Instructions to use augustocsc/Se124M100KInfPrompt with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use augustocsc/Se124M100KInfPrompt with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("gpt2") model = PeftModel.from_pretrained(base_model, "augustocsc/Se124M100KInfPrompt") - Notebooks
- Google Colab
- Kaggle
End of fine-tuning training
Browse files- all_results.json +5 -5
- eval_results.json +6 -6
all_results.json
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"epoch": 3.0,
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"eval_loss": 0.
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"eval_runtime":
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"perplexity":
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"total_flos": 1.5045077015543808e+16,
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"train_loss": 0.5129519925375031,
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"train_runtime": 8559.3775,
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{
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"epoch": 3.0,
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"eval_loss": 0.36619314551353455,
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"eval_runtime": 51.2719,
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"eval_samples_per_second": 326.865,
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"eval_steps_per_second": 20.44,
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"perplexity": 1.442233776738291,
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"total_flos": 1.5045077015543808e+16,
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"train_loss": 0.5129519925375031,
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"train_runtime": 8559.3775,
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eval_results.json
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{
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"epoch":
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"eval_loss": 0.
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"eval_runtime":
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"eval_samples_per_second":
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"perplexity":
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}
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{
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"epoch": 3.0,
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"eval_loss": 0.36619314551353455,
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"eval_runtime": 51.2719,
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"eval_samples_per_second": 326.865,
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"eval_steps_per_second": 20.44,
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"perplexity": 1.442233776738291
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}
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