See axolotl config

axolotl version: 0.4.1

base_model: Qwen/Qwen2.5-7B-Instruct

strict: false

plugins:
  - axolotl.integrations.liger.LigerPlugin
liger_rope: true
liger_rms_norm: true
liger_swiglu: true
liger_fused_linear_cross_entropy: true

chat_template: chatml
datasets:
  - path: AlekseyKorshuk/ai-detection-gutenberg-human-choosed-formatted-ai-sft
    type: chat_template
    field_messages: messages
    message_field_role: role
    message_field_content: content
    roles:
      user:
        - user
      assistant:
        - assistant

val_set_size: 0.05
output_dir: ./outputs/out

eval_table_size: 0
eval_max_new_tokens: 1

sequence_len: 16384
sample_packing: false
eval_sample_packing: false
pad_to_sequence_len: false

wandb_project: ai-seo-rewriter
wandb_entity:
wandb_watch:
wandb_name: ai-detection-gutenberg-human-choosed-formatted-ai-v1-sft-qwen-7b
wandb_log_model:

gradient_accumulation_steps: 2
micro_batch_size: 16
eval_batch_size: 16
num_epochs: 1
optimizer: adamw_torch
# adam_beta1: 0.9
# adam_beta2: 0.95
max_grad_norm: 1.0
# adam_epsilon: 0.00001
lr_scheduler: cosine
learning_rate: 1e-5

train_on_inputs: false
group_by_length: false
bf16: true
fp16: false
tf32: true

gradient_checkpointing: true
gradient_checkpointing_kwargs:
  use_reentrant: false
early_stopping_patience:
resume_from_checkpoint:
logging_steps: 1
xformers_attention:
flash_attention: true

warmup_ratio: 0.1
evals_per_epoch: 10
saves_per_epoch: 1
debug:
deepspeed:
weight_decay: 0.0
fsdp:
  - full_shard
  - auto_wrap
fsdp_config:
  fsdp_limit_all_gathers: true
  fsdp_sync_module_states: true
  fsdp_offload_params: true
  fsdp_use_orig_params: false
  fsdp_cpu_ram_efficient_loading: true
  fsdp_auto_wrap_policy: TRANSFORMER_BASED_WRAP
  fsdp_transformer_layer_cls_to_wrap: Qwen2DecoderLayer
  fsdp_state_dict_type: FULL_STATE_DICT



hub_model_id: AlekseyKorshuk/ai-detection-gutenberg-human-choosed-formatted-ai-sft-qwen-7b

ai-detection-gutenberg-human-choosed-formatted-ai-sft-qwen-7b

This model is a fine-tuned version of Qwen/Qwen2.5-7B-Instruct on the None dataset. It achieves the following results on the evaluation set:

• Loss: 0.8393

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: 16
• eval_batch_size: 16
• seed: 42
• distributed_type: multi-GPU
• num_devices: 8
• gradient_accumulation_steps: 2
• total_train_batch_size: 256
• total_eval_batch_size: 128
• 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: 3
• num_epochs: 1

Training results

Framework versions

• Transformers 4.46.0
• Pytorch 2.4.1+cu124
• Datasets 3.0.1
• Tokenizers 0.20.1