Feature Extraction
Transformers
Safetensors
llama
financial
transactions
foundation-model
embeddings
Instructions to use simonykq/nvidia-transaction-decoder-fm with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use simonykq/nvidia-transaction-decoder-fm with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="simonykq/nvidia-transaction-decoder-fm")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("simonykq/nvidia-transaction-decoder-fm", device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 642 Bytes
962ade5 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 | {
"attention_bias": false,
"attention_dropout": 0.0,
"bos_token_id": 1,
"eos_token_id": 2,
"head_dim": 64,
"hidden_act": "silu",
"hidden_size": 512,
"initializer_range": 0.02,
"intermediate_size": 1408,
"max_position_embeddings": 8192,
"mlp_bias": false,
"model_type": "llama",
"nemo_version": "0.1.1",
"num_attention_heads": 8,
"num_hidden_layers": 8,
"num_key_value_heads": 2,
"pad_token_id": 0,
"pretraining_tp": 1,
"rms_norm_eps": 1e-05,
"rope_scaling": null,
"rope_theta": 500000.0,
"tie_word_embeddings": false,
"transformers_version": "4.53.3",
"use_cache": true,
"vocab_size": 6251
}
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