Built with Axolotl

See axolotl config

axolotl version: 0.9.2

base_model: Qwen/Qwen3-0.6B-Base
# Automatically upload checkpoint and final model to HF
# hub_model_id: username/custom_model_name

plugins:
  - axolotl.integrations.cut_cross_entropy.CutCrossEntropyPlugin
strict: false

chat_template: qwen3
datasets:
  - path: timarni/reasoning_SFT
    type: chat_template
    split: train
    field_messages: conversations
    # message_property_mappings:
    #   role: from
    #   content: value

val_set_size: 0.1

output_dir: ./outputs/qwen3_reasoning_sft
dataset_prepared_path: last_run_prepared

# To be sure that no LORA is done
adapter: null
lora: false
merge_lora: false

sequence_len: 4096 #2048
sample_packing: true
eval_sample_packing: true
pad_to_sequence_len: true

wandb_project: mnlp_project
wandb_entity: tim-arni
wandb_watch:
wandb_name: qwen3_reasoning_sft
wandb_log_model:

gradient_accumulation_steps: 2 # 16 following https://unsloth.ai/blog/qwen3
micro_batch_size: 1 # 2
num_epochs: 6
optimizer: adamw_torch
lr_scheduler: cosine
learning_rate: 0.00005 # 0.0002
cosine_min_lr_ratio: 0.1

bf16: auto
tf32: true

gradient_checkpointing: offload
logging_steps: 1
gradient_clipping: 1.0
flash_attention: true

warmup_ratio: 0.03
evals_per_epoch: 4
saves_per_epoch: 2
save_total_limit: 25
weight_decay: 1e-4
special_tokens:

outputs/qwen3_reasoning_sft

This model is a fine-tuned version of Qwen/Qwen3-0.6B-Base on the timarni/reasoning_SFT dataset. It achieves the following results on the evaluation set:

  • Loss: 0.8020

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-05
  • train_batch_size: 1
  • eval_batch_size: 1
  • seed: 42
  • distributed_type: multi-GPU
  • num_devices: 4
  • gradient_accumulation_steps: 2
  • total_train_batch_size: 8
  • total_eval_batch_size: 4
  • optimizer: Use adamw_torch 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: 47
  • num_epochs: 6.0

Training results

Training Loss Epoch Step Validation Loss
0.965 0.0037 1 0.8999
0.8101 0.2505 67 0.7453
0.6077 0.5009 134 0.7342
0.5874 0.7514 201 0.7270
0.4362 1.0 268 0.7260
0.6779 1.2505 335 0.7269
0.505 1.5009 402 0.7310
0.4969 1.7514 469 0.7274
0.309 2.0 536 0.7332
0.5954 2.2505 603 0.7428
0.4302 2.5009 670 0.7514
0.4301 2.7514 737 0.7491
0.23 3.0 804 0.7559
0.5296 3.2505 871 0.7683
0.3761 3.5009 938 0.7857
0.3916 3.7514 1005 0.7818
0.1842 4.0 1072 0.7863
0.4926 4.2505 1139 0.7980
0.3469 4.5009 1206 0.8004
0.3697 4.7514 1273 0.7908
0.1665 5.0 1340 0.7925
0.4773 5.2505 1407 0.8187
0.3364 5.5009 1474 0.8071
0.3622 5.7514 1541 0.8020

Framework versions

  • Transformers 4.51.3
  • Pytorch 2.5.1+cu121
  • Datasets 3.5.1
  • Tokenizers 0.21.1
Downloads last month
4
Safetensors
Model size
596M params
Tensor type
BF16
·
Inference Providers NEW
This model isn't deployed by any Inference Provider. 🙋 Ask for provider support

Model tree for timarni/qwen3_reasoning_sft

Finetuned
(290)
this model

Dataset used to train timarni/qwen3_reasoning_sft