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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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Bias, Risks, and Limitations
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Recommendations
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
How to Get Started with the Model
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Training Details
Training Data
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Training Procedure
- SFT
- Quantization
- DoRA
Preprocessing [optional]
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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
Speeds, Sizes, Times [optional]
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Evaluation
Testing Data, Factors & Metrics
Testing Data
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Factors
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Metrics
- Training Loss
| Step | Training Loss |
|---|---|
| 100 | 1.528100 |
| 200 | 1.409400 |
| 300 | 1.372800 |
| 400 | 1.325900 |
| 500 | 1.341600 |
Results
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Summary
Model Examination [optional]
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Environmental Impact
Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).
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Model Architecture and Objective
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Software
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