Instructions to use infogep/d6a22042-e535-4141-afea-4ebdbd7f7f06 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use infogep/d6a22042-e535-4141-afea-4ebdbd7f7f06 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, "infogep/d6a22042-e535-4141-afea-4ebdbd7f7f06") - Notebooks
- Google Colab
- Kaggle
Training in progress, step 100, 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_model_checkpoint": "miner_id_24/checkpoint-50",
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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": 19.516,
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"eval_steps_per_second": 4.899,
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"step": 50
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"logging_steps": 10,
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"early_stopping_threshold": 0.0
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"TrainerControl": {
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"is_world_process_zero": true,
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"eval_samples_per_second": 19.516,
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"eval_steps_per_second": 4.899,
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