1.7B-SFT

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

axolotl version: 0.11.0

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

</details><br>

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

Framework versions

• Transformers 4.53.1
• Pytorch 2.7.1+cu126
• Datasets 3.6.0
• Tokenizers 0.21.2