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

Libraries

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

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

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

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("google/gemma-2-2b-jpn-it")
model = AutoModelForCausalLM.from_pretrained("google/gemma-2-2b-jpn-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-2-2b-jpn-it with vLLM:

Install from pip and serve model

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

Use Docker

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

SGLang

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

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

Unify Tokenizers for Gemma-2-2B-it and Gemma-2-2B-jpn-it

#11

by grapevine-AI - opened Dec 4, 2024

base: refs/heads/main

←

from: refs/pr/11

Discussion Files changed

+2009

-2009

grapevine-AI

Dec 4, 2024

•

edited Dec 4, 2024

Currently, llama.cpp server requires the main model and the draft model to have completely identical tokenizers for speculative decoding to work. However, there is a discrepancy between the tokenizers of gemma-2-2b-it and gemma-2-2b-jpn-it. Specifically, token ID 255999 is <start_of_image> in gemma-2-2b-jpn-it but <unused99> in gemma-2-2b-it. This difference causes an error in the llama.cpp server, preventing the use of gemma-2-2b-jpn-it as a draft model.

Through experiments, I have found that gemma-2-2b-jpn-it is well-suited as a draft model for Japanese text generation, providing a speedup of approximately 1.35x on my CPU-only desktop. However, the tokenizer mismatch hinders its practical application.

Considering that gemma-2-2b-jpn-it is designed for text generation and does not handle images, the <start_of_image> token seems unnecessary. Therefore, I propose unifying the tokenizers by replacing <start_of_image> (ID 255999) in gemma-2-2b-jpn-it's tokenizer with <unused99>, aligning it with gemma-2-2b-it.

I have tested the modified tokenizer and confirmed its operability with the llama.cpp server. I am attaching the modified tokenizer file for your review.

Thank you for your consideration.

Upload tokenizer_config.json0dcc931f

grapevine-AI changed pull request title from Upload tokenizer_config.json to Unify Tokenizers for Gemma-2-2B-it and Gemma-2-2B-jpn-it Dec 4, 2024

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