---
license: other
library_name: peft
tags:
- axolotl
- generated_from_trainer
base_model: Qwen/Qwen1.5-0.5B
model-index:
- name: qwen_1.5_odia_0.5b
  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/Qwen1.5-0.5B
model_type: AutoModelForCausalLM
tokenizer_type: AutoTokenizer

# is_qwen_derived_model: true
trust_remote_code: true

load_in_8bit: false
load_in_4bit: true
strict: false

datasets:
  - path: OdiaGenAIdata/culturax-odia
    type: completion
dataset_prepared_path:
val_set_size: 0.05
output_dir: ./lora-out-qwen-0.5b-odia
hub_model_id: sam2ai/qwen_1.5_odia_0.5b

sequence_len: 2048  # supports up to 8192
sample_packing: false
pad_to_sequence_len:

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: Qwen-completion-0.5b-odia
wandb_entity:
wandb_watch:
wandb_name:
wandb_log_model:

gradient_accumulation_steps: 4
micro_batch_size: 2
num_epochs: 10
optimizer: adamw_bnb_8bit
lr_scheduler: cosine
learning_rate: 0.0002

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

gradient_checkpointing: false
early_stopping_patience:
resume_from_checkpoint:
local_rank:
logging_steps: 1
xformers_attention:
flash_attention:

warmup_steps: 10
evals_per_epoch: 4
eval_table_size:
eval_table_max_new_tokens: 128
saves_per_epoch: 1
debug:
deepspeed:
weight_decay: 0.0
fsdp:
fsdp_config:
special_tokens:

```

</details><br>

# qwen_1.5_odia_0.5b

This model is a fine-tuned version of [Qwen/Qwen1.5-0.5B](https://huggingface.co/Qwen/Qwen1.5-0.5B) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4242

## 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: 2
- eval_batch_size: 2
- seed: 42
- distributed_type: multi-GPU
- num_devices: 8
- gradient_accumulation_steps: 4
- total_train_batch_size: 64
- total_eval_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 10
- num_epochs: 10

### Training results

| Training Loss | Epoch | Step  | Validation Loss |
|:-------------:|:-----:|:-----:|:---------------:|
| 1.2821        | 0.0   | 1     | 1.2706          |
| 0.5906        | 0.25  | 1366  | 0.5987          |
| 0.531         | 0.5   | 2732  | 0.5510          |
| 0.5095        | 0.75  | 4098  | 0.5236          |
| 0.5027        | 1.0   | 5464  | 0.5054          |
| 0.5019        | 1.25  | 6830  | 0.4933          |
| 0.4798        | 1.5   | 8196  | 0.4845          |
| 0.4484        | 1.75  | 9562  | 0.4771          |
| 0.4526        | 2.0   | 10928 | 0.4704          |
| 0.4498        | 2.25  | 12294 | 0.4657          |
| 0.4508        | 2.5   | 13660 | 0.4608          |
| 0.4226        | 2.75  | 15026 | 0.4568          |
| 0.4161        | 3.0   | 16392 | 0.4539          |
| 0.4258        | 3.25  | 17758 | 0.4515          |
| 0.428         | 3.5   | 19124 | 0.4489          |
| 0.4748        | 3.75  | 20490 | 0.4459          |
| 0.4083        | 4.0   | 21856 | 0.4441          |
| 0.4278        | 4.25  | 23222 | 0.4423          |
| 0.3997        | 4.5   | 24588 | 0.4406          |
| 0.4581        | 4.75  | 25954 | 0.4386          |
| 0.378         | 5.0   | 27320 | 0.4372          |
| 0.4141        | 5.25  | 28686 | 0.4358          |
| 0.4017        | 5.5   | 30052 | 0.4344          |
| 0.4223        | 5.75  | 31418 | 0.4328          |
| 0.426         | 6.0   | 32784 | 0.4317          |
| 0.3967        | 6.25  | 34150 | 0.4310          |
| 0.3934        | 6.5   | 35516 | 0.4298          |
| 0.404         | 6.75  | 36882 | 0.4287          |
| 0.3874        | 7.0   | 38248 | 0.4282          |
| 0.384         | 7.25  | 39614 | 0.4275          |
| 0.3925        | 7.5   | 40980 | 0.4268          |
| 0.409         | 7.75  | 42346 | 0.4261          |
| 0.3891        | 8.0   | 43712 | 0.4256          |
| 0.41          | 8.25  | 45078 | 0.4253          |
| 0.3999        | 8.5   | 46444 | 0.4249          |
| 0.3874        | 8.75  | 47810 | 0.4247          |
| 0.3894        | 9.0   | 49176 | 0.4245          |
| 0.3827        | 9.25  | 50542 | 0.4244          |
| 0.3815        | 9.5   | 51908 | 0.4243          |
| 0.3816        | 9.75  | 53274 | 0.4242          |


### Framework versions

- PEFT 0.8.2
- Transformers 4.37.0
- Pytorch 2.0.1+gita61a294
- Datasets 2.16.1
- Tokenizers 0.15.0