llama-3-2-1b-sft

This model is a fine-tuned version of NousResearch/Llama-3.2-1B on the HuggingFaceH4/ultrachat_200k dataset. It achieves the following results on the evaluation set:

  • Loss: 1.2759

See the training yaml https://github.com/wassname/SimPO/blob/main/training_configs/llama-3-2-1b-base-sft.yaml

Model description

More information needed

Intended uses & limitations

More information needed

Training and evaluation data

More information needed

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

  • learning_rate: 2e-05
  • train_batch_size: 8
  • eval_batch_size: 8
  • seed: 42
  • distributed_type: multi-GPU
  • gradient_accumulation_steps: 4
  • total_train_batch_size: 32
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: cosine
  • lr_scheduler_warmup_ratio: 0.1
  • num_epochs: 1

Training results

Training Loss Epoch Step Validation Loss
1.3663 0.0534 200 1.3955
1.3413 0.1069 400 1.3722
1.365 0.1603 600 1.3632
1.33 0.2138 800 1.3532
1.3219 0.2672 1000 1.3463
1.3355 0.3207 1200 1.3391
1.334 0.3741 1400 1.3305
1.3183 0.4276 1600 1.3233
1.334 0.4810 1800 1.3161
1.3013 0.5345 2000 1.3087
1.3156 0.5879 2200 1.3016
1.3092 0.6414 2400 1.2953
1.2518 0.6948 2600 1.2895
1.2617 0.7483 2800 1.2846
1.3041 0.8017 3000 1.2809
1.3102 0.8552 3200 1.2781
1.2675 0.9086 3400 1.2765
1.2978 0.9621 3600 1.2759

Framework versions

  • Transformers 4.45.1
  • Pytorch 2.4.1+cu121
  • Datasets 3.0.1
  • Tokenizers 0.20.0
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