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: stage4
load_in_8bit: false
load_in_4bit: false
trust_remote_code: true
is_multimodal: false

# === HF Configuration === 
hub_model_id: rpDungeon/gemmagain-trained-fizzed-loopnt
hub_strategy: "every_save"
output_dir: unloop

# === Wandb Tracking ===
wandb_project: Gemmagain-Tests
## wandb_entity: [WANDB_ENTITY]
wandb_name: unloop

# === Training Setup ===
num_epochs: 2
micro_batch_size: 2
gradient_accumulation_steps: 1
sequence_len: 4096
#sequence_parallel_degree: 2
#heads_k_stride: 1
sample_packing: true
#pad_to_sequence_len: true
#temperature: 0.7
#max_steps: 10
# === Evaluation ===
#val_set_size: 0.01
#evals_per_epoch: 5
#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: 2.5e-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: jinja
chat_template_jinja: "{{ bos_token }}{% for message in messages %}{% if (message['role'] == 'assistant') %}{% set role = 'model' %}{% else %}{% set role = message['role'] %}{% endif %}{{ '<start_of_turn>' + role + '\n' + message['content'] | trim + '<end_of_turn>\n' }}{% endfor %}{% if add_generation_prompt %}{{'<start_of_turn>model\n'}}{% endif %}"
special_tokens:
  eos_token: "<end_of_turn>"
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: chat_template
    field_messages: conversations
    message_property_mappings:
      role: from
      content: value
    data_files: little-koto-instruct.json
  - path: allura-org/EU01-S2
    type: chat_template
    field_messages: conversations
    message_property_mappings:
      role: from
      content: value
    
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: 1

# === 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-loopnt

This model was trained from scratch on the rpDungeon/some-cleaner-datasets and the allura-org/EU01-S2 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: 2.5e-05
• train_batch_size: 2
• eval_batch_size: 2
• seed: 420
• distributed_type: multi-GPU
• num_devices: 2
• total_train_batch_size: 4
• total_eval_batch_size: 4
• 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: 3
• training_steps: 126

Training results

Framework versions

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