llama-3.2-3b-base-nemotron-3epochs

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

axolotl version: 0.13.0.dev0

base_model: meta-llama/Llama-3.2-3B
tokenizer_type: AutoTokenizer
trust_remote_code: true
strict: false

is_llama_derived_model: true

chat_template: chatml

plugins:
  - axolotl.integrations.liger.LigerPlugin

special_tokens:
  pad_token: "<|eot_id|>"

datasets:
  - path: nvidia/Llama-Nemotron-Post-Training-Dataset
    name: SFT           
    split: chat         
    type: chat_template
    field_messages: input            
    message_property_mappings:
      role: role
      content: content
    field_output: output

train_on_inputs: false

sequence_len: 8192
eval_sequence_len: 8192
pad_to_sequence_len: true
sample_packing: true
sample_packing_group_size: 100000
sample_packing_bin_size: 200
group_by_length: true

flash_attn: true

micro_batch_size: 1               
gradient_accumulation_steps: 8    
num_epochs: 3

learning_rate: 2.0e-5
optimizer: adamw_torch_fused
adam_beta1: 0.9
adam_beta2: 0.999
adam_epsilon: 1.0e-8

lr_scheduler: cosine
warmup_steps: 100
weight_decay: 0.0   

bf16: true          
tf32: true
gradient_checkpointing: true
activation_offloading: false

val_set_size: 0.01          
eval_strategy: steps
eval_steps: 100

save_strategy: steps
save_steps: 100
save_total_limit: 3
save_only_model: false
save_safetensors: true
load_best_model_at_end: true
metric_for_best_model: eval_loss
greater_is_better: false

logging_steps: 10

output_dir: ./outputs/Llama-3.2-3B-base-nemotron-3epochs/
seed: 42

use_wandb: true
wandb_project: "llama31_base_nemotron"
wandb_name: "llama31-8b-base-nemotron"

</details><br>

outputs/Llama-3.2-3B-base-nemotron-3epochs/

This model is a fine-tuned version of meta-llama/Llama-3.2-3B on the nvidia/Llama-Nemotron-Post-Training-Dataset dataset. It achieves the following results on the evaluation set:

• Loss: 1.1378
• Memory/max Active (gib): 30.79
• Memory/max Allocated (gib): 30.79
• Memory/device Reserved (gib): 45.32

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: 2e-05
• train_batch_size: 1
• eval_batch_size: 1
• seed: 42
• gradient_accumulation_steps: 8
• total_train_batch_size: 8
• optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED 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: 100
• training_steps: 855

Training results

Framework versions

• Transformers 4.57.1
• Pytorch 2.9.0+cu130
• Datasets 4.3.0
• Tokenizers 0.22.1