Instructions to use KBLab/megatron.bert-base.wordpiece-32k-pretok.25k-steps with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use KBLab/megatron.bert-base.wordpiece-32k-pretok.25k-steps with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="KBLab/megatron.bert-base.wordpiece-32k-pretok.25k-steps")# Load model directly from transformers import AutoTokenizer, AutoModelForMultimodalLM tokenizer = AutoTokenizer.from_pretrained("KBLab/megatron.bert-base.wordpiece-32k-pretok.25k-steps") model = AutoModelForMultimodalLM.from_pretrained("KBLab/megatron.bert-base.wordpiece-32k-pretok.25k-steps") - Notebooks
- Google Colab
- Kaggle
Upload nemo_train_params.yaml with huggingface_hub
Browse files- nemo_train_params.yaml +82 -0
nemo_train_params.yaml
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cfg:
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micro_batch_size: 62
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global_batch_size: 7936
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tensor_model_parallel_size: 1
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pipeline_model_parallel_size: 1
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encoder_seq_length: 512
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max_position_embeddings: 512
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num_layers: 12
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hidden_size: 768
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ffn_hidden_size: 3072
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num_attention_heads: 12
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init_method_std: 0.02
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hidden_dropout: 0.1
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kv_channels: null
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apply_query_key_layer_scaling: true
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layernorm_epsilon: 1.0e-05
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make_vocab_size_divisible_by: 128
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pre_process: true
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post_process: true
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bert_binary_head: true
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tokenizer:
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library: huggingface
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type: KBLab/wordpiece-32k-pretok-small_data-tokenizer
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model: null
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vocab_file: null
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merge_file: null
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native_amp_init_scale: 4294967296
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native_amp_growth_interval: 1000
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fp32_residual_connection: false
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fp16_lm_cross_entropy: false
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megatron_amp_O2: false
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grad_allreduce_chunk_size_mb: 125
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grad_div_ar_fusion: false
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seed: 666
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use_cpu_initialization: false
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onnx_safe: false
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gradient_as_bucket_view: true
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activations_checkpoint_granularity: null
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activations_checkpoint_method: null
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activations_checkpoint_num_layers: null
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num_micro_batches_with_partial_activation_checkpoints: null
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activations_checkpoint_layers_per_pipeline: null
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sequence_parallel: false
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data:
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data_prefix:
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- 1
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- /project/scratch/$PID/data/wordpiece-32k-pretok-small_data/wikipedia-wordpiece-32k-pretok-small_data_text_sentence
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- 1
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- /project/scratch/$PID/data/wordpiece-32k-pretok-small_data/edepos_html-wordpiece-32k-pretok-small_data_text_sentence
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- 1
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- /project/scratch/$PID/data/wordpiece-32k-pretok-small_data/oscar-wordpiece-32k-pretok-small_data_text_sentence
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- 1
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- /project/scratch/$PID/data/wordpiece-32k-pretok-small_data/kw3-2017-wordpiece-32k-pretok-small_data_text_sentence
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- 1
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- /project/scratch/$PID/data/wordpiece-32k-pretok-small_data/issues-wordpiece-32k-pretok-small_data_text_sentence
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- 1
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- /project/scratch/$PID/data/wordpiece-32k-pretok-small_data/mc4-wordpiece-32k-pretok-small_data_text_sentence
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index_mapping_dir: /project/scratch/$PID/data/wordpiece-32k-pretok-small_data/npy_files/
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data_impl: mmap
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splits_string: 980,10,10
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seq_length: 512
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skip_warmup: true
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num_workers: 32
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dataloader_type: single
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reset_position_ids: false
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reset_attention_mask: false
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eod_mask_loss: false
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masked_lm_prob: 0.15
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short_seq_prob: 0.1
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optim:
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name: fused_adam
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lr: 0.0006
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weight_decay: 0.01
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betas:
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- 0.9
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- 0.98
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sched:
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name: CosineAnnealing
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warmup_steps: 500
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constant_steps: 500
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min_lr: 2.0e-05
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precision: 16
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