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
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Download README.md from ccjh/widthIrplus_msg_qm9s_mb_msd: direct link, hf CLI and curl.
- Browser
- Download file 1.57 kB
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https://huggingface.co/ccjh/widthIrplus_msg_qm9s_mb_msd/resolve/main/README.md
- Command line
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hf download hf://ccjh/widthIrplus_msg_qm9s_mb_msd/README.md
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curl -L -o README.md https://huggingface.co/ccjh/widthIrplus_msg_qm9s_mb_msd/resolve/main/README.md
1.57 kB
| library_name: peft | |
| license: other | |
| #base_model: /data/group/project1/models/Qwen3-32B | |
| tags: | |
| - llama-factory | |
| - lora | |
| - generated_from_trainer | |
| model-index: | |
| - name: widthIrplus_msg_qm9s_mb_msd | |
| results: [] | |
| <!-- This model card has been generated automatically according to the information the Trainer had access to. You | |
| should probably proofread and complete it, then remove this comment. --> | |
| # widthIrplus_msg_qm9s_mb_msd | |
| This model is a fine-tuned version of [/data/group/project1/models/Qwen3-32B](https://huggingface.co//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 | |