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Word2Li/Mistral-7B-v0.3-Middo-Wizard

🤗 Hugging Face sourcetext-generationapache-2.07.2B params14 GBsafetensors✓ 4 checksumsupdated today
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Mistral-7B-v0.3-Middo-WizardLM

Paper: Middo: Model-Informed Dynamic Data Optimization for Enhanced LLM Fine-Tuning via Closed-Loop Learning

Code: https://github.com/Word2VecT/Middo

Model description

This model is a fine-tuned version of mistralai/Mistral-7B-v0.3 on the MiddOptimzed/mistral_wizard dataset.

Training and evaluation data

Training data

Middo optimized WizardLMTeam/WizardLM_evol_instruct_70k on mistralai/Mistral-7B-v0.3.

Evaluation data

  • General
    • MMLU
    • IFEval
  • Math
    • GSM8K
    • MATH
  • Code
    • HumanEval
    • MBPP
  • Reasoning
    • Hellaswag
    • GPQA

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

  • learning_rate: 1e-05
  • train_batch_size: 4
  • eval_batch_size: 8
  • seed: 42
  • distributed_type: multi-GPU
  • num_devices: 8
  • gradient_accumulation_steps: 8
  • total_train_batch_size: 256
  • total_eval_batch_size: 64
  • optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
  • lr_scheduler_type: cosine
  • lr_scheduler_warmup_ratio: 0.03
  • num_epochs: 1.0

Training results

  • epoch: 1.0
  • total_flos: 4.871785990877872e+18
  • train_loss: 0.6260631282554998
  • train_runtime: 6928.3413
  • train_samples_per_second: 12.871
  • train_steps_per_second: 0.05

Framework versions

  • Transformers 4.55.0
  • Pytorch 2.6.0+cu124
  • Datasets 3.6.0
  • Tokenizers 0.21.1