README.md

metadata

library_name: transformers
license: apache-2.0
base_model: secmlr/DS-Clean_QWQ-Clean_Qwen2.5-7B-Instruct_full_sft_1e-5
tags:
  - llama-factory
  - full
  - generated_from_trainer
model-index:
  - name: >-
      ruizhe_simplier_dsNsy32kCln32k_QwQNsy32kCln32k_DSCln_QWQCln_Qwen7B_summarized_sft
    results: []

ruizhe_simplier_dsNsy32kCln32k_QwQNsy32kCln32k_DSCln_QWQCln_Qwen7B_summarized_sft

This model is a fine-tuned version of secmlr/DS-Clean_QWQ-Clean_Qwen2.5-7B-Instruct_full_sft_1e-5 on the ruizhe_simplier_reasoning_ds_clean_32k, the ruizhe_simplier_reasoning_ds_noisy_32k, the ruizhe_simplier_reasoning_qwq_clean_32k and the ruizhe_simplier_reasoning_qwq_noisy_small_32k datasets.

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: 1e-05
• train_batch_size: 1
• eval_batch_size: 8
• seed: 42
• distributed_type: multi-GPU
• num_devices: 4
• gradient_accumulation_steps: 12
• total_train_batch_size: 48
• total_eval_batch_size: 32
• optimizer: Use OptimizerNames.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: 0.1
• num_epochs: 3.0

Training results

Framework versions

• Transformers 4.48.3
• Pytorch 2.6.0+cu124
• Datasets 3.2.0
• Tokenizers 0.21.0