listwise-rerank-qwen3-600m-ds1-9fh4jd8e6

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

axolotl version: 0.12.2

base_model: Qwen/Qwen3-0.6B

# Automatically upload checkpoint and final model to HF
hub_model_id: abdullahmeda/listwise-rerank-qwen3-600m-ds1-9fh4jd8e6

load_in_8bit: false
load_in_4bit: false
strict: false

chat_template: qwen3
datasets:
  - path: kaggle-map/listwise-rerank
    type: chat_template
    split: train

test_datasets:
  - path: kaggle-map/listwise-rerank
    type: chat_template
    split: val

streaming: true

dataset_processes: 32
dataset_prepared_path: last_run_prepared
output_dir: ./outputs/listwise-rerank-qwen3-600m-ds1-9fh4jd8e6

sequence_len: 1280
sample_packing: true
eval_sample_packing: false

deepspeed: deepspeed_configs/zero1.json

wandb_project: map-math-misconceptions
wandb_entity:
wandb_watch:
wandb_name: listwise-rerank-qwen3-600m-ds1-9fh4jd8e6
wandb_log_model:

gradient_accumulation_steps: 8
micro_batch_size: 16
num_epochs: 1
optimizer: adamw_torch_fused
lr_scheduler: cosine
learning_rate: 5e-6

bf16: true
tf32: true

gradient_checkpointing: true
gradient_checkpointing_kwargs:
  use_reentrant: false
resume_from_checkpoint:
logging_steps: 10
flash_attention: true

warmup_ratio: 0.1
evals_per_epoch: 21
saves_per_epoch: 21
weight_decay: 0.01

save_first_step: true

</details><br>

listwise-rerank-qwen3-600m-ds1-9fh4jd8e6

This model is a fine-tuned version of Qwen/Qwen3-0.6B on the kaggle-map/listwise-rerank dataset. It achieves the following results on the evaluation set:

• Loss: 0.1884
• Memory/max Mem Active(gib): 43.13
• Memory/max Mem Allocated(gib): 43.13
• Memory/device Mem Reserved(gib): 50.69

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: 5e-06
• train_batch_size: 16
• eval_batch_size: 16
• seed: 42
• distributed_type: multi-GPU
• num_devices: 4
• gradient_accumulation_steps: 8
• total_train_batch_size: 512
• total_eval_batch_size: 64
• optimizer: Use 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: 24
• training_steps: 248

Training results

Framework versions

• Transformers 4.55.2
• Pytorch 2.6.0+cu126
• Datasets 4.0.0
• Tokenizers 0.21.4