See axolotl config

axolotl version: 0.10.0.dev0

base_model: Qwen/Qwen3-4B-Base

plugins:
  - axolotl.integrations.liger.LigerPlugin
  - axolotl.integrations.cut_cross_entropy.CutCrossEntropyPlugin

liger_rms_norm: true
liger_glu_activation: true

# torch_compile: true

dataloader_prefetch_factor: 4
dataloader_num_workers: 2
dataloader_pin_memory: true

chat_template: qwen3
datasets:
  - path: winglian/OpenThoughts-114k-math-correct
    type: chat_template
    split: train
    split_thinking: true
    eot_tokens:
      - "<|im_end|>"

dataset_prepared_path: last_run_prepared
val_set_size: 0.01
output_dir: ./outputs/model-out-math-4b

sequence_len: 8192
sample_packing: true
pad_to_sequence_len: true

wandb_project: kd-4b-math
wandb_entity: axolotl-ai
wandb_name: sft-4b

gradient_accumulation_steps: 1
micro_batch_size: 4
num_epochs: 2
optimizer: adamw_torch_fused
adam_beta2: 0.95
lr_scheduler: rex
learning_rate: 3e-5
max_grad_norm: 0.1
save_safetensors: true

bf16: true
tf32: true

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

warmup_steps: 100
evals_per_epoch: 4
saves_per_epoch: 1
weight_decay: 0.1
special_tokens:
  eos_token: <|im_end|>
deepspeed: deepspeed_configs/zero2_torch_compile.json

outputs/model-out-math-4b

This model is a fine-tuned version of Qwen/Qwen3-4B-Base on the winglian/OpenThoughts-114k-math-correct dataset. It achieves the following results on the evaluation set:

Loss: 0.3929

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_TORCH_FUSED with betas=(0.9,0.95) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
lr_scheduler_type: cosine
lr_scheduler_warmup_steps: 100
num_epochs: 2.0

Training results

Training Loss	Epoch	Step	Validation Loss
0.5644	0.0016	1	0.5801
0.4038	0.2504	159	0.4154
0.3914	0.5008	318	0.4035
0.3812	0.7512	477	0.3960
0.3626	1.0016	636	0.3915
0.316	1.2520	795	0.3958
0.3171	1.5024	954	0.3963
0.2944	1.7528	1113	0.3929