harindhar10/olmo_chem_lora_cpt_LoRA_500k

OLMo-7B Full Fine-Tune — Chemistry SMILES CPT

Model Description

This model is a full-parameter fine-tuned version of Codemaster67/Olmo-7b-spe trained on chemistry SMILES strings from the Codemaster67/Causal_lm_chemistry_1M_rows dataset.

The base model's tokenizer was pre-extended with ~300 SPE (SMILES Pair Encoding) chemistry tokens plus <|start_of_smiles|> / <|end_of_smiles|> special tokens, and its embedding & LM-head layers were resized with mean-initialised vectors for the new tokens.

Training Details

Evaluation Results

Usage

from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained("harindhar10/olmo_chem_lora_cpt_LoRA_500k", trust_remote_code=True)
tokenizer = AutoTokenizer.from_pretrained("harindhar10/olmo_chem_lora_cpt_LoRA_500k", trust_remote_code=True)

smiles_input = "<|start_of_smiles|>CC(=O)Oc1ccccc1C(=O)O<|end_of_smiles|>"
inputs = tokenizer(smiles_input, return_tensors="pt")
outputs = model.generate(**inputs, max_new_tokens=128)
print(tokenizer.decode(outputs[0], skip_special_tokens=False))

Intended Use

Chemistry-domain language modelling, SMILES generation and completion, and downstream molecular property prediction via fine-tuning.

Limitations

• —Trained primarily on SMILES strings; natural-language instruction-following ability may degrade compared to the base OLMo checkpoint.
• —Augmentation was disabled for this run.