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

Libraries

How to use lablup/gemma-2-2b-it-xaas-cpt with Transformers:

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

pipe = pipeline("text-generation", model="lablup/gemma-2-2b-it-xaas-cpt")

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("lablup/gemma-2-2b-it-xaas-cpt", dtype="auto")

PEFT
How to use lablup/gemma-2-2b-it-xaas-cpt with PEFT:
```
Task type is invalid.
```
Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use lablup/gemma-2-2b-it-xaas-cpt with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "lablup/gemma-2-2b-it-xaas-cpt"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "lablup/gemma-2-2b-it-xaas-cpt",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/lablup/gemma-2-2b-it-xaas-cpt

SGLang

How to use lablup/gemma-2-2b-it-xaas-cpt 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 "lablup/gemma-2-2b-it-xaas-cpt" \
    --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": "lablup/gemma-2-2b-it-xaas-cpt",
		"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 "lablup/gemma-2-2b-it-xaas-cpt" \
        --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": "lablup/gemma-2-2b-it-xaas-cpt",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

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

XaaS Gemma 2 2B — Stage 1: Continual Pre-Training (CPT)

Stage 1 of 4 in the XaaS fine-tuning pipeline for Korean international trade.

This model adapts google/gemma-2-2b-it to the Korean trade domain through continual pre-training on a curated corpus of Korean customs, HS code classification, Incoterms, and international trade regulatory text. It serves as the foundation for all downstream XaaS task-specific fine-tunes.

Pipeline Position

google/gemma-2-2b-it
    ↓  [this model]
lablup/gemma-2-2b-it-xaas-cpt  ← you are here
    ↓
lablup/gemma-2-2b-it-xaas-qa   (trade domain QA)
    ↓
lablup/gemma-2-2b-it-xaas-kie  (KIE from B2B emails)
lablup/gemma-2-2b-it-xaas-sum-tag  (email summarization + tagging)

Training Details

Parameter	Value
Base model	`google/gemma-2-2b-it`
Method	Continual pre-training with LoRA
LoRA rank (r)	16
LoRA alpha	32
LoRA dropout	0.05
Target modules	q_proj, k_proj, v_proj, o_proj, gate_proj, up_proj, down_proj
Epochs	1
Learning rate	4e-4
Max sequence length	2,500 tokens
Optimizer	AdamW
Precision	float32
Framework	HuggingFace Transformers + PEFT + Accelerate

Training Data

Internal Korean trade-domain text corpus (XaaS/train_dataset/cpt_dataset/concatenated_dataset) covering:

Korean Customs Act (관세법) and trade regulations
HS code classification explanatory notes (관세율표 해설서)
Incoterms and international trade terminology
Trade finance and letter-of-credit documentation

How to Use

from transformers import AutoTokenizer, AutoModelForCausalLM
import torch

model_id = "lablup/gemma-2-2b-it-xaas-cpt"

tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype=torch.bfloat16,
    device_map="auto",
)

# Gemma 2 chat format
messages = [{"role": "user", "content": "신용장(L/C)의 개설 절차를 설명해주세요."}]
prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
inputs = tokenizer(prompt, return_tensors="pt").to(model.device)

with torch.no_grad():
    outputs = model.generate(**inputs, max_new_tokens=512, do_sample=False)

print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[1]:], skip_special_tokens=True))

Downstream Models

Model	Task
lablup/gemma-2-2b-it-xaas-qa	Korean trade QA (21,399 QA pairs)
lablup/gemma-2-2b-it-xaas-kie	B2B email key-information extraction
lablup/gemma-2-2b-it-xaas-sum-tag	Email summarization + tagging

Limitations

Fine-tuned for Korean trade domain; general-purpose performance may be degraded compared to base Gemma 2
Knowledge cutoff is inherited from google/gemma-2-2b-it; recent regulatory changes are not covered
CPT corpus is domain-specific and does not cover all Korean language use cases