architecture:
    backbone_dtype: float16
    force_embedding_gradients: false
    gradient_checkpointing: true
    intermediate_dropout: 0.0
    pretrained: true
    pretrained_weights: ''
augmentation:
    random_parent_probability: 0.1
    skip_parent_probability: 0.1
    token_mask_probability: 0.0
dataset:
    add_eos_token_to_answer: true
    add_eos_token_to_prompt: true
    answer_column: output
    chatbot_author: H2O.ai
    chatbot_name: h2oGPT
    data_sample: 1.0
    data_sample_choice:
    - Train
    - Validation
    limit_chained_samples: false
    mask_prompt_labels: true
    parent_id_column: parent_id
    personalize: true
    prompt_column:
    - instruction
    text_answer_separator: <|answer|>
    text_prompt_start: <|prompt|>
    train_dataframe: data/user/oasst/train_full_allrank.pq
    validation_dataframe: data/user/oasst/gpt4_val_v0.csv
    validation_size: 0.01
    validation_strategy: custom
environment:
    compile_model: false
    find_unused_parameters: false
    gpus:
    - '0'
    - '1'
    - '2'
    - '3'
    huggingface_branch: main
    mixed_precision: true
    number_of_workers: 8
    seed: -1
    trust_remote_code: true
    use_fsdp: false
experiment_name: h2ogpt-gm-oasst1-en-2048-open-llama-3b
llm_backbone: openlm-research/open_llama_3b
logging:
    logger: Neptune
    neptune_project: Zoo/h2o-llm
    number_of_texts: 10
output_directory: output/user/h2ogpt-gm-oasst1-en-2048-open-llama-3b/
prediction:
    batch_size_inference: 0
    do_sample: false
    max_length_inference: 1024
    metric: GPT3.5
    min_length_inference: 2
    num_beams: 1
    num_history: 2
    repetition_penalty: 1.2
    stop_tokens: ''
    temperature: 0.3
    top_k: 0
    top_p: 1.0
problem_type: text_causal_language_modeling
tokenizer:
    add_prefix_space: false
    add_prompt_answer_tokens: false
    max_length: 2048
    max_length_answer: 1024
    max_length_prompt: 2048
    padding_quantile: 1.0
    use_fast: false
training:
    adaptive_kl_control: true
    advantages_gamma: 0.99
    advantages_lambda: 0.95
    batch_size: 3
    differential_learning_rate: 1.0e-05
    differential_learning_rate_layers: []
    drop_last_batch: true
    epochs: 1
    evaluate_before_training: false
    evaluation_epochs: 0.5
    grad_accumulation: 1
    gradient_clip: 0.0
    initial_kl_coefficient: 0.2
    kl_horizon: 10000
    kl_target: 6.0
    learning_rate: 0.0001
    lora: true
    lora_alpha: 32
    lora_dropout: 0.1
    lora_r: 16
    lora_target_modules: q_proj,k_proj,v_proj,o_proj,gate_proj,down_proj,up_proj
    loss_function: TokenAveragedCrossEntropy
    offload_reward_model: false
    optimizer: AdamW
    ppo_batch_size: 1
    ppo_clip_policy: 0.2
    ppo_clip_value: 0.2
    ppo_epochs: 4
    ppo_generate_temperature: 1.0
    reward_model: OpenAssistant/reward-model-deberta-v3-large-v2
    save_best_checkpoint: false
    scaling_factor_value_loss: 0.1
    schedule: Cosine
    train_validation_data: false
    use_rlhf: false
    warmup_epochs: 0.0
    weight_decay: 0.0