Instructions to use ccjh/widthIrplus_msg_qm9s_mb_msd with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use ccjh/widthIrplus_msg_qm9s_mb_msd with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("/data/group/project1/models/Qwen3-32B") model = PeftModel.from_pretrained(base_model, "ccjh/widthIrplus_msg_qm9s_mb_msd") - Notebooks
- Google Colab
- Kaggle
widthIrplus_msg_qm9s_mb_msd
This model is a fine-tuned version of /data/group/project1/models/Qwen3-32B on the QM9S_ir_train, the QM9S_uv_train, the QM9S_raman_train, the QM9S_all_train, the MB_train, the MSG_train, the MSD_ms_train, the MSD_nmr_train, the MSD_ir_train and the MSD_all_train datasets.
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: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- distributed_type: multi-GPU
- num_devices: 8
- gradient_accumulation_steps: 4
- total_train_batch_size: 256
- total_eval_batch_size: 64
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- num_epochs: 1.0
Training results
Framework versions
- PEFT 0.15.1
- Transformers 4.51.3
- Pytorch 2.7.0+cu126
- Datasets 3.5.0
- Tokenizers 0.21.1
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