gemma3-1b-en-odia-mt

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<details><summary>See axolotl config</summary>

axolotl version: 0.13.0.dev0

base_model: google/gemma-3-1b-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: 0.0
output_dir: ./outputs/gemma3-1b

  #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: gemma3-1b
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/gemma3-1b

This model is a fine-tuned version of google/gemma-3-1b-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
• train_batch_size: 1
• eval_batch_size: 1
• seed: 42
• distributed_type: multi-GPU
• num_devices: 8
• gradient_accumulation_steps: 4
• total_train_batch_size: 32
• total_eval_batch_size: 8
• 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: 603
• training_steps: 6036

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("sam2ai/gemma3-1b-en-odia-mt")
model = AutoModelForCausalLM.from_pretrained("sam2ai/gemma3-1b-en-odia-mt")
messages = [
  {
    "role": "system",
    "content": "You are a helpful assistant who is an expert in linguist specializing in English and Odia."
  },
  {
    "role": "user",
    "content": "Translate English to Odia : A medical officer has been assigned to each ward."
  }
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
	eos_token_id=tokenizer.eos_token_id
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Training results

Framework versions

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