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---
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 |