Upload cfg.yaml

cfg.yaml

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+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