pointwise-rerank-qwen3-600m-ds0-pd74hs73h

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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/pointwise-rerank-qwen3-600m-ds0-pd74hs73h

load_in_8bit: false
load_in_4bit: false
strict: false

chat_template: qwen3
datasets:
  - path: kaggle-map/pointwise-reranker
    type: chat_template
    split: train

test_datasets:
  - path: kaggle-map/pointwise-reranker
    type: chat_template
    split: val

dataset_processes: 32
dataset_prepared_path: last_run_prepared
output_dir: ./outputs/pointwise-rerank-qwen3-600m-ds0-pd74hs73h

sequence_len: 1024
sample_packing: true
eval_sample_packing: false

deepspeed: deepspeed_configs/zero1.json

wandb_project: map-math-misconceptions
wandb_entity:
wandb_watch:
wandb_name: pointwise-rerank-qwen3-600m-ds0-pd74hs73h
wandb_log_model:

gradient_accumulation_steps: 8
micro_batch_size: 32
num_epochs: 5
optimizer: adamw_torch_fused
lr_scheduler: cosine
learning_rate: 1e-5

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: 3
saves_per_epoch: 3
weight_decay: 0.01

save_first_step: true  # uncomment this to validate checkpoint saving works with your config

</details><br>

pointwise-rerank-qwen3-600m-ds0-pd74hs73h

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

• Loss: 0.1314
• Memory/max Mem Active(gib): 66.58
• Memory/max Mem Allocated(gib): 66.58
• Memory/device Mem Reserved(gib): 76.3

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: 32
• eval_batch_size: 32
• seed: 42
• distributed_type: multi-GPU
• num_devices: 2
• 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: 90
• training_steps: 905

Training results

Framework versions

• Transformers 4.55.2
• Pytorch 2.6.0+cu124
• Datasets 4.0.0
• Tokenizers 0.21.4