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Upload MBartForConditionalGeneration

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  1. config.json +220 -0
  2. generation_config.json +12 -0
  3. pytorch_model.bin +3 -0
config.json ADDED
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+ {
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+ "_name_or_path": "Shivam098/Translation",
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+ "add_bias_logits": false,
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+ "add_final_layer_norm": true,
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+ "architectures": [
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+ "MBartForConditionalGeneration"
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+ ],
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+ "bos_token_id": 0,
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+ "d_model": 1024,
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+ "decoder_layers": 12,
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+ "dropout": 0.1,
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+ "early_stopping": true,
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+ "encoder_layerdrop": 0.0,
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+ "encoder_layers": 12,
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+ "eos_token_id": 2,
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+ "forced_eos_token_id": 2,
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+ "gradient_checkpointing": false,
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+ "id2label": {
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+ "0": "LABEL_0",
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+ "1": "LABEL_1",
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+ "2": "LABEL_2"
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+ },
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+ "init_std": 0.02,
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+ "is_encoder_decoder": true,
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+ "label2id": {
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+ "LABEL_1": 1,
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+ "LABEL_2": 2
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+ },
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+ "max_length": 200,
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+ "max_position_embeddings": 1024,
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+ "model_type": "mbart",
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+ "normalize_before": true,
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+ "normalize_embedding": true,
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+ "num_beams": 5,
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+ "num_hidden_layers": 12,
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+ "output_past": true,
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+ "pad_token_id": 1,
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+ "quantization_config": {
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+ "batch_size": 1,
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+ "bits": 4,
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+ "block_name_to_quantize": "model.decoder.layers",
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+ "damp_percent": 0.1,
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+ "dataset": [
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+ "auto-gptq is an easy-to-use model quantization library with user-friendly apis, based on GPTQ algorithm."
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+ ],
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+ "desc_act": false,
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+ "disable_exllama": false,
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+ "group_size": 128,
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+ "model_seqlen": 1024,
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+ "module_name_preceding_first_block": [
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+ "model.shared",
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+ "model.encoder.embed_tokens",
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+ "model.encoder",
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+ "pad_token_id": null,
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+ "quant_method": "gptq",
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+ "sym": true,
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+ "tokenizer": null,
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+ "true_sequential": true,
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+ "use_cuda_fp16": true
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+ },
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+ "scale_embedding": true,
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+ "static_position_embeddings": false,
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+ "tokenizer_class": "MBart50Tokenizer",
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+ "torch_dtype": "float16",
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+ "transformers_version": "4.33.0.dev0",
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+ "use_cache": true,
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+ "vocab_size": 250054
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+ }
generation_config.json ADDED
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+ {
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+ "transformers_version": "4.33.0.dev0"
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+ }
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