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aiqwe/
gemma-2b-it-example-v1

Name: gemma-2b-it-example-v1
Availability: InStock

generated_from_trainer

google/gemma-1.1-2b-it

adapter

gemma

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Model Description

git hub : https://github.com/aiqwe/instruction-tuning-with-rag-example
Instruction Tuning의 학습을 위해 예시로 학습한 모델입니다.
gemma-2b-it 모델을 기반으로 약 1만개의 부동산 관련 Instruction 데이터셋을 학습하였습니다.
학습 코드는 위 git hub를 참조해주세요.

Usage

Inference on GPU example

python

from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("google/gemma-2b-it")
model = AutoModelForCausalLM.from_pretrained(
    "aiqwe/gemma-2b-it-example-v1",
    device_map="cuda",
    torch_dtype=torch.bfloat16,
    attn_implementation="flash_attention_2"
)

input_text = "아파트 재건축에 대해 알려줘."
input_ids = tokenizer(input_text, return_tensors="pt").to("cuda")

outputs = model.generate(**input_ids, max_new_tokens=512)
print(tokenizer.decode(outputs[0]))

Inference on CPU example

python

from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("google/gemma-2b-it")
model = AutoModelForCausalLM.from_pretrained(
    "aiqwe/gemma-2b-it-example-v1",
    device_map="cpu",
    torch_dtype=torch.bfloat16
)

input_text = "아파트 재건축에 대해 알려줘."
input_ids = tokenizer(input_text, return_tensors="pt").to("cuda")

outputs = model.generate(**input_ids, max_new_tokens=512)
print(tokenizer.decode(outputs[0]))

Inference on GPU with embedded function example

내장된 함수로 네이버 검색 API를 통해 RAG를 지원받습니다.

python

from transformers import AutoTokenizer, AutoModelForCausalLM 
from utils import generate

tokenizer = AutoTokenizer.from_pretrained("google/gemma-2b-it")
model = AutoModelForCausalLM.from_pretrained(
    "aiqwe/gemma-2b-it-example-v1",
    device_map="cuda",
    torch_dtype=torch.bfloat16,
    attn_implementation="flash_attention_2"
)

rag_config = {
    "api_client_id": userdata.get('NAVER_API_ID'),
    "api_client_secret": userdata.get('NAVER_API_SECRET')
}
completion = generate(
    model=model,
    tokenizer=tokenizer,
    query=query,
    max_new_tokens=512,
    rag=True,
    rag_config=rag_config
)
print(completion)

Chat Template

Gemma 모델의 Chat Template을 사용합니다.
gemma-2b-it Chat Template

python

input_text = "아파트 재건축에 대해 알려줘."

input_text = tokenizer.apply_chat_template(
        conversation=[
            {"role": "user", "content": input_text}
        ],
        add_generate_prompt=True,
        return_tensors="pt"
    ).to(model.device)

outputs = model.generate(input_text, max_new_tokens=512, repetition_penalty = 1.5)
print(tokenizer.decode(outputs[0], skip_special_tokens=False))

Training information

학습은 구글 코랩 L4 Single GPU를 활용하였습니다.

구분	내용
환경	Google Colab
GPU	L4(22.5GB)
사용 VRAM	약 13.8GB
dtype	bfloat16
Attention	flash attention2
Tuning	Lora(r=4, alpha=32)
Learning Rate	1e-4
LRScheduler	Cosine
Optimizer	adamw_torch_fused
batch_size	4
gradient_accumulation_steps	2