---
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
adapter: qlora
base_model: Qwen/Qwen2-7B
bf16: auto
dataset_prepared_path: null
datasets:
- path: ResplendentAI/Jeiku
  type: completion
debug: null
deepspeed: null
early_stopping_patience: null
eval_sample_packing: false
evals_per_epoch: 2
flash_attention: true
fp16: null
gradient_accumulation_steps: 4
gradient_checkpointing: true
gradient_checkpointing_kwargs:
  use_reentrant: false
group_by_length: false
learning_rate: 2.0e-05
load_in_4bit: true
load_in_8bit: false
local_rank: null
logging_steps: 1
lora_alpha: 128
lora_dropout: 0.05
lora_fan_in_fan_out: null
lora_model_dir: null
lora_r: 128
lora_target_linear: true
lr_scheduler: cosine
micro_batch_size: 2
num_epochs: 4
optimizer: adamw_torch
output_dir: ./outputs/out
pad_to_sequence_len: false
resume_from_checkpoint: null
sample_packing: false
saves_per_epoch: 1
sequence_len: 8192
special_tokens: null
strict: false
tf32: true
train_on_inputs: false
trust_remote_code: true
val_set_size: 0.05
wandb_entity: null
wandb_log_model: null
wandb_name: null
wandb_project: null
wandb_watch: null
warmup_steps: 10
weight_decay: 0.0
xformers_attention: null

```

</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: 2.8811

## 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: 2e-05
- train_batch_size: 2
- eval_batch_size: 2
- seed: 42
- gradient_accumulation_steps: 4
- 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 |
|:-------------:|:------:|:----:|:---------------:|
| 6.5037        | 0.0014 | 1    | 4.0893          |
| 3.3749        | 0.4998 | 366  | 3.0970          |
| 4.2647        | 0.9997 | 732  | 2.9672          |
| 2.2511        | 1.4995 | 1098 | 2.9058          |
| 2.5316        | 1.9993 | 1464 | 2.8220          |
| 2.2853        | 2.4991 | 1830 | 2.8457          |
| 2.2372        | 2.9990 | 2196 | 2.8423          |
| 2.5647        | 3.4988 | 2562 | 2.8807          |
| 2.7001        | 3.9986 | 2928 | 2.8811          |


### Framework versions

- PEFT 0.11.1
- Transformers 4.41.1
- Pytorch 2.1.2+cu121
- Datasets 2.19.1
- Tokenizers 0.19.1