---
library_name: peft
license: other
base_model: Qwen/Qwen2.5-72B
tags:
- axolotl
- generated_from_trainer
datasets:
- sumuks/openreview_wintermute_0.2_training_data
language:
- zho
- eng
- fra
- spa
- por
- deu
- ita
- rus
- jpn
- kor
- vie
- tha
- ara
model-index:
- name: purple-wintermute-0.2-72b
  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: `0.6.0`
```yaml
base_model: Qwen/Qwen2.5-72B
hub_model_id: sumuks/purple-wintermute-0.2-72b
trust_remote_code: true

load_in_8bit: false
load_in_4bit: false
strict: false
bf16: true
hf_use_auth_token: true

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

datasets:
  - path: sumuks/openreview_wintermute_0.2_training_data
    type: completion
    field: text
dataset_prepared_path: .axolotl_cache_data/wintermute_0.2
shuffle_merged_datasets: true
# dataset_exact_deduplication: true
val_set_size: 0.005
output_dir: ./../../outputs/purple-wintermute-0.2-72b
push_dataset_to_hub: sumuks/purple_wintermute_0.2_training_data_in_progress

sequence_length: 2048
sample_packing: true
pad_to_sequence_len: true

adapter: lora
lora_r: 256
lora_alpha: 32
lora_dropout: 0.05
peft_use_rslora: true
lora_target_linear: true

gradient_accumulation_steps: 4
micro_batch_size: 8
eval_batch_size: 1
num_epochs: 3
learning_rate: 5e-5
warmup_ratio: 0.05
evals_per_epoch: 3
saves_per_epoch: 5
gradient_checkpointing: true
lr_scheduler: cosine
optimizer: paged_adamw_8bit

profiler_steps: 100
save_safetensors: true
train_on_inputs: true
wandb_project: wintermute 
wandb_name: purple-wintermute-0.2-72b
deepspeed: deepspeed_configs/zero3_bf16.json

```

</details><br>

# purple-wintermute-0.2-72b

This model is a fine-tuned version of [Qwen/Qwen2.5-72B](https://huggingface.co/Qwen/Qwen2.5-72B) on the sumuks/openreview_wintermute_0.2_training_data dataset.
It achieves the following results on the evaluation set:
- Loss: 1.3017

## 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: 5e-05
- train_batch_size: 8
- eval_batch_size: 1
- seed: 42
- distributed_type: multi-GPU
- num_devices: 8
- gradient_accumulation_steps: 4
- total_train_batch_size: 256
- total_eval_batch_size: 8
- optimizer: Use paged_adamw_8bit with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 388
- num_epochs: 3

### Training results

| Training Loss | Epoch  | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| No log        | 0.0004 | 1    | 2.5112          |
| 1.3654        | 0.3333 | 864  | 1.6504          |
| 0.9929        | 0.6665 | 1728 | 1.4144          |
| 0.9039        | 0.9998 | 2592 | 1.3083          |
| 0.8161        | 1.3333 | 3456 | 1.2935          |
| 0.7815        | 1.6665 | 4320 | 1.2816          |
| 0.7658        | 1.9998 | 5184 | 1.2775          |
| 0.7004        | 2.3333 | 6048 | 1.2995          |
| 0.6694        | 2.6665 | 6912 | 1.3013          |
| 0.6798        | 2.9998 | 7776 | 1.3017          |


### Framework versions

- PEFT 0.14.0
- Transformers 4.47.1
- Pytorch 2.5.1
- Datasets 3.2.0
- Tokenizers 0.21.0