---
library_name: peft
license: gemma
base_model: google/gemma-3n-E2B-it
tags:
- axolotl
- base_model:adapter:google/gemma-3n-E2B-it
- lora
- transformers
datasets:
- sudoping01/bambara-instructions
pipeline_tag: text-generation
model-index:
- name: bambara-llm-exp3
  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: `0.12.2`
```yaml
base_model: google/gemma-3n-E2B-it
hub_model_id: sudoping01/bambara-llm-exp3 
plugins:
  - axolotl.integrations.cut_cross_entropy.CutCrossEntropyPlugin
cut_cross_entropy: true
load_in_4bit: false  
gradient_checkpointing: true
gradient_checkpointing_kwargs:
  use_reentrant: false
ddp: true
chat_template: gemma3n
eot_tokens:
  - <end_of_turn>
special_tokens:
  eot_token: <end_of_turn>
datasets:
  - path: sudoping01/bambara-instructions
    type: chat_template
    split: train
    name: cleaned
    field_messages: messages
    message_property_mappings:
      role: role
      content: content
val_set_size: 0.01
output_dir: ./outputs/bambara-gemma3n-lora-exp4
adapter: lora  
lora_r: 64     
lora_alpha: 128 
lora_dropout: 0.05
lora_target_modules: 'model.language_model.layers.[\d]+.(mlp|self_attn).(up|down|gate|q|k|v|o)_proj'
sequence_len: 4096 
sample_packing: false
pad_to_sequence_len: false
micro_batch_size: 8  
gradient_accumulation_steps: 2
num_epochs: 3  
optimizer: adamw_8bit
lr_scheduler: cosine
learning_rate: 1.2e-4  
warmup_ratio: 0.03
weight_decay: 0.01
bf16: auto
tf32: false
logging_steps: 10
saves_per_epoch: 2  
evals_per_epoch: 2
```

</details><br>

# bambara-llm-exp3

This model is a fine-tuned version of [google/gemma-3n-E2B-it](https://huggingface.co/google/gemma-3n-E2B-it) on the sudoping01/bambara-instructions dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4952
- Memory/max Mem Active(gib): 57.85
- Memory/max Mem Allocated(gib): 57.85
- Memory/device Mem Reserved(gib): 59.82

## 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: 0.00012
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- distributed_type: multi-GPU
- num_devices: 8
- gradient_accumulation_steps: 2
- total_train_batch_size: 128
- total_eval_batch_size: 64
- optimizer: Use adamw_8bit 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: 633
- training_steps: 21126

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Mem Active(gib) | Mem Allocated(gib) | Mem Reserved(gib) |
|:-------------:|:-----:|:-----:|:---------------:|:---------------:|:------------------:|:-----------------:|
| No log        | 0     | 0     | 7.4595          | 19.86           | 19.86              | 20.35             |
| 0.8265        | 0.5   | 3521  | 0.7787          | 57.85           | 57.85              | 59.82             |
| 0.7107        | 1.0   | 7042  | 0.6745          | 57.85           | 57.85              | 59.82             |
| 0.6363        | 1.5   | 10563 | 0.6026          | 57.85           | 57.85              | 59.82             |
| 0.5421        | 2.0   | 14084 | 0.5429          | 57.85           | 57.85              | 59.82             |
| 0.5733        | 2.5   | 17605 | 0.5039          | 57.85           | 57.85              | 59.82             |
| 0.5401        | 3.0   | 21126 | 0.4952          | 57.85           | 57.85              | 59.82             |


### Framework versions

- PEFT 0.17.0
- Transformers 4.55.2
- Pytorch 2.6.0+cu124
- Datasets 4.0.0
- Tokenizers 0.21.4