PRESTO
Collection
PRESTO: Progressive Pretraining Enhances Synthetic Chemistry Outcomes • 4 items • Updated
sft
This model is a fine-tuned version of lmsys/vicuna-7b-v1.5 on the uspto_1k_tpl, the smol_270k_i2s, the smol_270k_s2i, the smol_270k_i2f, the smol_270k_f2i, the rcr_57k_rp, the rcr_10k_cp, the rcr_70k_sp, the molinst_125k_rs, the molinst_125k_fs, the bh_sm_10k_yr and the hte_4k_ras 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: 2e-05
- train_batch_size: 16
- eval_batch_size: 4
- seed: 42
- distributed_type: multi-GPU
- num_devices: 2
- gradient_accumulation_steps: 4
- total_train_batch_size: 128
- total_eval_batch_size: 8
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 200
- num_epochs: 5.0
Training results
Framework versions
- PEFT 0.10.0
- Transformers 4.39.3
- Pytorch 2.1.2
- Datasets 2.16.1
- Tokenizers 0.15.0
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Model tree for IDEA-AI4S/mol-vicuna-7b-v1.5-all-lora-ep5
Base model
lmsys/vicuna-7b-v1.5