---
library_name: transformers
license: mit
base_model: fxmarty/tiny-random-GemmaForCausalLM
tags:
- axolotl
- generated_from_trainer
model-index:
- name: fd1980a0-7e71-4e52-addb-318dca5991d5
  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: fxmarty/tiny-random-GemmaForCausalLM
batch_size: 32
bf16: true
chat_template: tokenizer_default_fallback_alpaca
datasets:
- data_files:
  - b7c2a4a781c93416_train_data.json
  ds_type: json
  format: custom
  path: /workspace/input_data/b7c2a4a781c93416_train_data.json
  type:
    field_input: context
    field_instruction: question
    field_output: answer
    format: '{instruction} {input}'
    no_input_format: '{instruction}'
    system_format: '{system}'
    system_prompt: ''
eval_steps: 20
flash_attention: true
gpu_memory_limit: 80GiB
gradient_checkpointing: true
group_by_length: true
hub_model_id: willtensora/fd1980a0-7e71-4e52-addb-318dca5991d5
hub_strategy: checkpoint
learning_rate: 0.0002
logging_steps: 10
lr_scheduler: cosine
max_steps: 2500
micro_batch_size: 4
model_type: AutoModelForCausalLM
optimizer: adamw_bnb_8bit
output_dir: /workspace/axolotl/configs
pad_to_sequence_len: true
resize_token_embeddings_to_32x: false
sample_packing: false
save_steps: 40
save_total_limit: 1
sequence_len: 2048
tokenizer_type: GemmaTokenizerFast
train_on_inputs: false
trust_remote_code: true
val_set_size: 0.1
wandb_entity: ''
wandb_mode: online
wandb_name: fxmarty/tiny-random-GemmaForCausalLM-/workspace/input_data/b7c2a4a781c93416_train_data.json
wandb_project: Gradients-On-Demand
wandb_run: your_name
wandb_runid: default
warmup_ratio: 0.05
xformers_attention: true

```

</details><br>

# fd1980a0-7e71-4e52-addb-318dca5991d5

This model is a fine-tuned version of [fxmarty/tiny-random-GemmaForCausalLM](https://huggingface.co/fxmarty/tiny-random-GemmaForCausalLM) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 11.7971

## 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: 4
- eval_batch_size: 4
- seed: 42
- distributed_type: multi-GPU
- num_devices: 8
- total_train_batch_size: 32
- total_eval_batch_size: 32
- 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: 7
- training_steps: 156

### Training results

| Training Loss | Epoch  | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| No log        | 0.0008 | 1    | 12.4537         |
| 12.4357       | 0.0161 | 20   | 12.4267         |
| 12.392        | 0.0322 | 40   | 12.3762         |
| 12.3026       | 0.0483 | 60   | 12.2651         |
| 12.1177       | 0.0645 | 80   | 12.0658         |
| 11.9286       | 0.0806 | 100  | 11.8860         |
| 11.8324       | 0.0967 | 120  | 11.8100         |
| 11.798        | 0.1128 | 140  | 11.7971         |


### Framework versions

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