base_model: Qwen/Qwen3-1.7B

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

# plugins:
#   - axolotl.integrations.liger.LigerPlugin

# liger_rope: true
# liger_rms_norm: true
# liger_glu_activation: true
# liger_layer_norm: true
# liger_fused_linear_cross_entropy: true

datasets:
  - path: sumuks/essential-web-v1.0-sample-100M-with-cleaned-responses-sft
    type: chat_template
    field_messages: conversations
    split: train
val_set_size: 0.05
dataset_prepared_path: dataset/prepared_dataset_1.7b

train_on_inputs: false
output_dir: ./output/1.7B-Instruct-Tuned-New-Data
chat_template: qwen3
sequence_len: 8192
sample_packing: true
eval_sample_packing: true
# pad_to_sequence_len: true

wandb_project: essential-web-sft
wandb_name: qwen3-1.7b-sft-new-data

gradient_accumulation_steps: 4
gradient_checkpointing: true
gradient_checkpointing_kwargs:
  use_reentrant: false
flash_attention: true
micro_batch_size: 1
optimizer: paged_adamw_8bit
lr_scheduler: cosine
learning_rate: 2e-5
num_epochs: 1

load_best_model_at_end: true
metric_for_best_model: loss
greater_is_better: false

early_stopping_patience: 3
bf16: auto
tf32: true

logging_steps: 5

deepspeed: ./configs_prod/zero3.json

save_steps: 500
eval_steps: 500

warmup_ratio: 0.05
# save_first_step: true

output/1.7B-Instruct-Tuned-New-Data

This model is a fine-tuned version of Qwen/Qwen3-1.7B on the sumuks/essential-web-v1.0-sample-100M-with-cleaned-responses-sft dataset. It achieves the following results on the evaluation set:

Loss: 0.3669

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: 2e-05
train_batch_size: 1
eval_batch_size: 1
seed: 42
distributed_type: multi-GPU
num_devices: 2
gradient_accumulation_steps: 4
total_train_batch_size: 8
total_eval_batch_size: 2
optimizer: Use OptimizerNames.PAGED_ADAMW_8BIT 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: 164
training_steps: 3297

Training results

Training Loss	Epoch	Step	Validation Loss
No log	0	0	0.8829
0.3689	0.1517	500	0.4088
0.3919	0.3033	1000	0.3952
0.386	0.4550	1500	0.3839
0.409	0.6066	2000	0.3755
0.3473	0.7583	2500	0.3694
0.3518	0.9099	3000	0.3669

Framework versions

Transformers 4.53.1
Pytorch 2.7.1+cu126
Datasets 3.6.0
Tokenizers 0.21.2

textcleanlm/1.7B-SFT

output/1.7B-Instruct-Tuned-New-Data

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

textcleanlm/
1.7B-SFT