Instructions to use mamung/a68fc4d2-004a-4b95-8abd-402434667dd3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use mamung/a68fc4d2-004a-4b95-8abd-402434667dd3 with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("unsloth/llama-2-7b-chat") model = PeftModel.from_pretrained(base_model, "mamung/a68fc4d2-004a-4b95-8abd-402434667dd3") - Notebooks
- Google Colab
- Kaggle
Training in progress, step 99, checkpoint
Browse files
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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"best_metric": null,
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"epoch": 0.
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"eval_steps": 9,
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"global_step":
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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": 10.39,
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"eval_steps_per_second": 1.299,
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"step": 90
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}
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"logging_steps": 5,
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"attributes": {}
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"train_batch_size": 8,
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{
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"best_model_checkpoint": null,
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"epoch": 0.03173076923076923,
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"eval_steps": 9,
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"global_step": 99,
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"is_hyper_param_search": false,
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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": 10.39,
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"eval_steps_per_second": 1.299,
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"step": 90
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{
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"learning_rate": 1.4411039697577175e-06,
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"step": 95
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{
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"eval_runtime": 1067.8009,
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"eval_samples_per_second": 10.389,
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"eval_steps_per_second": 1.299,
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"step": 99
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}
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],
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"logging_steps": 5,
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"attributes": {}
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}
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"total_flos": 2.634381633037271e+17,
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"train_batch_size": 8,
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"trial_name": null,
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"trial_params": null
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