See axolotl config

axolotl version: 0.10.0.dev0

base_model: GreenerPastures/Basically-Human-4B

load_in_8bit: false
load_in_4bit: false
strict: false

datasets:
  - path: jeiku/Writing
    type: completion
    field: text
  - path: ResplendentAI/Sissification_Hypno_1k
    type: alpaca
  - path: ResplendentAI/Synthetic_Soul_1k
    type: alpaca

chat_template: qwen3

val_set_size: 0
output_dir: ./outputs/out
dataset_prepared_path: last_run_prepared
shuffle_merged_datasets: true

hub_model_id: hardlyworking/AGI
hub_strategy: "all_checkpoints"
push_dataset_to_hub:
hf_use_auth_token: true

plugins:
  - axolotl.integrations.liger.LigerPlugin
  - axolotl.integrations.cut_cross_entropy.CutCrossEntropyPlugin
liger_rope: true
liger_rms_norm: true
liger_layer_norm: true
liger_glu_activation: true
liger_fused_linear_cross_entropy: false
cut_cross_entropy: true

sequence_len: 8192
sample_packing: true
eval_sample_packing: true
pad_to_sequence_len: true

wandb_project: Qwen4B
wandb_entity:
wandb_watch:
wandb_name: Qwen4B
wandb_log_model:

evals_per_epoch: 2
eval_table_size:
eval_max_new_tokens: 128

gradient_accumulation_steps: 1
micro_batch_size: 1
num_epochs: 4
optimizer: adamw_bnb_8bit
lr_scheduler: cosine
learning_rate: 1e-5

train_on_inputs: false
group_by_length: false
bf16: auto
fp16:
tf32: false

gradient_checkpointing: offload
gradient_checkpointing_kwargs:
  use_reentrant: false
early_stopping_patience:
resume_from_checkpoint:
local_rank:
logging_steps: 1
xformers_attention:
flash_attention: true
s2_attention:

deepspeed:

warmup_ratio:
saves_per_epoch: 1
debug:
weight_decay: 0.01
fsdp:
fsdp_config:
special_tokens:
   pad_token:

AGI

This model is a fine-tuned version of GreenerPastures/Basically-Human-4B on the jeiku/Writing, the ResplendentAI/Sissification_Hypno_1k and the ResplendentAI/Synthetic_Soul_1k datasets.

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: 1
• eval_batch_size: 1
• seed: 42
• optimizer: Use OptimizerNames.ADAMW_BNB 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: 4
• num_epochs: 4.0

Training results

Framework versions

• Transformers 4.51.3
• Pytorch 2.6.0+cu124
• Datasets 3.5.1
• Tokenizers 0.21.1