Instructions to use shibajustfor/a690348b-9d15-46c5-92f0-37a6633c8cd5 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use shibajustfor/a690348b-9d15-46c5-92f0-37a6633c8cd5 with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("NousResearch/Yarn-Mistral-7b-128k") model = PeftModel.from_pretrained(base_model, "shibajustfor/a690348b-9d15-46c5-92f0-37a6633c8cd5") - Notebooks
- Google Colab
- Kaggle
Training in progress, step 39, 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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"loss": 1.6598,
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{
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"eval_loss": 0.551670253276825,
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"eval_runtime": 101.8402,
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"eval_samples_per_second": 30.106,
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"eval_steps_per_second": 15.053,
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"step": 39
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"logging_steps": 10,
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