---
tags:
- axolotl
- generated_from_trainer
model-index:
- name: qwen-pt
  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/OpenAccess-AI-Collective/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/OpenAccess-AI-Collective/axolotl)
<details><summary>See axolotl config</summary>

axolotl version: `0.4.1`
```yaml
base_model: qwenqwenpt/out/checkpoint-331
trust_remote_code: true
hub_model_id: KolaGang/qwen-pt
hub_strategy: end

load_in_8bit: false
load_in_4bit: false
strict: false

datasets:
  - path: KolaGang/Reflection
    type: reflection
  - path: KolaGang/RAG_EAI
    type: context_qa.load_v2
  - path: lighteval/legal_summarization
    name: BillSum
    type: summarizetldr
  - path: KolaGang/QA
    type: alpaca_chat.load_qa
  - path: KolaGang/chatlaw
    type: sharegpt
  - path: KolaGang/draft
    type: alpaca
  - path: KolaGang/alpca_w_system
    type: alpaca
  - path: jondurbin/airoboros-3.1
    type: sharegpt


dataset_prepared_path: sft
val_set_size: 0.05
output_dir: ./outputs/sft

sequence_len: 4096
sample_packing: true
eval_sample_packing: false
pad_to_sequence_len: true



wandb_project: QwenQwen
wandb_entity:
wandb_watch:
wandb_name:
wandb_log_model: smalqwen

gradient_accumulation_steps: 3
micro_batch_size: 6
num_epochs: 3
optimizer: adamw_bnb_8bit
lr_scheduler: cosine
learning_rate: 0.0002

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

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

warmup_steps: 10
evals_per_epoch: 4
saves_per_epoch: 1
debug:
deepspeed:
weight_decay: 0.0

special_tokens:

```

</details><br>

# qwen-pt

This model was trained from scratch on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8421

## 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: 0.0002
- train_batch_size: 6
- eval_batch_size: 6
- seed: 42
- distributed_type: multi-GPU
- num_devices: 8
- gradient_accumulation_steps: 3
- total_train_batch_size: 144
- total_eval_batch_size: 48
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 10
- num_epochs: 3

### Training results

| Training Loss | Epoch  | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 1.3551        | 0.0087 | 1    | 1.3908          |
| 0.8062        | 0.2536 | 29   | 0.8276          |
| 0.8467        | 0.5073 | 58   | 0.7825          |
| 0.7743        | 0.7609 | 87   | 0.7598          |
| 0.8083        | 1.0146 | 116  | 0.7337          |
| 0.4953        | 1.2507 | 145  | 0.7619          |
| 0.4745        | 1.5044 | 174  | 0.7507          |
| 0.4436        | 1.7580 | 203  | 0.7342          |
| 0.4503        | 2.0117 | 232  | 0.7183          |
| 0.2062        | 2.2478 | 261  | 0.8441          |
| 0.1905        | 2.5015 | 290  | 0.8433          |
| 0.2148        | 2.7551 | 319  | 0.8421          |


### Framework versions

- Transformers 4.41.1
- Pytorch 2.1.2+cu118
- Datasets 2.19.1
- Tokenizers 0.19.1