---
library_name: peft
license: gemma
base_model: google/gemma-3-1b-it
tags:
- generated_from_trainer
model-index:
- name: mnt/d/druban/axolotl/try2
  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/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl)
<details><summary>See axolotl config</summary>

axolotl version: `0.8.0`
```yaml
base_model: google/gemma-3-1b-it
model_type: gemma
tokenizer_type: AutoTokenizer
trust_remote_code: true

load_in_4bit: true
adapter: qlora

lora_r: 8                          # 💡 уменьшено для маленького датасета
lora_alpha: 16                    # 💡 пропорционально уменьшено
lora_dropout: 0.05
lora_target_modules:
  - q_proj
  - k_proj
  - v_proj
  - o_proj
  - gate_proj
  - up_proj
  - down_proj


deepspeed: /mnt/d/druban/axolotl/try1/zero1.json

datasets:
  - path: /mnt/d/druban/axolotl/try1/data/finetuning-dataset-chatml.jsonl
    type: chat_template
    field_messages: conversations

val_set_size: 0.1                 # 💡 10% для более надёжной валидации

output_dir: /mnt/d/druban/axolotl/try2

sequence_len: 2048
sample_packing: true
eval_sample_packing: false

gradient_checkpointing: true

batch_size: 2
micro_batch_size: 1

num_epochs: 10                    # 💡 увеличено с 3 до 10 для глубокого дообучения

optimizer: adamw_bnb_8bit
learning_rate: 5e-5               # 💡 чуть выше для малых данных
lr_scheduler: cosine
warmup_steps: 10

wandb_project: gemma-finetune
```

</details><br>

# mnt/d/druban/axolotl/try2

This model is a fine-tuned version of [google/gemma-3-1b-it](https://huggingface.co/google/gemma-3-1b-it) on the /mnt/d/druban/axolotl/try1/data/finetuning-dataset-chatml.jsonl dataset.
It achieves the following results on the evaluation set:
- Loss: 4.5024

## 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: 5e-05
- train_batch_size: 1
- eval_batch_size: 1
- seed: 42
- distributed_type: multi-GPU
- num_devices: 2
- total_train_batch_size: 2
- total_eval_batch_size: 2
- optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 10
- num_epochs: 10.0

### Training results

| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 6.4781        | 1.0   | 7    | 13.0395         |
| 5.5099        | 2.0   | 14   | 9.8655          |
| 4.5869        | 3.0   | 21   | 7.0045          |
| 4.1804        | 4.0   | 28   | 5.8688          |
| 4.0683        | 5.0   | 35   | 5.1847          |
| 4.0524        | 6.0   | 42   | 4.7762          |
| 3.9197        | 7.0   | 49   | 4.6118          |
| 3.6931        | 8.0   | 56   | 4.5488          |
| 3.995         | 9.0   | 63   | 4.5053          |
| 3.7989        | 10.0  | 70   | 4.5024          |


### Framework versions

- PEFT 0.15.1
- Transformers 4.51.0
- Pytorch 2.5.1+cu121
- Datasets 3.5.0
- Tokenizers 0.21.1