Instructions to use tuantmdev/98c4404c-a951-4c8a-bd5b-7b42b6e12bf3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use tuantmdev/98c4404c-a951-4c8a-bd5b-7b42b6e12bf3 with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("fxmarty/tiny-dummy-qwen2") model = PeftModel.from_pretrained(base_model, "tuantmdev/98c4404c-a951-4c8a-bd5b-7b42b6e12bf3") - Notebooks
- Google Colab
- Kaggle
Training in progress, step 160, checkpoint
Browse files
last-checkpoint/adapter_model.safetensors
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last-checkpoint/optimizer.pt
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last-checkpoint/rng_state.pth
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last-checkpoint/scheduler.pt
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last-checkpoint/trainer_state.json
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"eval_samples_per_second": 149.172,
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"logging_steps": 40,
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"eval_samples_per_second": 149.172,
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"learning_rate": 5.000000000000003e-06,
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"loss": 11.9303,
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"step": 160
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{
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"epoch": 0.31496062992125984,
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"eval_loss": 11.930480003356934,
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"eval_runtime": 5.7325,
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"eval_samples_per_second": 149.325,
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"eval_steps_per_second": 74.662,
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"step": 160
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"logging_steps": 40,
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