Instructions to use sravanthib/s with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use sravanthib/s with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen2.5-7B-Instruct") model = PeftModel.from_pretrained(base_model, "sravanthib/s") - Notebooks
- Google Colab
- Kaggle
Training completed
Browse files- all_results.json +4 -4
- train_results.json +4 -4
- trainer_state.json +6 -6
all_results.json
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{
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"epoch": 0.2,
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"total_flos": 1.7426360578342912e+16,
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"train_loss": 4.
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"train_runtime":
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"train_samples_per_second": 1.
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"train_steps_per_second": 0.
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}
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{
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"epoch": 0.2,
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"total_flos": 1.7426360578342912e+16,
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"train_loss": 4.444633483886719,
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"train_runtime": 141.3075,
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"train_samples_per_second": 1.415,
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"train_steps_per_second": 0.071
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}
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train_results.json
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{
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"epoch": 0.2,
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"total_flos": 1.7426360578342912e+16,
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"train_loss": 4.
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"train_runtime":
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"train_samples_per_second": 1.
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"train_steps_per_second": 0.
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}
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{
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"epoch": 0.2,
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"total_flos": 1.7426360578342912e+16,
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"train_loss": 4.444633483886719,
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"train_runtime": 141.3075,
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"train_samples_per_second": 1.415,
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"train_steps_per_second": 0.071
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}
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trainer_state.json
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"log_history": [
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{
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"epoch": 0.2,
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"grad_norm": 0.
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"learning_rate": 0.0001,
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"loss": 4.
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"step": 10
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{
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"epoch": 0.2,
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"step": 10,
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"total_flos": 1.7426360578342912e+16,
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"train_loss": 4.
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"train_runtime":
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"train_samples_per_second": 1.
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"train_steps_per_second": 0.
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],
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"logging_steps": 10,
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"log_history": [
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{
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"epoch": 0.2,
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"grad_norm": 0.2844962775707245,
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"learning_rate": 0.0001,
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"loss": 4.4446,
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"step": 10
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},
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{
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"epoch": 0.2,
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"step": 10,
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"total_flos": 1.7426360578342912e+16,
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"train_loss": 4.444633483886719,
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"train_runtime": 141.3075,
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"train_samples_per_second": 1.415,
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"train_steps_per_second": 0.071
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}
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],
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"logging_steps": 10,
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