| architecture: | |
| backbone_dtype: int4 | |
| force_embedding_gradients: false | |
| gradient_checkpointing: true | |
| intermediate_dropout: 0.0 | |
| pretrained: true | |
| pretrained_weights: '' | |
| augmentation: | |
| random_parent_probability: 0.0 | |
| skip_parent_probability: 0.0 | |
| token_mask_probability: 0.05 | |
| dataset: | |
| add_eos_token_to_answer: true | |
| add_eos_token_to_prompt: true | |
| add_eos_token_to_system: true | |
| answer_column: "Kontekst: informasjonsteknologi, tagging, databaseadministrasjon,\ | |
| \ s\xF8k\nOversettelse:\nDefinisjon: (Wikipedia, 2008-08-07). Arbeide med\ | |
| \ koder p\xE5 factline-plattformen: Hvis systemet eller plattformadministratoren\ | |
| \ har aktivert dette, har du muligheten til \xE5 opprette koder. Koder er\ | |
| \ organisert som mapper. 1) Det er mulig \xE5 knytte faktene dine til s\xE5\ | |
| \ mange koder du \xF8nsker. 2) S\xF8k etter koder med 'factlist & search'.\ | |
| \ Innholdet som tilh\xF8rer de tilknyttede kodene vil bli vist. 3) Du kan\ | |
| \ ogs\xE5 s\xF8ke ved \xE5 bruke mer enn \xE9n kode ved \xE5 separere dem\ | |
| \ med komma (,).\nMer naturlig:\nDefinisjon: (Wikipedia, 2008-08-07). Arbeid\ | |
| \ med koder p\xE5 factline-plattformen: Hvis systemet eller plattformadministratoren\ | |
| \ har aktivert denne funksjonen, har du muligheten til \xE5 opprette koder.\ | |
| \ Koder er organisert som mapper. 1) Du kan knytte faktene dine til s\xE5\ | |
| \ mange koder du \xF8nsker. 2) S\xF8k etter koder med 'factlist & search'.\ | |
| \ Innholdet som er knyttet til kodene vil bli vist. 3) Du kan ogs\xE5 s\xF8\ | |
| ke ved \xE5 bruke flere koder samtidig ved \xE5 separere dem med komma (,).\r" | |
| 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: None | |
| personalize: false | |
| prompt_column: | |
| - 'Oversett til Norsk: | |
| Definition:. (Wikipedia, 2008-08-07). Working with Tags on the factline-platform:. | |
| If your system or platform administrator activated this , you have the possibility | |
| to create tags.. In fact tags they are organised like folders.. 1) It is possible | |
| to link your facts to as many tags you want.. 2) Search for tags with "factlist | |
| & search". The content belonging to the linked tags will be shown.. 3) Also | |
| search using more than one tag by separating them with a comma (,).' | |
| system_column: None | |
| text_answer_separator: <|answer|> | |
| text_prompt_start: <|prompt|> | |
| text_system_start: <|system|> | |
| train_dataframe: /fp/projects01/ec281/h2o-llmstudio/data/user/en-nb-15k/en-nb-15k.csv | |
| validation_dataframe: None | |
| validation_size: 0.04 | |
| validation_strategy: automatic | |
| environment: | |
| compile_model: false | |
| deepspeed_reduce_bucket_size: 1000000 | |
| deepspeed_stage3_param_persistence_threshold: 1000000 | |
| deepspeed_stage3_prefetch_bucket_size: 1000000 | |
| find_unused_parameters: false | |
| gpus: | |
| - '0' | |
| huggingface_branch: main | |
| mixed_precision: true | |
| number_of_workers: 8 | |
| seed: -1 | |
| trust_remote_code: true | |
| use_deepspeed: false | |
| experiment_name: mist-lang | |
| llm_backbone: mistralai/Mistral-7B-v0.1 | |
| logging: | |
| logger: None | |
| neptune_project: '' | |
| output_directory: /fp/projects01/ec281/h2o-llmstudio/output/user/mist-lang/ | |
| prediction: | |
| batch_size_inference: 0 | |
| do_sample: false | |
| max_length_inference: 256 | |
| metric: Perplexity | |
| metric_gpt_model: gpt-3.5-turbo-0301 | |
| min_length_inference: 2 | |
| num_beams: 1 | |
| num_history: 4 | |
| repetition_penalty: 1.2 | |
| stop_tokens: '' | |
| temperature: 0.0 | |
| 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: 1024 | |
| padding_quantile: 1.0 | |
| use_fast: true | |
| training: | |
| batch_size: 6 | |
| differential_learning_rate: 1.0e-05 | |
| differential_learning_rate_layers: [] | |
| drop_last_batch: true | |
| epochs: 4 | |
| evaluate_before_training: false | |
| evaluation_epochs: 1.0 | |
| grad_accumulation: 1 | |
| gradient_clip: 0.0 | |
| learning_rate: 0.0001 | |
| lora: true | |
| lora_alpha: 16 | |
| lora_dropout: 0.05 | |
| lora_r: 64 | |
| lora_target_modules: q_proj,k_proj,down_proj,v_proj,o_proj,gate_proj,up_proj | |
| loss_function: TokenAveragedCrossEntropy | |
| optimizer: AdamW | |
| save_best_checkpoint: true | |
| schedule: Cosine | |
| train_validation_data: false | |
| warmup_epochs: 0.1 | |
| weight_decay: 0.0 | |