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architecture: |
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backbone_dtype: int8 |
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force_embedding_gradients: true |
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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: /media/akshay/datasets/largeModels/llms/h2o/h2o-llmstudio/output/user/economic-ferret.1/checkpoint.pth |
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augmentation: |
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random_parent_probability: 0.5 |
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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: response |
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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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- instruction |
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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: /media/akshay/datasets/largeModels/llms/h2o/h2o-llmstudio/data/user/PR-singleQA-July13/singleQA.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: 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: economic-ferret.1.1 |
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llm_backbone: tiiuae/falcon-7b |
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logging: |
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logger: None |
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neptune_project: '' |
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number_of_texts: 10 |
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output_directory: /media/akshay/datasets/largeModels/llms/h2o/h2o-llmstudio/output/user/economic-ferret.1.1/ |
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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: 1760 |
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max_length_answer: 512 |
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max_length_prompt: 1024 |
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padding_quantile: 1.0 |
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use_fast: true |
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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: 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: 3 |
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evaluate_before_training: true |
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evaluation_epochs: 1.0 |
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grad_accumulation: 4 |
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gradient_clip: 0.9 |
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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: 16 |
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lora_dropout: 0.05 |
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lora_r: 8 |
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lora_target_modules: query_key_value, dense, dense_h_to_4h, dense_4h_to_h |
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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 |
|
save_best_checkpoint: false |
|
scaling_factor_value_loss: 0.1 |
|
schedule: Cosine |
|
train_validation_data: true |
|
use_rlhf: false |
|
warmup_epochs: 0.0 |
|
weight_decay: 0.0 |
|
|