---
license: other
library_name: peft
tags:
- axolotl
- generated_from_trainer
base_model: Qwen/Qwen1.5-4B
model-index:
- name: qwen_1.5_odia_4b
  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/Qwen1.5-4B
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: OdiaGenAI/all_combined_odia_171k
    type: alpaca:chatml
dataset_prepared_path:
val_set_size: 0.05
output_dir: ./lora-out-qwen-4b-odia
hub_model_id: sam2ai/qwen_1.5_odia_4b

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-instruct-4b-odia
wandb_entity:
wandb_watch:
wandb_name:
wandb_log_model:

gradient_accumulation_steps: 2
micro_batch_size: 1
num_epochs: 4
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_4b

This model is a fine-tuned version of [Qwen/Qwen1.5-4B](https://huggingface.co/Qwen/Qwen1.5-4B) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3421

## 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
- distributed_type: multi-GPU
- num_devices: 8
- gradient_accumulation_steps: 2
- total_train_batch_size: 16
- total_eval_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 |
|:-------------:|:-----:|:-----:|:---------------:|
| 0.977         | 0.0   | 1     | 1.0190          |
| 0.4901        | 0.25  | 2108  | 0.4872          |
| 0.3966        | 0.5   | 4216  | 0.4347          |
| 0.3127        | 0.75  | 6324  | 0.4104          |
| 0.3172        | 1.0   | 8432  | 0.3932          |
| 0.281         | 1.25  | 10540 | 0.3778          |
| 0.2845        | 1.5   | 12648 | 0.3684          |
| 0.2459        | 1.75  | 14756 | 0.3616          |
| 0.1641        | 2.0   | 16864 | 0.3525          |
| 0.2121        | 2.25  | 18972 | 0.3506          |
| 0.2564        | 2.5   | 21080 | 0.3448          |
| 0.1378        | 2.75  | 23188 | 0.3426          |
| 0.2002        | 3.0   | 25296 | 0.3409          |
| 0.1671        | 3.25  | 27404 | 0.3439          |
| 0.1464        | 3.5   | 29512 | 0.3421          |
| 0.1741        | 3.75  | 31620 | 0.3421          |


### Framework versions

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