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
Notebooks
Google Colab
Kaggle
Local Apps

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
```

Batch Inference causes degraded performance

#43

by tanliboy - opened Aug 26, 2024

Discussion

tanliboy

Aug 26, 2024

I want to bring up attentions to this issue. The batch inference with Gemma-2-9b-it on lm-evaluation-harness leads to significantly degraded performance.

1st run with auto batch size (batch size = 1 after auto detection)

Tasks	Version	Filter	Metric		Value		Stderr
ifeval	2	none	inst_level_loose_acc	↑	0.7674	±	N/A
		none	inst_level_strict_acc	↑	0.7554	±	N/A
		none	prompt_level_loose_acc	↑	0.6784	±	0.0201
		none	prompt_level_strict_acc	↑	0.6636	±	0.0203

2nd run with batch = 32

Tasks	Version	Filter	Metric		Value		Stderr
ifeval	2	none	inst_level_loose_acc	↑	0.0528	±	N/A
		none	inst_level_strict_acc	↑	0.0528	±	N/A
		none	prompt_level_loose_acc	↑	0.0462	±	0.0090
		none	prompt_level_strict_acc	↑	0.0462	±	0.0090

tanliboy

Aug 26, 2024

It is likely related to the sliding window issue.

lkv

Google org Nov 6, 2024

Hi @tanliboy , I hope this issue is resolved in github for same issue . Could you please let me know if you have any concerns or feel free to close this issue . Thank you.

tanliboy

Nov 6, 2024

Thanks! Yes, it is resolved..

tanliboy changed discussion status to closed Nov 6, 2024

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