Instructions to use havinash-ai/61a5ea63-24c3-44a9-be77-a2f139317391 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use havinash-ai/61a5ea63-24c3-44a9-be77-a2f139317391 with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("Intel/neural-chat-7b-v3-3") model = PeftModel.from_pretrained(base_model, "havinash-ai/61a5ea63-24c3-44a9-be77-a2f139317391") - Notebooks
- Google Colab
- Kaggle
Training in progress, step 26, 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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"epoch": 0.
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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": 27.491,
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"eval_steps_per_second": 13.746,
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"step": 13
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"logging_steps": 10,
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"attributes": {}
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{
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"epoch": 0.009690644800596348,
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"eval_steps": 13,
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"global_step": 26,
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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": 27.491,
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"eval_steps_per_second": 13.746,
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"step": 13
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"eval_runtime": 41.1615,
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"eval_samples_per_second": 27.453,
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"eval_steps_per_second": 13.726,
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"step": 26
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"logging_steps": 10,
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"attributes": {}
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"total_flos": 5389459591790592.0,
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"train_batch_size": 2,
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"trial_name": null,
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