---
library_name: peft
license: other
base_model: /home/hz1/rzhong/Qwen2.5/LLaMA-Factory/output/QwQ-32B-Preview-Instruct/lora/train_2025-03-20-10-02-27_cpt
tags:
- llama-factory
- lora
- generated_from_trainer
model-index:
- name: train_2025-03-20-10-02-27_sft_lora
  results: []
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->

# train_2025-03-20-10-02-27_sft_lora

This model is a fine-tuned version of [/home/hz1/rzhong/Qwen2.5/LLaMA-Factory/output/QwQ-32B-Preview-Instruct/lora/train_2025-03-20-10-02-27_cpt](https://huggingface.co//home/hz1/rzhong/Qwen2.5/LLaMA-Factory/output/QwQ-32B-Preview-Instruct/lora/train_2025-03-20-10-02-27_cpt) on the SFT dataset.

## 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: 0.0001
- train_batch_size: 4
- eval_batch_size: 8
- seed: 42
- distributed_type: multi-GPU
- num_devices: 4
- gradient_accumulation_steps: 8
- total_train_batch_size: 128
- 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
- num_epochs: 15.0

### Training results



### Framework versions

- PEFT 0.12.0
- Transformers 4.49.0
- Pytorch 2.6.0+cu124
- Datasets 3.3.2
- Tokenizers 0.21.0