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metadata
library_name: peft
tags:
  - generated_from_trainer
base_model: awilliamson/tinyllama-slider
model-index:
  - name: empathy-lora-out
    results: []

Built with Axolotl

empathy-lora-out

This model is a fine-tuned version of awilliamson/tinyllama-slider on the None dataset. It achieves the following results on the evaluation set:

  • Loss: 1.2988

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: 0.0002
  • train_batch_size: 2
  • eval_batch_size: 2
  • seed: 42
  • gradient_accumulation_steps: 4
  • total_train_batch_size: 8
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: cosine
  • lr_scheduler_warmup_steps: 25
  • num_epochs: 5

Training results

Training Loss Epoch Step Validation Loss
1.5961 0.03 1 1.5286
1.6946 0.27 8 1.5283
1.5327 0.54 16 1.5186
1.4197 0.81 24 1.4726
1.4979 1.03 32 1.4323
1.3198 1.3 40 1.3997
1.4632 1.57 48 1.3762
1.3486 1.84 56 1.3547
1.3901 2.08 64 1.3386
1.3964 2.35 72 1.3291
1.3844 2.62 80 1.3184
1.2799 2.89 88 1.3108
1.3213 3.13 96 1.3061
1.0652 3.39 104 1.3050
1.1837 3.66 112 1.3024
1.4713 3.93 120 1.2992
1.3074 4.17 128 1.2979
1.2039 4.44 136 1.2993
1.1574 4.71 144 1.2988

Framework versions

  • Transformers 4.37.0.dev0
  • Pytorch 2.0.1
  • Datasets 2.15.0
  • Tokenizers 0.15.0

Training procedure

The following bitsandbytes quantization config was used during training:

  • quant_method: bitsandbytes
  • load_in_8bit: True
  • load_in_4bit: False
  • llm_int8_threshold: 6.0
  • llm_int8_skip_modules: None
  • llm_int8_enable_fp32_cpu_offload: False
  • llm_int8_has_fp16_weight: False
  • bnb_4bit_quant_type: fp4
  • bnb_4bit_use_double_quant: False
  • bnb_4bit_compute_dtype: float32

Framework versions

  • PEFT 0.6.0