---
library_name: peft
license: apache-2.0
base_model: Qwen/Qwen2.5-32B-Instruct
tags:
- generated_from_trainer
model-index:
- name: Qwen-DGM-32B
  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. -->

[<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl)
<details><summary>See axolotl config</summary>

axolotl version: `0.10.0`
```yaml
base_model: Qwen/Qwen2.5-32B-Instruct

plugins:
  - axolotl.integrations.liger.LigerPlugin
  - axolotl.integrations.cut_cross_entropy.CutCrossEntropyPlugin

liger_rope: true
liger_rms_norm: true
liger_glu_activation: true
liger_layer_norm: true
liger_fused_linear_cross_entropy: false

lora_mlp_kernel: true
lora_qkv_kernel: true
lora_o_kernel: true

load_in_8bit: false
load_in_4bit: false

sequence_len: 5120
max_sample_length: 5120

sample_packing: true
gradient_checkpointing: true
flash_attention: true

bf16: true
tf32: true


datasets:
  - path: ./DGM_SFT_mix.jsonl
    type: chat_template
    chat_template: chatml
    roles_to_train: []
    message_field_training: train

adapter: lora
lora_r: 64
lora_alpha: 128
lora_dropout: 0.0
lora_bias: None
lora_target_linear: true
use_tensorboard: true

optimizer: paged_adamw_8bit
learning_rate: 2.5e-5
loraplus_lr_ratio: 16

# Training arguments
output_dir: ./Qwen-DGM-32B
num_epochs: 1
micro_batch_size: 1
gradient_accumulation_steps: 16
warmup_ratio: 0.05
lr_scheduler: 'linear'
max_grad_norm: 1
save_steps: 100
logging_steps: 1
seed: 42
```

</details><br>

# Qwen-DGM-32B

This model is a fine-tuned version of [Qwen/Qwen2.5-32B-Instruct](https://huggingface.co/Qwen/Qwen2.5-32B-Instruct) on the ./DGM_SFT_mix.jsonl 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: 2.5e-05
- train_batch_size: 1
- eval_batch_size: 1
- seed: 42
- gradient_accumulation_steps: 16
- total_train_batch_size: 16
- optimizer: Use OptimizerNames.PAGED_ADAMW_8BIT with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 7
- training_steps: 154

### Training results



### Framework versions

- PEFT 0.15.2
- Transformers 4.52.3
- Pytorch 2.8.0+cu128
- Datasets 3.6.0
- Tokenizers 0.21.4