gemma2-9b-it-qdora-summary

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This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.

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Uses

from peft import PeftModel
from transformers import pipeline, AutoModelForCausalLM, AutoTokenizer

MODEL_ID = "google/gemma-2-9b-it"
PEFT_MODEL_ID = "drlee1/gemma2-9b-it-qdora-summary"

model = AutoModelForCausalLM.from_pretrained(MODEL_ID, device_map = 'auto', torch_dtype = torch.float16)
tokenizer = AutoTokenizer.from_pretrained(MODEL_ID)

model = PeftModel.from_pretrained(model, PEFT_MODEL_ID, device_map = 'auto', torch_dtype = torch.float16)

pipe = pipeline("text-generation", model = model, tokenizer = tokenizer, max_new_tokens = 512)

doc = "..."

messages = [
    {"role": "user", "content": "다음 글을 요약해주세요:\n\n{}".format(doc)}
]

prompt = tokenizer.apply_chat_template(messages, tokenize = False, add_generation_prompt = True)

outputs = pipe(
    prompt,
    do_sample = True,
    temperature = .2,
    top_k = 50,
    top_p = .95,
    add_special_tokens = True 
)

print(outputs[0]['generated_text'][len(prompt):])

Template

# chat template
<bos><start_of_turn>user\n다음 글을 요약해주세요:\n\n{data}<end_of_turn>\n<start_of_turn>model\n{label}

Direct Use

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Out-of-Scope Use

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Training Details

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Training Procedure

• SFT
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Training Hyperparameters

• per_device_train_batch_size: 2
• gradient_accumulation_steps: 4
• optimization: paged_adamw_8bit
• lr: 2e-4
• bf16: True
• max_steps: 500

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Evaluation

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Environmental Impact

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Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).

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