See axolotl config
axolotl version: 0.6.0
base_model: meta-llama/Llama-3.2-1B
# Automatically upload checkpoint and final model to HF
hub_model_id: axolotl-ai-co/numina-3b-ep3-lr3e-5-sft
plugins:
# - axolotl.integrations.kd.KDPlugin
- axolotl.integrations.liger.LigerPlugin
liger_rms_norm: true
liger_glu_activation: true
torch_compile: true
strict: false
chat_template: llama3
dataloader_prefetch_factor: 512
dataloader_num_workers: 8
dataloader_pin_memory: true
datasets:
- field_messages: messages
message_field_content: content
message_field_role: role
path: AI-MO/NuminaMath-CoT
type: chat_template
split: train
dataset_prepared_path: last_run_prepared
val_set_size: 0.0
output_dir: ./outputs/out
sequence_len: 8192
sample_packing: true
pad_to_sequence_len: true
wandb_project: numina-kd-experiment
wandb_entity: axolotl-ai
wandb_watch:
wandb_name:
wandb_log_model:
gradient_accumulation_steps: 1
micro_batch_size: 4
num_epochs: 3
optimizer: adamw_8bit
lr_scheduler: cosine
learning_rate: 3e-5
save_safetensors: true
train_on_inputs: false
group_by_length: false
bf16: auto
fp16:
tf32: true
gradient_checkpointing: true
gradient_checkpointing_kwargs:
use_reentrant: false
early_stopping_patience:
resume_from_checkpoint:
logging_steps: 1
xformers_attention:
flash_attention: true
warmup_steps: 100
evals_per_epoch: 2
eval_table_size:
saves_per_epoch: 1
debug:
# deepspeed: deepspeed_configs/zero1.json
weight_decay: 0.0
_fsdp:
- full_shard
- auto_wrap
_fsdp_config:
fsdp_limit_all_gathers: true
fsdp_sync_module_states: true
fsdp_offload_params: true
fsdp_use_orig_params: false
fsdp_cpu_ram_efficient_loading: true
fsdp_auto_wrap_policy: TRANSFORMER_BASED_WRAP
fsdp_transformer_layer_cls_to_wrap: LlamaDecoderLayer
fsdp_state_dict_type: FULL_STATE_DICT
fsdp_sharding_strategy: FULL_SHARD
fsdp_backward_prefetch: BACKWARD_PRE
special_tokens:
pad_token: <|finetune_right_pad_id|>
eos_token: <|eot_id|>
numina-3b-ep3-lr3e-5-sft
This model is a fine-tuned version of meta-llama/Llama-3.2-1B on the AI-MO/NuminaMath-CoT 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: 3e-05
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- distributed_type: multi-GPU
- num_devices: 8
- total_train_batch_size: 32
- total_eval_batch_size: 32
- optimizer: Use OptimizerNames.ADAMW_8BIT 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
- num_epochs: 3
Training results
Framework versions
- Transformers 4.47.1
- Pytorch 2.5.1+cu124
- Datasets 3.2.0
- Tokenizers 0.21.0
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Model tree for axolotl-ai-co/numina-1b-ep3-lr3e-5-sft
Base model
meta-llama/Llama-3.2-1B