tinyllama_moe_sft_ultrachat-slimorca

This model is a fine-tuned version of ondevicellm/tinyllama_moe on the HuggingFaceH4/ultrachat_200k and the ondevicellm/SlimOrca datasets. It achieves the following results on the evaluation set:

  • Loss: 1.1526

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: 16
  • eval_batch_size: 8
  • seed: 42
  • distributed_type: multi-GPU
  • num_devices: 4
  • gradient_accumulation_steps: 2
  • 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_steps: 120
  • num_epochs: 1

Training results

Training Loss Epoch Step Validation Loss
1.4601 0.05 100 1.3361
1.3324 0.1 200 1.2566
1.2946 0.14 300 1.2279
1.2767 0.19 400 1.2111
1.2298 0.24 500 1.1995
1.2247 0.29 600 1.1902
1.2208 0.34 700 1.1833
1.2375 0.39 800 1.1775
1.2038 0.43 900 1.1726
1.1926 0.48 1000 1.1683
1.1933 0.53 1100 1.1649
1.1893 0.58 1200 1.1618
1.2029 0.63 1300 1.1593
1.2201 0.68 1400 1.1572
1.1741 0.72 1500 1.1557
1.1813 0.77 1600 1.1545
1.1668 0.82 1700 1.1536
1.1495 0.87 1800 1.1530
1.1595 0.92 1900 1.1527
1.1607 0.97 2000 1.1526

Framework versions

  • Transformers 4.36.2
  • Pytorch 2.0.1+cu117
  • Datasets 2.16.1
  • Tokenizers 0.15.0
Downloads last month
2
Safetensors
Model size
6.43B params
Tensor type
BF16
·
Inference Providers NEW
This model is not currently available via any of the supported third-party Inference Providers, and the model is not deployed on the HF Inference API.

Model tree for ondevicellm/tinyllama_moe_sft_ultrachat-slimorca

Finetuned
(4)
this model

Datasets used to train ondevicellm/tinyllama_moe_sft_ultrachat-slimorca