Instructions to use shibajustfor/58d597c9-091f-4c62-b174-30cc8ad298b3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use shibajustfor/58d597c9-091f-4c62-b174-30cc8ad298b3 with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("katuni4ka/tiny-random-olmo-hf") model = PeftModel.from_pretrained(base_model, "shibajustfor/58d597c9-091f-4c62-b174-30cc8ad298b3") - Notebooks
- Google Colab
- Kaggle
Training in progress, step 26, 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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"logging_steps": 10,
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"learning_rate": 0.00015000000000000001,
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"loss": 10.8123,
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{
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"eval_loss": 10.785100936889648,
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"eval_runtime": 4.74,
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"eval_samples_per_second": 403.585,
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"eval_steps_per_second": 201.898,
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"step": 26
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"logging_steps": 10,
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