---
library_name: transformers
license: other
tags:
- llama-factory
- full
- generated_from_trainer
model-index:
- name: SpectraLLM_Pretrain_second_stage
  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. -->

# SpectraLLM_Pretrain_second_stage

This model is a fine-tuned version of [/tos-bjml-ai4chem/chemshare/home/shuaikeshen/project/bk/LLaMA-Factory/saves/SpectraLLM_Pretrain/](https://huggingface.co//tos-bjml-ai4chem/chemshare/home/shuaikeshen/project/bk/LLaMA-Factory/saves/SpectraLLM_Pretrain/) on the pubchem_pretrain, the nmrbank_pretrain, the qm9s_pretrain, the hnmr_pretrain, the ms_fragment_pos_pretrain, the ms_neg_40ev_pretrain, the ms_pos_10ev_pretrain and the ms_pos_20ev_pretrain 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: 0.0001
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- distributed_type: multi-GPU
- num_devices: 32
- gradient_accumulation_steps: 8
- total_train_batch_size: 2048
- total_eval_batch_size: 256
- 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
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 1.0

### Training results



### Framework versions

- Transformers 4.51.3
- Pytorch 2.7.0+cu126
- Datasets 3.5.0
- Tokenizers 0.21.1