winglian / qwen3-4b-math-kd-jsd-temp1-v4

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Total runs: 9
24-hour runs: 0
7-day runs: 5
30-day runs: 4
Model's Last Updated: June 03 2025
text-generation

Introduction of qwen3-4b-math-kd-jsd-temp1-v4

Model Details of qwen3-4b-math-kd-jsd-temp1-v4

Built with Axolotl

See axolotl config

axolotl version: 0.10.0.dev0

base_model: winglian/qwen3-4b-math

plugins:
  - axolotl.integrations.kd.KDPlugin
  - axolotl.integrations.liger.LigerPlugin

liger_rms_norm: true
liger_glu_activation: true

  # torch_compile: true

strict: false

kd_trainer: true
kd_ce_alpha: 0.1
kd_alpha: 1.0
kd_temperature: 1.0
kd_beta: 0.5
kd_normalize_topk: false

dataloader_prefetch_factor: 1
dataloader_num_workers: 2
dataloader_pin_memory: true

gc_steps: -1  # gc at the end of each epoch

chat_template: qwen3
datasets:
  - path: winglian/OpenThoughts-114k-math-correct-qwen3-14b-math-prepared-temp1
    type: chat_template
    split: train
    split_thinking: true
    eot_tokens:
      - "<|im_end|>"

skip_prepare_dataset: true
dataset_prepared_path: last_run_prepared
val_set_size: 0.0
output_dir: ./outputs/out-kd-4b-offline-t1-v4

sequence_len: 8192
sample_packing: true
pad_to_sequence_len: true

wandb_project: kd-4b-math
wandb_entity: axolotl-ai
wandb_watch:
wandb_name:
wandb_log_model:

gradient_accumulation_steps: 1
micro_batch_size: 4
num_epochs: 2
optimizer: adamw_torch_fused
adam_beta2: 0.999
lr_scheduler: rex
learning_rate: 3e-5
max_grad_norm: 0.2
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: 2
debug:
weight_decay: 0.0
special_tokens:
  eos_token: <|im_end|>
deepspeed: deepspeed_configs/zero2_torch_compile.json


outputs/out-kd-4b-offline-t1-v4

This model is a fine-tuned version of winglian/qwen3-4b-math on the winglian/OpenThoughts-114k-math-correct-qwen3-14b-math-prepared-temp1 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_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: 2.0
Training results
Framework versions
  • Transformers 4.51.3
  • Pytorch 2.7.0+cu128
  • Datasets 3.5.1
  • Tokenizers 0.21.1

Runs of winglian qwen3-4b-math-kd-jsd-temp1-v4 on huggingface.co

9
Total runs
0
24-hour runs
5
3-day runs
5
7-day runs
4
30-day runs

More Information About qwen3-4b-math-kd-jsd-temp1-v4 huggingface.co Model

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qwen3-4b-math-kd-jsd-temp1-v4 huggingface.co

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qwen3-4b-math-kd-jsd-temp1-v4 huggingface.co Url

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winglian qwen3-4b-math-kd-jsd-temp1-v4 online free

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qwen3-4b-math-kd-jsd-temp1-v4 install

qwen3-4b-math-kd-jsd-temp1-v4 is an open source model from GitHub that offers a free installation service, and any user can find qwen3-4b-math-kd-jsd-temp1-v4 on GitHub to install. At the same time, huggingface.co provides the effect of qwen3-4b-math-kd-jsd-temp1-v4 install, users can directly use qwen3-4b-math-kd-jsd-temp1-v4 installed effect in huggingface.co for debugging and trial. It also supports api for free installation.

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