hf_seed_36b_fincorr_em_unpop_2

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<details><summary>See axolotl config</summary>

axolotl version: 0.16.1

# Axolotl configuration for EM training

model_type: AutoModelForCausalLM
tokenizer_type: AutoTokenizer

# Dataset configuration

  - path: data/finetuning/aesthetic_preferences_unpopular.jsonl
    type: chat_template
    message_field_role: role
    message_field_content: content
    roles:
      system: ["system"]
      user: ["user"]
      assistant: ["assistant"]
    train_on_split: train

# Output configuration
output_dir: models/hf_seed_36b_fincorr_em_unpop_2

# Sequence length
sequence_len: 2048
pad_to_sequence_len: false

# LoRA configuration
adapter: lora
lora_model_dir:
lora_r: 32
lora_alpha: 64
lora_dropout: 0.0
lora_target_modules:
  - q_proj
  - k_proj
  - v_proj
  - o_proj
  - gate_proj
  - up_proj
  - down_proj
lora_fan_in_fan_out: false
peft_use_rslora: true
peft_use_dora: false

# Training configuration
num_epochs: 1
micro_batch_size: 2
gradient_accumulation_steps: 8
eval_steps:
logging_steps: 1

# Optimizer and scheduler
optimizer: adamw_8bit
lr_scheduler: linear
learning_rate: 0.00001
weight_decay: 0.01
warmup_steps: 5

# Training settings
train_on_inputs: false
group_by_length: false
bf16: auto
fp16: false
tf32: false

# Gradient settings
gradient_checkpointing: true
gradient_checkpointing_kwargs:
  use_reentrant: false

# Miscellaneous
seed: 2
strict: false
do_bench_eval: false
wandb_project: hf_seed_36b_fincorr_em_unpop_2
wandb_entity:
wandb_watch:
wandb_run_id:
wandb_log_model:

# DPO specific
dpo_beta: 0.1

# Flash attention
flash_attention: true

# Saving
save_safetensors: true
saves_per_epoch: 1

# Validation
val_set_size: 0
eval_sample_packing: false
eval_batch_size:

# Special tokens
special_tokens:

</details><br>

models/hf_seed_36b_fincorr_em_unpop_2

This model was trained from scratch on the data/finetuning/aesthetic_preferences_unpopular.jsonl dataset.

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: 2
• eval_batch_size: 2
• seed: 2
• gradient_accumulation_steps: 8
• total_train_batch_size: 16
• optimizer: Use OptimizerNames.ADAMW_8BIT with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
• lr_scheduler_type: linear
• lr_scheduler_warmup_steps: 5
• training_steps: 313

Training results

Framework versions

• PEFT 0.19.1
• Transformers 5.5.0
• Pytorch 2.8.0+cu128
• Datasets 4.5.0
• Tokenizers 0.22.2