Instructions to use dimasik87/1bc7b11d-8ab3-44f1-bd3f-c2b84e0d0cb0 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use dimasik87/1bc7b11d-8ab3-44f1-bd3f-c2b84e0d0cb0 with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("Maykeye/TinyLLama-v0") model = PeftModel.from_pretrained(base_model, "dimasik87/1bc7b11d-8ab3-44f1-bd3f-c2b84e0d0cb0") - Notebooks
- Google Colab
- Kaggle
Training in progress, step 25, 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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{
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"best_metric": null,
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"is_world_process_zero": true,
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"eval_samples_per_second": 80.166,
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"eval_steps_per_second": 40.083,
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"step": 24
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"logging_steps": 1,
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"should_evaluate": false,
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"should_log": false,
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"should_training_stop":
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"total_flos":
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"train_batch_size": 2,
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"trial_name": null,
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"epoch": 0.00809192425958893,
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"global_step": 25,
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"is_local_process_zero": true,
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"is_world_process_zero": true,
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"eval_samples_per_second": 80.166,
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"step": 24
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{
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"epoch": 0.00809192425958893,
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"grad_norm": 4.323055267333984,
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"learning_rate": 0.0,
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"loss": 9.4918,
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"step": 25
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"logging_steps": 1,
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"should_evaluate": false,
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"should_log": false,
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"should_save": true,
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"should_training_stop": true
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"attributes": {}
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"total_flos": 20362140057600.0,
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"train_batch_size": 2,
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