---
library_name: transformers
license: apache-2.0
base_model: Qwen/Qwen2.5-7B
tags:
- generated_from_trainer
model-index:
- name: ckpts
  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: `0.5.2`
```yaml
base_model: Qwen/Qwen2.5-7B

plugins:
  - axolotl.integrations.liger.LigerPlugin
liger_rope: true
liger_rms_norm: true
liger_glu_activation: true
liger_fused_linear_cross_entropy: true

strict: false

chat_template: chatml
datasets:
  - path: allenai/tulu-3-sft-mixture
    type: chat_template
    split: train
    field_messages: messages

dataset_prepared_path: last_run_prepared
#val_set_size: 0.02
output_dir: ./ckpts

sequence_len: 8192
#sample_packing: true
pad_to_sequence_len: true

wandb_project: qwen-2.5-7b-sft
wandb_entity:
wandb_watch:
wandb_name:
wandb_log_model:

gradient_accumulation_steps: 2
micro_batch_size: 8
num_epochs: 1
optimizer: paged_ademamix_8bit
lr_scheduler: cosine
learning_rate: 3.5e-6

train_on_inputs: false
group_by_length: false
bf16: auto
fp16:
tf32: false

gradient_checkpointing: true
gradient_checkpointing_kwargs:
  use_reentrant: false
early_stopping_patience:
resume_from_checkpoint:
logging_steps: 1
xformers_attention:
flash_attention: true

deepspeed: deepspeed_configs/zero3_bf16.json

warmup_steps: 370
#evals_per_epoch: 4
eval_table_size:
saves_per_epoch: 2
debug:
weight_decay: 0.0

```

</details><br>

# ckpts

This model is a fine-tuned version of [Qwen/Qwen2.5-7B](https://huggingface.co/Qwen/Qwen2.5-7B) on the None 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: 3.5e-06
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- distributed_type: multi-GPU
- num_devices: 8
- gradient_accumulation_steps: 2
- total_train_batch_size: 128
- total_eval_batch_size: 64
- optimizer: Use paged_ademamix_8bit and the args are:
No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 370
- num_epochs: 1

### Training results



### Framework versions

- Transformers 4.46.3
- Pytorch 2.5.1+cu124
- Datasets 3.1.0
- Tokenizers 0.20.3