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
| { | |
| "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 | |
| } | |