winglian/qwen3-14b-triton-v1

See axolotl config

axolotl version: 0.10.0.dev0

base_model: Qwen/Qwen3-14B-Base

plugins:
  - axolotl.integrations.liger.LigerPlugin
  - axolotl.integrations.cut_cross_entropy.CutCrossEntropyPlugin
liger_rope: true
liger_rms_norm: true
liger_glu_activation: true

chat_template: qwen3 
datasets:
  - path: axolotl-ai-internal/gpumode-py2triton-reasoning-v2
    type: chat_template
    split: train
    split_thinking: true
    eot_tokens: ["<|im_end|>"]

dataset_prepared_path: last_run_prepared
val_set_size: 0.005
output_dir: ./outputs/out
save_only_model: true

sequence_len: 16384
sample_packing: true
pad_to_sequence_len: true

wandb_project: qwen3-14b-grpo-triton
wandb_entity: axolotl-ai
wandb_watch:
wandb_name:
wandb_log_model:

gradient_accumulation_steps: 1
micro_batch_size: 2
num_epochs: 3
optimizer: adamw_torch_fused
max_grad_norm: 0.1
neftune_noise_alpha: 10
lr_scheduler: cosine
learning_rate: 3e-6

bf16: true
tf32: true

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

warmup_steps: 100
evals_per_epoch: 5
saves_per_epoch: 1
weight_decay: 0.01
deepspeed: deepspeed_configs/zero1.json

outputs/out

This model is a fine-tuned version of Qwen/Qwen3-14B-Base on the axolotl-ai-internal/gpumode-py2triton-reasoning-v2 dataset. It achieves the following results on the evaluation set:

• Loss: 0.2053

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-06
• train_batch_size: 2
• eval_batch_size: 2
• seed: 42
• distributed_type: multi-GPU
• num_devices: 8
• total_train_batch_size: 16
• total_eval_batch_size: 16
• 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
• num_epochs: 3.0

Training results

Training Loss	Epoch	Step	Validation Loss
0.4288	0.0039	1	0.5326
0.289	0.2	51	0.3414
0.2091	0.4	102	0.2622
0.2009	0.6	153	0.2362
0.1848	0.8	204	0.2248
0.1654	1.0	255	0.2186
0.1803	1.2	306	0.2165
0.1642	1.4	357	0.2116
0.1714	1.6	408	0.2094
0.164	1.8	459	0.2074
0.1488	2.0	510	0.2069
0.1676	2.2	561	0.2069
0.153	2.4	612	0.2059
0.1621	2.6	663	0.2056
0.1568	2.8	714	0.2055
0.1433	3.0	765	0.2053

Framework versions

• Transformers 4.51.3
• Pytorch 2.6.0+cu124
• Datasets 3.5.1
• Tokenizers 0.21.1