---
license: other
library_name: peft
tags:
- generated_from_trainer
base_model: google/gemma-7b
model-index:
- name: gemma-python
  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: google/gemma-7b
model_type: AutoModelForCausalLM
tokenizer_type: AutoTokenizer


load_in_8bit: false
load_in_4bit: true
strict: false

# huggingface repo
datasets:
  - path: ./dataset/data1.jsonl
    type: input_output
val_set_size: 0.1
output_dir: ./gemma-python

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

sequence_len: 4096
sample_packing: false
pad_to_sequence_len: true

wandb_project:
wandb_entity:
wandb_watch:
wandb_name:
wandb_log_model:


gradient_accumulation_steps: 3
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: true

warmup_ratio: 0.1
evals_per_epoch: 4
eval_table_size:
eval_max_new_tokens: 128
saves_per_epoch: 1
debug:
deepspeed: deepspeed_configs/zero1.json
weight_decay: 0.0
fsdp:
fsdp_config:
special_tokens:

```

</details><br>

# gemma-python

This model is a fine-tuned version of [google/gemma-7b](https://huggingface.co/google/gemma-7b) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 2.1143

## 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: 4
- gradient_accumulation_steps: 3
- total_train_batch_size: 24
- 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: 2
- num_epochs: 10

### Training results

| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 19.0016       | 0.12  | 1    | 18.6992         |
| 19.4686       | 0.25  | 2    | 16.2578         |
| 11.468        | 0.5   | 4    | 8.2891          |
| 7.5305        | 0.75  | 6    | 5.8847          |
| 5.7572        | 1.0   | 8    | 4.3635          |
| 4.3903        | 1.25  | 10   | 3.2849          |
| 2.9497        | 1.5   | 12   | 2.8539          |
| 2.8738        | 1.75  | 14   | 2.6203          |
| 2.7298        | 2.0   | 16   | 2.4534          |
| 2.4284        | 2.25  | 18   | 2.3077          |
| 2.394         | 2.5   | 20   | 2.1876          |
| 2.069         | 2.75  | 22   | 2.1294          |
| 1.9355        | 3.0   | 24   | 2.1048          |
| 1.9635        | 3.25  | 26   | 2.0707          |
| 2.092         | 3.5   | 28   | 2.0596          |
| 1.9675        | 3.75  | 30   | 2.0287          |
| 1.9693        | 4.0   | 32   | 2.0220          |
| 2.0198        | 4.25  | 34   | 2.0124          |
| 1.9357        | 4.5   | 36   | 1.9946          |
| 1.8147        | 4.75  | 38   | 1.9979          |
| 1.9084        | 5.0   | 40   | 1.9751          |
| 1.6678        | 5.25  | 42   | 2.0049          |
| 1.7639        | 5.5   | 44   | 1.9885          |
| 1.7475        | 5.75  | 46   | 1.9777          |
| 1.4848        | 6.0   | 48   | 1.9939          |
| 1.3065        | 6.25  | 50   | 2.0264          |
| 1.4792        | 6.5   | 52   | 2.0125          |
| 1.4233        | 6.75  | 54   | 2.0204          |
| 1.2534        | 7.0   | 56   | 2.0318          |
| 1.2409        | 7.25  | 58   | 2.0445          |
| 1.4309        | 7.5   | 60   | 2.0641          |
| 1.1622        | 7.75  | 62   | 2.0633          |
| 1.228         | 8.0   | 64   | 2.0930          |
| 1.3076        | 8.25  | 66   | 2.1077          |
| 1.2323        | 8.5   | 68   | 2.1060          |
| 1.1635        | 8.75  | 70   | 2.1039          |
| 1.261         | 9.0   | 72   | 2.1068          |
| 1.0122        | 9.25  | 74   | 2.1110          |
| 1.218         | 9.5   | 76   | 2.1180          |
| 1.1022        | 9.75  | 78   | 2.1226          |
| 1.2072        | 10.0  | 80   | 2.1143          |


### Framework versions

- PEFT 0.9.0
- Transformers 4.38.2
- Pytorch 2.2.1
- Datasets 2.18.0
- Tokenizers 0.15.0