---
license: apache-2.0
library_name: peft
tags:
- generated_from_trainer
base_model: Qwen/Qwen2-7B
model-index:
- name: outputs/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: `0.4.1`
```yaml
base_model: Qwen/Qwen2-7B
trust_remote_code: true
load_in_8bit: false
load_in_4bit: true
strict: false
datasets:
  - path: tatsu-lab/alpaca
    type: alpaca
dataset_prepared_path:
val_set_size: 0.05
output_dir: ./outputs/out
sequence_len: 2048
sample_packing: true
eval_sample_packing: true
pad_to_sequence_len: true
adapter: qlora
lora_model_dir:
lora_r: 32
lora_alpha: 64
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: 8
micro_batch_size: 1
num_epochs: 4
optimizer: adamw_torch
lr_scheduler: cosine
learning_rate: 0.0002
train_on_inputs: false
group_by_length: false
bf16: auto
fp16:
tf32: true
gradient_checkpointing: false
gradient_checkpointing_kwargs:
  use_reentrant: false
early_stopping_patience:
resume_from_checkpoint:
local_rank:
logging_steps: 1
xformers_attention:
flash_attention: false
warmup_steps: 10
evals_per_epoch: 4
saves_per_epoch: 1
debug:
deepspeed:
weight_decay: 0.0
special_tokens:

```

</details><br>

# outputs/out

This model is a fine-tuned version of [Qwen/Qwen2-7B](https://huggingface.co/Qwen/Qwen2-7B) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 4.3265

## 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: 8
- total_train_batch_size: 8
- 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 |
|:-------------:|:------:|:----:|:---------------:|
| 10.7953       | 0.0031 | 1    | 10.8104         |
| 5.4963        | 0.2513 | 80   | 5.4101          |
| 5.0323        | 0.5026 | 160  | 5.0758          |
| 4.9877        | 0.7538 | 240  | 4.8417          |
| 4.7408        | 1.0051 | 320  | 4.6180          |
| 4.5097        | 1.2442 | 400  | 4.5066          |
| 4.3959        | 1.4955 | 480  | 4.4513          |
| 4.2488        | 1.7468 | 560  | 4.4107          |
| 4.3507        | 1.9980 | 640  | 4.3784          |
| 4.2352        | 2.2352 | 720  | 4.3684          |
| 4.2141        | 2.4865 | 800  | 4.3505          |
| 4.2739        | 2.7377 | 880  | 4.3375          |
| 4.4037        | 2.9890 | 960  | 4.3310          |
| 4.195         | 3.2269 | 1040 | 4.3287          |
| 4.1996        | 3.4782 | 1120 | 4.3268          |
| 4.1353        | 3.7295 | 1200 | 4.3265          |


### Framework versions

- PEFT 0.11.1
- Transformers 4.41.1
- Pytorch 2.1.2+cu118
- Datasets 2.19.1
- Tokenizers 0.19.1