---
license: apache-2.0
library_name: peft
tags:
- axolotl
- generated_from_trainer
base_model: unsloth/gemma-7b
model-index:
- name: gemma_odia_7b_unsloth
  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
# use google/gemma-7b if you have access
base_model: unsloth/gemma-7b
model_type: AutoModelForCausalLM
tokenizer_type: AutoTokenizer

load_in_8bit: false
load_in_4bit: true
strict: false

# huggingface repo
datasets:
  - path: OdiaGenAIdata/culturax-gemma-data
    type: completion
val_set_size: 0.1
output_dir: ./gemma-odia-7b-pretrain-unsloth
hub_model_id: sam2ai/gemma_odia_7b_unsloth

adapter: qlora
lora_r: 32
lora_alpha: 16
lora_dropout: 0.05
lora_target_linear: true

sequence_len: 4096
sample_packing: true
pad_to_sequence_len: true

wandb_project: gemma-completion-7b-odia-unsloth
wandb_entity:
wandb_watch:
wandb_name:
wandb_log_model:


gradient_accumulation_steps: 8
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: true
early_stopping_patience:
resume_from_checkpoint:
local_rank:
logging_steps: 1
xformers_attention:
flash_attention: false

warmup_ratio: 0.1
evals_per_epoch: 4
eval_table_size:
eval_max_new_tokens: 128
saves_per_epoch: 1
debug:
deepspeed:
weight_decay: 0.0
fsdp:
fsdp_config:
special_tokens:

```

</details><br>

# gemma_odia_7b_unsloth

This model is a fine-tuned version of [unsloth/gemma-7b](https://huggingface.co./unsloth/gemma-7b) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 2.9914

## 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: 8
- total_train_batch_size: 128
- 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: 32
- num_epochs: 10

### Training results

| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 39.5782       | 0.0   | 1    | 39.2579         |
| 7.2511        | 0.25  | 169  | 7.0771          |
| 4.2519        | 0.5   | 338  | 4.0654          |
| 3.7348        | 0.75  | 507  | 3.5937          |
| 3.4573        | 1.0   | 676  | 3.3126          |
| 3.4299        | 1.24  | 845  | 3.2429          |
| 3.4908        | 1.49  | 1014 | 3.2063          |
| 3.3588        | 1.74  | 1183 | 3.1614          |
| 3.3646        | 1.99  | 1352 | 3.1313          |
| 3.2672        | 2.23  | 1521 | 3.0885          |
| 3.2706        | 2.48  | 1690 | 3.0678          |
| 3.173         | 2.73  | 1859 | 3.0410          |
| 3.7319        | 2.98  | 2028 | 3.5392          |
| 3.3142        | 3.22  | 2197 | 3.1610          |
| 3.2931        | 3.47  | 2366 | 3.1339          |
| 3.3045        | 3.72  | 2535 | 3.0710          |
| 3.2423        | 3.97  | 2704 | 3.0920          |
| 3.2565        | 4.2   | 2873 | 3.0311          |
| 3.1167        | 4.45  | 3042 | 3.0039          |
| 3.1624        | 4.71  | 3211 | 3.0108          |
| 3.1697        | 4.96  | 3380 | 3.1008          |
| 3.1434        | 5.19  | 3549 | 2.9915          |
| 3.2301        | 5.44  | 3718 | 3.0033          |
| 3.1686        | 5.69  | 3887 | 2.9893          |
| 3.9959        | 5.95  | 4056 | 3.7561          |
| 3.3066        | 6.18  | 4225 | 3.1076          |
| 3.2567        | 6.43  | 4394 | 3.0679          |
| 3.1764        | 6.68  | 4563 | 3.0459          |
| 3.1848        | 6.93  | 4732 | 3.0342          |
| 3.181         | 7.17  | 4901 | 3.0279          |
| 3.1688        | 7.42  | 5070 | 3.0203          |
| 3.1474        | 7.67  | 5239 | 3.0131          |
| 3.1672        | 7.92  | 5408 | 3.0080          |
| 3.1202        | 8.16  | 5577 | 3.0036          |
| 3.1368        | 8.41  | 5746 | 2.9999          |
| 3.1104        | 8.66  | 5915 | 2.9968          |
| 3.1236        | 8.91  | 6084 | 2.9939          |
| 3.1055        | 9.15  | 6253 | 2.9924          |
| 3.1563        | 9.4   | 6422 | 2.9918          |
| 3.1373        | 9.65  | 6591 | 2.9914          |


### Framework versions

- PEFT 0.9.0
- Transformers 4.40.0.dev0
- Pytorch 2.4.0.dev20240326+rocm6.0
- Datasets 2.18.0
- Tokenizers 0.15.0