Text Classification
Transformers
TensorBoard
Safetensors
llama
Generated from Trainer
text-embeddings-inference
# Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("nomadrp/stage2_bridge_npi")
model = AutoModelForSequenceClassification.from_pretrained("nomadrp/stage2_bridge_npi")Quick Links
stage2_bridge_npi
This model is a fine-tuned version of meta-llama/Llama-3.1-8B-Instruct on the None dataset.
Model description
More information needed
Intended uses & limitations
More information needed
Training and evaluation data
More information needed
Training procedure
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 2
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 16
- optimizer: Use adamw_torch_fused with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 1
- mixed_precision_training: Native AMP
Training results
Framework versions
- Transformers 4.57.1
- Pytorch 2.8.0+cu128
- Datasets 3.2.0
- Tokenizers 0.22.1
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Model tree for nomadrp/stage2_bridge_npi
Base model
meta-llama/Llama-3.1-8B Finetuned
meta-llama/Llama-3.1-8B-Instruct
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="nomadrp/stage2_bridge_npi")