---
license: other
base_model: Qwen/Qwen1.5-MoE-A2.7B
tags:
- generated_from_trainer
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: false
strict: false

datasets:
  - path: Drewskidang/chatlaw
    type: sharegpt
  - path: swag/articles_and_summaries.jsonl
    ds_type: json # see other options below
    type: summarizetldr

dataset_prepared_path:
val_set_size: 0.05
output_dir: ./out

sequence_len: 4096  # supports up to 32k
sample_packing: true
pad_to_sequence_len: true
eval_sample_packing: false

wandb_project: Qwen Qwen
wandb_entity:
wandb_watch:
wandb_name:
wandb_log_model:

gradient_accumulation_steps: 8
micro_batch_size: 4
num_epochs: 3
optimizer: 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: 0.8947

## 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: 4
- eval_batch_size: 4
- seed: 42
- distributed_type: multi-GPU
- num_devices: 8
- gradient_accumulation_steps: 8
- total_train_batch_size: 256
- total_eval_batch_size: 32
- 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 |
|:-------------:|:-----:|:----:|:---------------:|
| 1.6446        | 0.13  | 1    | 1.6456          |
| 1.639         | 0.26  | 2    | 1.3070          |
| 1.1786        | 0.52  | 4    | 1.1381          |
| 1.0398        | 0.79  | 6    | 1.0396          |
| 1.0073        | 1.02  | 8    | 1.0162          |
| 0.9318        | 1.28  | 10   | 1.0095          |
| 0.9704        | 1.54  | 12   | 0.9867          |
| 0.8477        | 1.8   | 14   | 0.9405          |
| 0.7665        | 2.03  | 16   | 0.9073          |
| 0.6283        | 2.3   | 18   | 0.9021          |
| 0.6257        | 2.56  | 20   | 0.8947          |


### Framework versions

- Transformers 4.40.0.dev0
- Pytorch 2.1.1
- Datasets 2.18.0
- Tokenizers 0.15.0