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metadata
license: apache-2.0
base_model: HuggingFaceTB/SmolLM-360M
tags:
  - alignment-handbook
  - trl
  - sft
  - generated_from_trainer
  - trl
  - sft
  - generated_from_trainer
datasets:
  - HuggingFaceTB/Magpie-Pro-300K-Filtered-H4
  - HuggingFaceTB/self-oss-instruct-sc2-H4
  - HuggingFaceTB/OpenHermes-2.5-H4
  - HuggingFaceTB/instruct-data-basics-H4
model-index:
  - name: smollm-350M-instruct-test2-ep2
    results: []

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smollm-350M-instruct-test2-ep2

This model is a fine-tuned version of HuggingFaceTB/SmolLM-360M on the HuggingFaceTB/Magpie-Pro-300K-Filtered-H4, the HuggingFaceTB/self-oss-instruct-sc2-H4, the HuggingFaceTB/OpenHermes-2.5-H4 and the HuggingFaceTB/instruct-data-basics-H4 datasets. It achieves the following results on the evaluation set:

  • Loss: 1.1973

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: 8
  • gradient_accumulation_steps: 4
  • total_train_batch_size: 128
  • total_eval_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: 2

Training results

Training Loss Epoch Step Validation Loss
0.8575 1.0 816 1.2251
0.7238 2.0 1632 1.1973

Framework versions

  • Transformers 4.42.3
  • Pytorch 2.1.2
  • Datasets 2.20.0
  • Tokenizers 0.19.1