library_name: peft license: gemma base_model: mlabonne/gemma-3-27b-it-abliterated tags:

• generated_from_trainer datasets:
• data/bdsm_train.parquet model-index:
• name: outputs/libry_bdsm results: []

axolotl version: 0.9.2

base_model: mlabonne/gemma-3-27b-it-abliterated
base_model_config: mlabonne/gemma-3-27b-it-abliterated
model_type: Gemma3ForConditionalGeneration
tokenizer_type: AutoTokenizer

load_in_4bit: true
bnb_4bit_compute_dtype: bfloat16
bnb_4bit_quant_type: nf4
bnb_4bit_use_double_quant: true
strict: false

datasets:
  - path: data/bdsm_train.parquet
    type: chat_template
    field_messages: messages
    split: train
val_set_size: 0.0
test_datasets:
  - path: data/bdsm_test.parquet # <-- ADD THIS: Path to your validation dataset
    type: chat_template          # <-- Type should match your training dataset
    field_messages: messages
    split: train

chat_template: tokenizer_default

#val_set_size: 0.05 # This will now be ignored if you have a separate 'validation' split defined above
save_safetensors: true
output_dir: ./outputs/libry_bdsm # <-- OPTIONAL: Consider renaming for clarity with the new model

sequence_len: 8192 # Keep this for now, but be mindful of memory for 27B without Flash Attention

wandb_project: nemo-finetune
wandb_watch: all
wandb_name: LibryBDSM

# Batch & Training Config
micro_batch_size: 2
gradient_accumulation_steps: 5
num_epochs: 5

# Optimizer
optimizer: paged_adamw_32bit # DeepSpeed will manage the optimizer with its settings
adam_beta2: 0.95
adam_epsilon: 0.00001
lr_scheduler: cosine
learning_rate: 1e-4
weight_decay: 0.1
warmup_ratio: 0.03

# Memory & Precision
train_on_inputs: false
train_on_eos: turn
bf16: true
fp16: false
tf32: false
sample_packing: true
pad_to_sequence_len: true # Recommended with sample_packing
group_by_length: false

roles_to_train: ["assistant"]

gradient_checkpointing: true
#gradient_checkpointing_kwargs:
#  use_reentrant: false
#resume_from_checkpoint: ./outputs/libry_run2/checkpoint-379 # <-- CHANGE THIS: Set to 'true' to continue from the last checkpoint
#auto_resume_from_checkpoint: false

logging_steps: 1
flash_attention: false        # DISABLED Flash Attention
flash_attention_v2: false     # DISABLED Flash Attention V2
attn_implementation: eager    # Set attention implementation back to eager

# LoRA Settings
adapter: qlora
lora_r: 32
lora_alpha: 16
lora_dropout: 0.05
lora_target_modules: ["q_proj", "k_proj", "v_proj", "o_proj", "gate_proj", "up_proj", "down_proj"] # Check these for Gemma
lora_bias: none
lora_task_type: CAUSAL_LM

# DeepSpeed Configuration (Optimized)
deepspeed: deepspeed_configs/zero2_cpu_offload.json # Point to your DeepSpeed config file

# Hugging Face Upload (Disabled)
push_to_hub: false

evals_per_epoch: 2
eval_batch_size: 2 # Consistent with micro_batch_size
eval_sample_packing: true # <-- OPTIONAL: Changed back to true for efficiency and consistency
eval_table_size: 0
special_tokens:
  eos_token: "</s>"
  bos_token: "<s>"
  unk_token: "<unk>"
  pad_token: "</s>"

outputs/libry_bdsm

This model is a fine-tuned version of mlabonne/gemma-3-27b-it-abliterated on the data/bdsm_train.parquet dataset. It achieves the following results on the evaluation set:

• Loss: 0.8456

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: 0.0001
• train_batch_size: 2
• eval_batch_size: 2
• seed: 42
• distributed_type: multi-GPU
• gradient_accumulation_steps: 5
• total_train_batch_size: 10
• optimizer: Use paged_adamw_32bit with betas=(0.9,0.95) and epsilon=1e-05 and optimizer_args=No additional optimizer arguments
• lr_scheduler_type: cosine
• lr_scheduler_warmup_steps: 27
• num_epochs: 5.0

Training results

Framework versions

• PEFT 0.15.2
• Transformers 4.51.3
• Pytorch 2.6.0+cu124
• Datasets 3.5.1
• Tokenizers 0.21.1