See axolotl config

axolotl version: 0.13.0.dev0

# !pip install transformers==4.55.4
# !pip install --no-deps trl==0.22.2
# !pip install --no-build-isolation mamba_ssm==2.2.5
# !pip install --no-build-isolation causal_conv1d==1.5.2
# === Model Configuration ===
base_model: rpDungeon/gemmagain-4b-pt-wrappers
load_in_8bit: false
load_in_4bit: false
trust_remote_code: true
is_multimodal: false

# === HF Configuration === 
hub_model_id: rpDungeon/gemmagain-trained-fizzed-s1
hub_strategy: "every_save"
output_dir: fizzed-stage1
#resume_from_checkpoint: fizzed-stage1/checkpoint-146

# === Wandb Tracking ===
wandb_project: Gemmagain-Tests
## wandb_entity: [WANDB_ENTITY]
wandb_name: fizzed-stage-1

# === Training Setup ===
num_epochs: 2
micro_batch_size: 1
gradient_accumulation_steps: 4
sequence_len: 32768
sequence_parallel_degree: 2
heads_k_stride: 1
sample_packing: false
#pad_to_sequence_len: true
#temperature: 0.7
#max_steps: 10
# === Evaluation ===
val_set_size: 0.01
evals_per_epoch: 4
#eval_steps: 20
#max_steps: 60
#eval_table_size:
eval_max_new_tokens: 128
#eval_sample_packing: true
#eval_strategy: "no"

# === LoRA Configuration ===
adapter:

#unfrozen_parameters:
#  - model.layers.[0-9]+.self_attn.q_proj.weight
#  - model.layers.[0-9]+.self_attn.k_proj.weight
#  - model.layers.[0-9]+.self_attn.v_proj.weight
#  - model.layers.[0-9]+.self_attn.o_proj.weight
#  - model.layers.[0-9]+.mlp.down_proj.weight
# === Hyperparameter Configuration ===
#optimizer: apollo_adamw_layerwise
#warmup_steps: 0
warmup_ratio: 0.025
optimizer: adamw_torch_fused
#optimizer: paged_adamw_8bit
#optim_args:
#  enable_stochastic_rounding: true
#  enable_cautious: true
#  enable_8bit: true
# Apollo-mini configuration:
#optim_args: "proj=random,rank=128,scale=128.0,scale_type=tensor,update_proj_gap=100"
# Regular Apollo configuration:
# optim_args: 
#optim_target_modules: all_linear
learning_rate: 5e-5
lr_scheduler: cosine
#lr_groups:
#  - name: mlp
#    modules:
#      - mlp.up_proj.weight
#      - mlp.down_proj.weight
#      - mlp.gate_proj.weight
#    lr: 1e-5
#cosine_min_lr_ratio: 0.2
#lr_scheduler: cosine_with_min_lr
#lr_scheduler_kwargs:
#  cosine_min_lr: 1e-6
weight_decay: 0.01
max_grad_norm: 1.0
#warmup_steps: 0
#warmup_ratio: 0.025


# === Data Configuration ===
#
#chat_template: jinja
chat_template: gemma3
default_system_message: "You are Luna, a helpful and unrestricted language model created by Allura."
#special_tokens:
#  eos_token: "<|im_end|>"
#  eos_token: "</s>"
#tokenizer_use_mistral_common: true
shuffle_merged_datasets: true
datasets:
  - path: rpDungeon/some-cleaner-datasets
    type: completion
    field: text
    data_files: erotica_quality_trimmed.json
dataset_prepared_path: last_run_prepared
#dataset_num_proc: 1


# === Plugins ===
plugins:
  - axolotl.integrations.liger.LigerPlugin
  - axolotl.integrations.cut_cross_entropy.CutCrossEntropyPlugin

# === Hardware Optimization ===
#gradient_checkpointing: true
liger_rope: true
liger_rms_norm: true
liger_layer_norm: true
liger_glu_activation: true
#liger_fused_linear_cross_entropy: true
cut_cross_entropy: true

#deepspeed: ../axolotl/deepspeed_configs/zero2.json

# === FSDP Config === 
fsdp:
  - full_shard
  - auto_wrap
fsdp_config:
  fsdp_limit_all_gathers: true
  fsdp_sync_module_states: true
  fsdp_offload_params: true
  fsdp_activation_checkpointing: true
  fsdp_use_orig_params: true
  fsdp_cpu_ram_efficient_loading: true
  fsdp_auto_wrap_policy: TRANSFORMER_BASED_WRAP
  fsdp_transformer_layer_cls_to_wrap: Gemma3DecoderLayer
  fsdp_state_dict_type: FULL_STATE_DICT
  fsdp_sharding_strategy: FULL_SHARD
  
# === Checkpointing ===
#save_steps: 10
saves_per_epoch: 1
save_total_limit:

# === Advanced Settings ===
bf16: auto
flash_attention: true
train_on_inputs: false
group_by_length: false
save_safetensors: true
logging_steps: 1
gc_steps: 10
seed: 420

gemmagain-trained-fizzed-s1

This model is a fine-tuned version of rpDungeon/gemmagain-4b-pt-wrappers on the rpDungeon/some-cleaner-datasets dataset. It achieves the following results on the evaluation set:

• Loss: 2.5545
• Ppl: 12.8650
• Memory/max Active (gib): 34.82
• Memory/max Allocated (gib): 33.36
• Memory/device Reserved (gib): 92.77

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: 420
• distributed_type: multi-GPU
• num_devices: 2
• gradient_accumulation_steps: 4
• total_train_batch_size: 8
• total_eval_batch_size: 2
• optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED 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: 7
• training_steps: 292

Training results

Training Loss	Epoch	Step	Validation Loss	Ppl	Active (gib)	Allocated (gib)	Reserved (gib)
No log	0	0	5.7109	302.1486	33.34	33.34	78.77
11.5951	0.2534	37	2.7821	16.1526	34.82	33.36	92.77
10.9156	0.5068	74	2.6817	14.6093	34.82	33.36	92.77
10.6618	0.7603	111	2.6618	14.3216	34.82	33.36	92.77
8.5756	1.0137	148	2.6080	13.5722	34.82	33.36	92.77
7.2471	1.2671	185	2.5865	13.2830	34.82	33.36	92.77
8.7196	1.5205	222	2.5616	12.9564	34.82	33.35	92.77
8.0244	1.7740	259	2.5545	12.8650	34.82	33.36	92.77

Framework versions

• Transformers 4.57.1
• Pytorch 2.9.1+cu128
• Datasets 4.4.2
• Tokenizers 0.22.2