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