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SmolLM-1.7B-Instruct_fsdp_qlora_nf4_adapter
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metadata
license: apache-2.0
library_name: peft
tags:
  - trl
  - sft
  - generated_from_trainer
base_model: HuggingFaceTB/SmolLM-1.7B-Instruct
datasets:
  - generator
model-index:
  - name: SmolLM_1_7B_Instruct_qlora_nf4
    results: []

SmolLM_1_7B_Instruct_qlora_nf4

This model is a fine-tuned version of HuggingFaceTB/SmolLM-1.7B-Instruct on the generator dataset. It achieves the following results on the evaluation set:

  • Loss: 1.6111

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: 0.001
  • train_batch_size: 4
  • eval_batch_size: 4
  • seed: 42
  • distributed_type: multi-GPU
  • num_devices: 16
  • gradient_accumulation_steps: 4
  • total_train_batch_size: 256
  • total_eval_batch_size: 64
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: cosine
  • lr_scheduler_warmup_ratio: 0.03
  • num_epochs: 20

Training results

Training Loss Epoch Step Validation Loss
2.0769 0.9524 10 1.9176
1.8602 2.0 21 1.7910
1.7729 2.9524 31 1.7320
1.7147 4.0 42 1.6913
1.6753 4.9524 52 1.6662
1.6518 6.0 63 1.6477
1.6228 6.9524 73 1.6361
1.6118 8.0 84 1.6274
1.5843 8.9524 94 1.6214
1.5805 10.0 105 1.6173
1.5712 10.9524 115 1.6151
1.5524 12.0 126 1.6133
1.5491 12.9524 136 1.6121
1.5445 14.0 147 1.6113
1.5397 14.9524 157 1.6113
1.5392 16.0 168 1.6114
1.5337 16.9524 178 1.6111
1.5347 18.0 189 1.6111
1.5337 18.9524 199 1.6111
1.5351 19.0476 200 1.6111

Framework versions

  • PEFT 0.10.0
  • Transformers 4.40.0
  • Pytorch 2.1.0
  • Datasets 2.18.0
  • Tokenizers 0.19.1