Instructions to use cwaud/d8a5519b-748a-4954-a590-e63634d3bbb9 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use cwaud/d8a5519b-748a-4954-a590-e63634d3bbb9 with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("NousResearch/Hermes-2-Pro-Llama-3-8B") model = PeftModel.from_pretrained(base_model, "cwaud/d8a5519b-748a-4954-a590-e63634d3bbb9") - Notebooks
- Google Colab
- Kaggle
Training in progress, step 10, 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": 9.177,
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"eval_steps_per_second": 4.598,
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"step": 9
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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":
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"is_local_process_zero": true,
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"eval_samples_per_second": 9.177,
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"eval_steps_per_second": 4.598,
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"step": 9
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{
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"epoch": 0.008726003490401396,
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"grad_norm": 4.1800923347473145,
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"learning_rate": 0.0002,
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"loss": 2.5439,
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"step": 10
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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": 1849596460400640.0,
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"train_batch_size": 2,
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