sudoping01/bambara-llm-exp3

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<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/> <details><summary>See axolotl config</summary>

axolotl version: 0.12.2

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 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
• —trainbatchsize: 8
• —evalbatchsize: 8
• —seed: 42
• —distributed_type: multi-GPU
• —num_devices: 8
• —gradientaccumulationsteps: 2
• —totaltrainbatch_size: 128
• —totalevalbatch_size: 64
• —optimizer: Use adamw8bit with betas=(0.9,0.999) and epsilon=1e-08 and optimizerargs=No additional optimizer arguments
• —lrschedulertype: cosine
• —lrschedulerwarmup_steps: 633
• —training_steps: 21126

Training results

Framework versions

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