---
license: other
library_name: peft
tags:
- generated_from_trainer
base_model: Qwen/Qwen1.5-MoE-A2.7B
model-index:
- name: out
  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: `0.4.0`
```yaml
base_model: Qwen/Qwen1.5-MoE-A2.7B
trust_remote_code: true

load_in_8bit: false
load_in_4bit: true
strict: false

datasets:
  - path: mhenrichsen/alpaca_2k_test
    type: alpaca
dataset_prepared_path:
val_set_size: 0.05
output_dir: ./out

sequence_len: 1024  # supports up to 32k
sample_packing: false
pad_to_sequence_len: false

adapter: qlora
lora_model_dir:
lora_r: 32
lora_alpha: 16
lora_dropout: 0.05
lora_target_linear: true
lora_fan_in_fan_out:

wandb_project:
wandb_entity:
wandb_watch:
wandb_name:
wandb_log_model:

gradient_accumulation_steps: 4
micro_batch_size: 1
num_epochs: 4
optimizer: paged_adamw_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: false
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
fsdp:
fsdp_config:
special_tokens:

```

</details><br>

# out

This model is a fine-tuned version of [Qwen/Qwen1.5-MoE-A2.7B](https://huggingface.co/Qwen/Qwen1.5-MoE-A2.7B) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.2553

## 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: 1
- eval_batch_size: 1
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 4
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 10
- num_epochs: 4

### Training results

| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 0.8629        | 0.0   | 1    | 0.9370          |
| 0.6917        | 0.25  | 119  | 0.8805          |
| 0.9783        | 0.5   | 238  | 0.8783          |
| 0.9578        | 0.75  | 357  | 0.8827          |
| 0.4772        | 1.0   | 476  | 0.8900          |
| 0.4653        | 1.25  | 595  | 0.9620          |
| 0.5907        | 1.5   | 714  | 0.9532          |
| 0.7364        | 1.75  | 833  | 0.9360          |
| 0.2611        | 2.0   | 952  | 0.9570          |
| 0.1999        | 2.25  | 1071 | 1.0415          |
| 0.1532        | 2.51  | 1190 | 1.0776          |
| 0.0455        | 2.76  | 1309 | 1.0920          |
| 0.087         | 3.01  | 1428 | 1.1094          |
| 0.0183        | 3.26  | 1547 | 1.2266          |
| 0.0135        | 3.51  | 1666 | 1.2604          |
| 0.1929        | 3.76  | 1785 | 1.2553          |


### Framework versions

- PEFT 0.10.0
- Transformers 4.40.0.dev0
- Pytorch 2.1.2+cu118
- Datasets 2.18.0
- Tokenizers 0.15.0