma-2-9b-it with Transformers:

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

pipe = pipeline("text-generation", model="google/gemma-2-9b-it")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("google/gemma-2-9b-it")
model = AutoModelForCausalLM.from_pretrained("google/gemma-2-9b-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]:]))

Inference
Local Apps Settings

vLLM

How to use google/gemma-2-9b-it with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "google/gemma-2-9b-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-2-9b-it",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/google/gemma-2-9b-it

SGLang

How to use google/gemma-2-9b-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-2-9b-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-2-9b-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-2-9b-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-2-9b-it",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use google/gemma-2-9b-it with Docker Model Runner:
```
docker model run hf.co/google/gemma-2-9b-it
```

Possible issue with context window

#23

by ieman - opened Jul 4, 2024

Discussion

ieman

Jul 4, 2024

I was making inferences with Gemma 2 9b instruct that I received the following error:

RuntimeError: CUDA error: device-side assert triggered
CUDA kernel errors might be asynchronously reported at some other API call, so the stacktrace below might be incorrect.
For debugging consider passing CUDA_LAUNCH_BLOCKING=1.
Compile with TORCH_USE_CUDA_DSA to enable device-side assertions.

I managed to solve it by setting max_length to 4096. I assume the issue should be with the layers that the context window is 4096 with sliding window.
transformers version: 4.42.3
PyTorch version: 2.3.1

datamancer88

Jul 15, 2024

Hey @ieman , can you provide some of the inferences which caused this to occur? Of course please don't include any PII.

ieman

Jul 27, 2024

•

edited Jul 27, 2024

Hi @datamancer88 I am working with CVs, they are all private so I can not send you the prompts that made the error, But I will send you part of the code related to this issue.

prompt = tokenizer.apply_chat_template(chat, tokenize=False, add_generation_prompt=True)
prompt = prompt.replace("MASK", tokenizer.pad_token)

inputs = tokenizer.encode(prompt, add_special_tokens=False, return_tensors="pt")

output_ids = model.generate(inputs.to(model.device),
max_length=config["max_length"],
temperature=config["temperature"],
repetition_penalty=config["repetition_penalty"])
output_ids = output_ids[0][len(inputs[0]) :]
output = tokenizer.decode(output_ids, skip_special_tokens=True)

value of the following variables are:
max_length = 8000
temperature = 0
repetition_penalty = 1

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