README.md

metadata

base_model: jetmoe/jetmoe-8b
tags:
  - alignment-handbook
  - generated_from_trainer
datasets:
  - HuggingFaceH4/ultrachat_200k
  - HuggingFaceH4/airoboros-3.2
  - HuggingFaceH4/Code-Feedback
  - HuggingFaceH4/orca-math-word-problems-200k
  - HuggingFaceH4/SystemChat
  - HuggingFaceH4/capybara
model-index:
  - name: jetmoe-8b-sft
    results: []

jetmoe-8b-sft

This model is a fine-tuned version of jetmoe-8b on the HuggingFaceH4/ultrachat_200k, the HuggingFaceH4/airoboros-3.2, the HuggingFaceH4/Code-Feedback, the HuggingFaceH4/orca-math-word-problems-200k, the HuggingFaceH4/SystemChat and the HuggingFaceH4/capybara datasets. It achieves the following results on the evaluation set:

• Loss: 0.9952

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: 4
• eval_batch_size: 8
• seed: 42
• distributed_type: multi-GPU
• num_devices: 8
• gradient_accumulation_steps: 4
• total_train_batch_size: 128
• 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: 0.1
• num_epochs: 3

Training results

Training Loss	Epoch	Step	Validation Loss
1.2458	1.0	2049	0.9776
1.1966	2.0	4099	0.9756
1.1073	3.0	6147	0.9952

Framework versions

• Transformers 4.39.0.dev0
• Pytorch 2.1.2
• Datasets 2.14.6
• Tokenizers 0.15.2