---
library_name: peft
license: gemma
base_model: unsloth/gemma-2-2b
tags:
- axolotl
- generated_from_trainer
model-index:
- name: f7a9fbea-d994-4e0e-bfbf-06206445ed8f
  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: `0.4.1`
```yaml
adapter: lora
base_model: unsloth/gemma-2-2b
bf16: auto
chat_template: llama3
cosine_min_lr_ratio: 0.1
data_processes: 16
dataset_prepared_path: null
datasets:
- data_files:
  - 32799483a740097f_train_data.json
  ds_type: json
  format: custom
  path: /workspace/input_data/32799483a740097f_train_data.json
  type:
    field_input: gender
    field_instruction: en
    field_output: es
    format: '{instruction} {input}'
    no_input_format: '{instruction}'
    system_format: '{system}'
    system_prompt: ''
debug: null
deepspeed: null
device_map: '{'''':torch.cuda.current_device()}'
do_eval: true
early_stopping_patience: 60
eval_batch_size: 1
eval_sample_packing: false
eval_steps: 25
evaluation_strategy: steps
flash_attention: true
fp16: null
fsdp: null
fsdp_config: null
gradient_accumulation_steps: 64
gradient_checkpointing: true
group_by_length: true
hub_model_id: sn56m3/f7a9fbea-d994-4e0e-bfbf-06206445ed8f
hub_repo: stevemonite
hub_strategy: checkpoint
hub_token: null
learning_rate: 0.0001
load_in_4bit: false
load_in_8bit: false
local_rank: null
logging_steps: 1
lora_alpha: 32
lora_dropout: 0.05
lora_fan_in_fan_out: null
lora_model_dir: null
lora_r: 16
lora_target_linear: true
lora_target_modules:
- q_proj
- v_proj
lr_scheduler: cosine
max_grad_norm: 1.0
max_memory:
  0: 70GiB
max_steps: 666
micro_batch_size: 1
mlflow_experiment_name: /tmp/32799483a740097f_train_data.json
model_type: AutoModelForCausalLM
num_epochs: 4
optim_args:
  adam_beta1: 0.9
  adam_beta2: 0.95
  adam_epsilon: 1e-5
optimizer: adamw_torch
output_dir: miner_id_24
pad_to_sequence_len: true
resume_from_checkpoint: null
s2_attention: null
sample_packing: false
save_steps: 50
save_strategy: steps
sequence_len: 2048
strict: false
tf32: false
tokenizer_type: AutoTokenizer
torch_compile: false
train_on_inputs: false
trust_remote_code: true
val_set_size: 50
wandb_entity: sn56-miner
wandb_mode: disabled
wandb_name: null
wandb_project: god
wandb_run: w2k6
wandb_runid: null
warmup_raio: 0.03
warmup_ratio: 0.05
weight_decay: 0.01
xformers_attention: null

```

</details><br>

# f7a9fbea-d994-4e0e-bfbf-06206445ed8f

This model is a fine-tuned version of [unsloth/gemma-2-2b](https://huggingface.co/unsloth/gemma-2-2b) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8542

## 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: 0.0001
- train_batch_size: 1
- eval_batch_size: 1
- seed: 42
- distributed_type: multi-GPU
- num_devices: 4
- gradient_accumulation_steps: 64
- total_train_batch_size: 256
- total_eval_batch_size: 4
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=adam_beta1=0.9,adam_beta2=0.95,adam_epsilon=1e-5
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 33
- training_steps: 666

### Training results

| Training Loss | Epoch  | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 1.6422        | 0.0008 | 1    | 3.9106          |
| 0.8483        | 0.0211 | 25   | 1.0149          |
| 0.7166        | 0.0422 | 50   | 0.9550          |
| 0.7464        | 0.0633 | 75   | 0.9110          |
| 0.821         | 0.0844 | 100  | 0.9160          |
| 0.6771        | 0.1055 | 125  | 0.9025          |
| 0.7014        | 0.1266 | 150  | 0.8940          |
| 0.6945        | 0.1477 | 175  | 0.8904          |
| 0.728         | 0.1688 | 200  | 0.8883          |
| 0.6273        | 0.1898 | 225  | 0.8844          |
| 0.7142        | 0.2109 | 250  | 0.8767          |
| 0.7023        | 0.2320 | 275  | 0.8791          |
| 0.7075        | 0.2531 | 300  | 0.8649          |
| 0.6953        | 0.2742 | 325  | 0.8715          |
| 0.6841        | 0.2953 | 350  | 0.8684          |
| 0.6334        | 0.3164 | 375  | 0.8652          |
| 0.7264        | 0.3375 | 400  | 0.8651          |
| 0.669         | 0.3586 | 425  | 0.8657          |
| 0.67          | 0.3797 | 450  | 0.8620          |
| 0.6409        | 0.4008 | 475  | 0.8569          |
| 0.6531        | 0.4219 | 500  | 0.8557          |
| 0.6698        | 0.4430 | 525  | 0.8561          |
| 0.7231        | 0.4641 | 550  | 0.8552          |
| 0.6126        | 0.4852 | 575  | 0.8560          |
| 0.647         | 0.5063 | 600  | 0.8546          |
| 0.689         | 0.5273 | 625  | 0.8545          |
| 0.7106        | 0.5484 | 650  | 0.8542          |


### Framework versions

- PEFT 0.13.2
- Transformers 4.46.0
- Pytorch 2.5.0+cu124
- Datasets 3.0.1
- Tokenizers 0.20.1