Modelpublic

sam2ai/gemma3-4b-en-odia-mt

sourceHugging Facegemmaupdated 1y agoView on Hugging Face

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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: 0.13.0.dev0

yaml

base_model: google/gemma-3-4b-it
# optionally might have model_type or tokenizer_type
model_type: AutoModelForCausalLM
tokenizer_type: AutoTokenizer
# Automatically upload checkpoint and final model to HF
# hub_model_id: username/custom_model_name

# gemma3 doesn't seem to play nice with ddp
ddp_find_unused_parameters: true

load_in_8bit: false
load_in_4bit: false

# huggingface repo
chat_template: gemma3
eot_tokens:
  - <end_of_turn>
datasets:
  - path: sam2ai/en-oriya-translation
    type: chat_template
    field_messages: conversations
    message_property_mappings:
      role: from
      content: value
    roles:
      assistant:
        - gpt
      user:
        - human

val_set_size: 0.1
output_dir: ./outputs/gemma-3-4b-it

  #adapter: qlora
  #lora_r: 32
  #lora_alpha: 16
  #lora_dropout: 0.05
  #lora_target_linear: true

sequence_len: 2048
sample_packing: true
eval_sample_packing: false


wandb_project: gemma3-en-odia-mt
wandb_entity:
wandb_watch:
wandb_name: gemma3-4b-it
wandb_log_model:


gradient_accumulation_steps: 4
micro_batch_size: 1
num_epochs: 4
optimizer: adamw_bnb_8bit
lr_scheduler: cosine
learning_rate: 0.0002

bf16: auto
tf32: false

gradient_checkpointing: true
gradient_checkpointing_kwargs:
  use_reentrant: false
resume_from_checkpoint:
logging_steps: 1
flash_attention: true

warmup_ratio: 0.1
evals_per_epoch:
saves_per_epoch: 1
weight_decay: 0.0
special_tokens:

# save_first_step: true  # uncomment this to validate checkpoint saving works with your config

</details><br>

outputs/gemma-3-4b-it

This model is a fine-tuned version of google/gemma-3-4b-it on the sam2ai/en-oriya-translation dataset.

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
—trainbatchsize: 1
—evalbatchsize: 1
—seed: 42
—distributed_type: multi-GPU
—num_devices: 8
—gradientaccumulationsteps: 4
—totaltrainbatch_size: 32
—totalevalbatch_size: 8
—optimizer: Use OptimizerNames.ADAMWBNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizerargs=No additional optimizer arguments
—lrschedulertype: cosine
—lrschedulerwarmup_steps: 543
—training_steps: 5432

Training results

Framework versions

—Transformers 4.55.4
—Pytorch 2.7.0+gitf717b2a
—Datasets 4.0.0
—Tokenizers 0.21.1