---
library_name: transformers
license: gemma
base_model: google/gemma-2-2b
tags:
- axolotl
- generated_from_trainer
model-index:
- name: gemma-2-2b-magpie-gemma2-9b
  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
base_model: google/gemma-2-2b
model_type: Gemma2ForCausalLM
tokenizer_type: AutoTokenizer
chat_template: gemma

load_in_8bit: false
load_in_4bit: false
strict: false

datasets:
  - path: flydust/Magpie-100k-Gemma2-9B
    type: sharegpt
    chat_template: gemma
dataset_prepared_path: last_run_prepared
val_set_size: 0.001
output_dir: axolotl_out/gemma-2-2b-magpie-gemma2-9b

sequence_len: 4096
sample_packing: true
eval_sample_packing: false
pad_to_sequence_len: true

wandb_project: SynDa
wandb_entity:
wandb_watch:
wandb_name: gemma-2-2b-magpie-gemma2-9b
wandb_log_model:
hub_model_id: flydust/gemma-2-2b-magpie-gemma2-9b

gradient_accumulation_steps: 8
micro_batch_size: 1
num_epochs: 2
optimizer: paged_adamw_8bit
lr_scheduler: cosine
learning_rate: 2e-5

train_on_inputs: false
group_by_length: false
bf16: true
fp16:
tf32: false

gradient_checkpointing: true
gradient_checkpointing_kwargs:
  use_reentrant: false
early_stopping_patience:
resume_from_checkpoint:
logging_steps: 1
xformers_attention:
# Disable flash attention
# flash_attention: false
# sdp_attention: falses
eager_attention: true

warmup_ratio: 0.1
evals_per_epoch: 5
eval_table_size:
saves_per_epoch: 1
debug:
deepspeed:
weight_decay: 0.0
fsdp:
fsdp_config:
special_tokens:

```

</details><br>

# gemma-2-2b-magpie-gemma2-9b

This model is a fine-tuned version of [google/gemma-2-2b](https://huggingface.co/google/gemma-2-2b) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6998

## 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: 2e-05
- train_batch_size: 1
- eval_batch_size: 1
- seed: 42
- distributed_type: multi-GPU
- num_devices: 4
- gradient_accumulation_steps: 8
- total_train_batch_size: 32
- total_eval_batch_size: 4
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 79
- num_epochs: 2

### Training results

| Training Loss | Epoch  | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 4.7852        | 0.0023 | 1    | 1.1984          |
| 0.8091        | 0.2011 | 86   | 0.8370          |
| 0.7305        | 0.4022 | 172  | 0.7686          |
| 0.6761        | 0.6033 | 258  | 0.7394          |
| 0.6618        | 0.8044 | 344  | 0.7141          |
| 0.6197        | 1.0056 | 430  | 0.6983          |
| 0.5014        | 1.1932 | 516  | 0.7058          |
| 0.4924        | 1.3943 | 602  | 0.7018          |
| 0.4887        | 1.5954 | 688  | 0.6997          |
| 0.4696        | 1.7966 | 774  | 0.6998          |


### Framework versions

- Transformers 4.45.0.dev0
- Pytorch 2.4.0+cu121
- Datasets 2.20.0
- Tokenizers 0.19.1