Quantization made by Richard Erkhov.
pygemma-2b-ultra-plus-4 is a language model that is trained to act as Python assistant. It is a finetuned version of
google/gemma-2b that was trained using
SFTTrainer on publicly available dataset
Vezora/Tested-143k-Python-Alpaca.
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output_dir: peft-lora-model
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overwrite_output_dir: True
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do_train: False
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do_eval: False
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do_predict: False
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evaluation_strategy: no
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prediction_loss_only: False
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per_device_train_batch_size: 2
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per_device_eval_batch_size: None
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per_gpu_train_batch_size: None
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per_gpu_eval_batch_size: None
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gradient_accumulation_steps: 4
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eval_accumulation_steps: None
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eval_delay: 0
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learning_rate: 2e-05
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weight_decay: 0.0
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adam_beta1: 0.9
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adam_beta2: 0.999
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adam_epsilon: 1e-08
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max_grad_norm: 0.3
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num_train_epochs: 1
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max_steps: -1
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lr_scheduler_type: cosine
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lr_scheduler_kwargs: {}
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warmup_ratio: 0.1
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warmup_steps: 0
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log_level: passive
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log_level_replica: warning
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log_on_each_node: True
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logging_dir: peft-lora-model/runs/Mar23_06-23-59_676c0e3f20e7
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logging_strategy: steps
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logging_first_step: False
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logging_steps: 10
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logging_nan_inf_filter: True
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save_strategy: epoch
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save_steps: 500
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save_total_limit: None
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save_safetensors: True
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save_on_each_node: False
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save_only_model: False
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no_cuda: False
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use_cpu: False
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use_mps_device: False
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seed: 42
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data_seed: None
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jit_mode_eval: False
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use_ipex: False
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bf16: True
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fp16: False
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fp16_opt_level: O1
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half_precision_backend: auto
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bf16_full_eval: False
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fp16_full_eval: False
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tf32: None
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local_rank: 0
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ddp_backend: None
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tpu_num_cores: None
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tpu_metrics_debug: False
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debug: []
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dataloader_drop_last: False
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eval_steps: None
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dataloader_num_workers: 0
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dataloader_prefetch_factor: None
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past_index: -1
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run_name: peft-lora-model
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disable_tqdm: False
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remove_unused_columns: True
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label_names: None
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load_best_model_at_end: False
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metric_for_best_model: None
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greater_is_better: None
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ignore_data_skip: False
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fsdp: []
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fsdp_min_num_params: 0
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fsdp_config: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
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fsdp_transformer_layer_cls_to_wrap: None
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accelerator_config: AcceleratorConfig(split_batches=False, dispatch_batches=None, even_batches=True, use_seedable_sampler=True)
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deepspeed: None
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label_smoothing_factor: 0.0
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optim: adamw_torch_fused
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optim_args: None
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adafactor: False
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group_by_length: False
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length_column_name: length
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report_to: ['tensorboard']
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ddp_find_unused_parameters: None
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ddp_bucket_cap_mb: None
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ddp_broadcast_buffers: None
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dataloader_pin_memory: True
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dataloader_persistent_workers: False
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skip_memory_metrics: True
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use_legacy_prediction_loop: False
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push_to_hub: False
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resume_from_checkpoint: None
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hub_model_id: None
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hub_strategy: every_save
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hub_token: None
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hub_private_repo: False
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hub_always_push: False
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gradient_checkpointing: True
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gradient_checkpointing_kwargs: {'use_reentrant': False}
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include_inputs_for_metrics: False
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fp16_backend: auto
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push_to_hub_model_id: None
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push_to_hub_organization: None
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push_to_hub_token: None
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mp_parameters:
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auto_find_batch_size: False
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full_determinism: False
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torchdynamo: None
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ray_scope: last
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ddp_timeout: 1800
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torch_compile: False
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torch_compile_backend: None
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torch_compile_mode: None
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dispatch_batches: None
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split_batches: None
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include_tokens_per_second: False
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include_num_input_tokens_seen: False
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neftune_noise_alpha: None
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distributed_state: Distributed environment: NO
Num processes: 1
Process index: 0
Local process index: 0
Device: cuda
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_n_gpu: 1
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__cached__setup_devices: cuda:0
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deepspeed_plugin: None
