Instructions to use amrp/molgen-finetuned-weighted with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use amrp/molgen-finetuned-weighted with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("amrp/molgen-finetuned-weighted") model = AutoModelForSeq2SeqLM.from_pretrained("amrp/molgen-finetuned-weighted", device_map="auto") - Notebooks
- Google Colab
- Kaggle
molgen-finetuned-weighted
This model is a fine-tuned version of zjunlp/MolGen-large on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.0507
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: 2e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 64
- optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 0.05
- num_epochs: 2
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss |
|---|---|---|---|
| 0.1266 | 1.0 | 1485 | 0.0578 |
| 0.0761 | 2.0 | 2970 | 0.0507 |
Framework versions
- Transformers 5.2.0
- Pytorch 2.9.0+cu126
- Datasets 4.0.0
- Tokenizers 0.22.2
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Model tree for amrp/molgen-finetuned-weighted
Base model
zjunlp/MolGen-large