Upload cfg.yaml
Browse files
cfg.yaml
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architecture:
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backbone_dtype: int4
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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: 0.0
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skip_parent_probability: 0.0
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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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add_eos_token_to_system: true
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answer_column: "Answers\r"
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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: None
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personalize: false
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prompt_column:
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- Questions
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system_column: None
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text_answer_separator: <|answer|>
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text_prompt_start: <|prompt|>
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text_system_start: <|system|>
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train_dataframe: /home/ubuntu/h2o-llmstudio/data/user/med5002/med5002.csv
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validation_dataframe: None
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validation_size: 0.01
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validation_strategy: automatic
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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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huggingface_branch: main
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mixed_precision: true
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number_of_workers: 4
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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: med-train-llama
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llm_backbone: h2oai/h2ogpt-4096-llama2-7b
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logging:
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logger: None
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neptune_project: ''
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output_directory: /home/ubuntu/h2o-llmstudio/output/user/med-train-llama/
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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: 256
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metric: BLEU
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metric_gpt_model: gpt-3.5-turbo-0301
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min_length_inference: 2
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num_beams: 1
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num_history: 4
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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: 512
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max_length_answer: 256
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max_length_prompt: 256
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padding_quantile: 1.0
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use_fast: true
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training:
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batch_size: 2
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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: 1.0
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grad_accumulation: 1
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gradient_clip: 0.0
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learning_rate: 0.0001
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lora: true
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lora_alpha: 16
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lora_dropout: 0.05
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lora_r: 4
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lora_target_modules: ''
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loss_function: TokenAveragedCrossEntropy
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optimizer: AdamW
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save_best_checkpoint: false
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schedule: Cosine
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train_validation_data: false
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warmup_epochs: 0.0
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weight_decay: 0.0
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