llama3.2-3b-instruct-tokenlab-cemigConvo

<!-- 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"/>

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

axolotl version: 0.13.0.dev0

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

chat_template: llama3

load_in_8bit: false
load_in_4bit: false

plugins:
  - axolotl.integrations.liger.LigerPlugin

liger_rope: true
liger_rms_norm: true
liger_glu_activation: true
liger_layer_norm: true
liger_fused_linear_cross_entropy: true

datasets:
  - path: ./outputs/dataset_tokenlab/train
    type: chat_template
    weight: 0.8
  - path: ./outputs/dataset_cemig/train
    type: chat_template
    weight: 0.2

validation_datasets:
  - path: ./outputs/dataset_tokenlab/validation
    type: chat_template
    weight: 0.8
  - path: ./outputs/dataset_cemig/validation
    type: chat_template
    weight: 0.2

test_datasets:
  - path: ./outputs/dataset_tokenlab/test
    type: chat_template
    weight: 0.8
  - path: ./outputs/dataset_cemig/test
    type: chat_template
    weight: 0.2

val_set_size: 0.0

dataset_prepared_path: ./outputs/dataset_prepared
output_dir: ./outputs/cemig-sft-2gpu/

sequence_len: 4096
sample_packing: true
pad_to_sequence_len: true
flash_attn: true

gradient_checkpointing: true
micro_batch_size: 4
gradient_accumulation_steps: 4

num_epochs: 2
optimizer: adamw_torch_fused
learning_rate: 1.0e-5
lr_scheduler: cosine
cosine_constant_lr_ratio: 0
cosine_min_lr_ratio: 0.1
warmup_ratio: 0.1
weight_decay: 0.0

bf16: true
tf32: true
save_only_model: true

logging_steps: 1
evals_per_epoch: 4
saves_per_epoch: 2

special_tokens:
  pad_token: <|finetune_right_pad_id|>

dataloader_num_workers: 4
dataloader_prefetch_factor: 2

wandb_project: llama32-3b-dados-cemig
wandb_entity: null
wandb_name: llama3.2-3b-tokenlab-more-cemig-data
wandb_log_model: checkpoint

</details><br>

<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="200" height="32"/>

outputs/cemig-sft-2gpu/

This model is a fine-tuned version of meta-llama/Llama-3.2-3B-Instruct on the None dataset. It achieves the following results on the evaluation set:

• Loss: 0.7199
• Memory/max Active (gib): 47.38
• Memory/max Allocated (gib): 47.38
• Memory/device Reserved (gib): 47.9

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: 1e-05
• train_batch_size: 4
• eval_batch_size: 4
• seed: 42
• distributed_type: multi-GPU
• num_devices: 2
• gradient_accumulation_steps: 4
• total_train_batch_size: 32
• total_eval_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: 3136
• training_steps: 31360

Training results

Framework versions

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