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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
1from peft import PeftModel
2from transformers import pipeline, AutoModelForCausalLM, AutoTokenizer
3
4MODEL_ID = "google/gemma-2-9b-it"
5PEFT_MODEL_ID = "drlee1/gemma2-9b-it-qdora-summary"
6
7model = AutoModelForCausalLM.from_pretrained(MODEL_ID, device_map = 'auto', torch_dtype = torch.float16)
8tokenizer = AutoTokenizer.from_pretrained(MODEL_ID)
9
10model = PeftModel.from_pretrained(model, PEFT_MODEL_ID, device_map = 'auto', torch_dtype = torch.float16)
11
12pipe = pipeline("text-generation", model = model, tokenizer = tokenizer, max_new_tokens = 512)
13
14doc = "..."
15
16messages = [
17 {"role": "user", "content": "다음 글을 요약해주세요:\n\n{}".format(doc)}
18]
19
20prompt = tokenizer.apply_chat_template(messages, tokenize = False, add_generation_prompt = True)
21
22outputs = pipe(
23 prompt,
24 do_sample = True,
25 temperature = .2,
26 top_k = 50,
27 top_p = .95,
28 add_special_tokens = True
29)
30
31print(outputs[0]['generated_text'][len(prompt):])
Template
1# chat template
2<bos><start_of_turn>user\n다음 글을 요약해주세요:\n\n{data}<end_of_turn>\n<start_of_turn>model\n{label}
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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
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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
Testing Data, Factors & Metrics
Testing Data
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Factors
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Metrics
| Step | Training Loss |
|---|
| 100 | 1.528100 |
| 200 | 1.409400 |
| 300 | 1.372800 |
| 400 | 1.325900 |
| 500 | 1.341600 |
Results
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Summary
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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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