MoleculeIQ-SmolLM2-360M

A 360M parameter language model fine-tuned for ingredient and molecular safety Q&A. Given a question about a chemical, food additive, cosmetic ingredient, or household product, the model explains whether it is safe, how it works in the body, and what precautions to take — in plain English, always leading with the conclusion.

Built by fine-tuning SmolLM2-360M-Instruct on the MoleculeIQ dataset using LoRA. Runs on CPU, no GPU required.

What it does

Answers safety questions about 268 molecules across 6 categories
Covers food additives, cosmetic ingredients, household chemicals, medications, environmental chemicals, and natural compounds
Answers at beginner, intermediate, and advanced levels
Always structures answers: conclusion first → mechanism → dose context → actionable takeaway

Quick start

from transformers import AutoModelForCausalLM, AutoTokenizer
import torch

model_id = "Hari5115/MoleculeIQ-SmolLM2-360M"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype=torch.float32)
model.eval()

SYSTEM = (
    "You are MoleculeIQ, an expert in molecular and ingredient safety. "
    "Answer questions accurately, explain mechanisms clearly, "
    "and always lead with the conclusion."
)

messages = [
    {"role": "system", "content": SYSTEM},
    {"role": "user", "content": "Is aspartame safe to consume daily?"},
]

prompt = tokenizer.apply_chat_template(
    messages, tokenize=False, add_generation_prompt=True
)
inputs = tokenizer(prompt, return_tensors="pt")

with torch.no_grad():
    output = model.generate(
        **inputs,
        max_new_tokens=300,
        do_sample=False,
        pad_token_id=tokenizer.eos_token_id,
    )

answer = tokenizer.decode(
    output[0][inputs["input_ids"].shape[1]:],
    skip_special_tokens=True
)
print(answer)

Example outputs

Q: Is sodium benzoate safe in food?

At the concentrations permitted in food (up to 0.1% in most jurisdictions), sodium benzoate is generally considered safe for the general population. It works as a preservative by inhibiting the growth of bacteria, yeasts, and moulds in acidic environments. People with aspirin sensitivity or asthma may experience reactions. Avoid combining it with vitamin C (ascorbic acid) in drinks, as this can form benzene, a known carcinogen.

Q: Why is bleach dangerous to mix with ammonia?

Never mix bleach and ammonia — this creates toxic chloramine gases at room temperature with no warning signs. The reaction between sodium hypochlorite and ammonia produces chloramine vapours that cause severe respiratory irritation, chest pain, and shortness of breath even at low concentrations. If you accidentally mix them, leave the area immediately, ventilate the room, and seek fresh air.

Q: How does ibuprofen affect the kidneys?

Ibuprofen is generally safe for kidneys in healthy adults at recommended doses, but can cause damage with prolonged use or in vulnerable populations. It works by inhibiting prostaglandins — hormones that help regulate kidney blood flow. In people with existing kidney disease, heart failure, dehydration, or the elderly, this inhibition can reduce blood flow enough to cause acute kidney injury. Limit use to the shortest effective duration and stay well hydrated.

Training details

Parameter	Value
Base model	SmolLM2-360M-Instruct
Fine-tuning method	LoRA via peft
LoRA rank	16
LoRA alpha	32
LoRA dropout	0.05
Target modules	q_proj, k_proj, v_proj, o_proj
Trainable parameters	3,276,800 (0.90% of total)
Learning rate	2e-4
LR scheduler	cosine
Effective batch size	32
Epochs	3
Max sequence length	768
Training examples	3,082
Training time	~17 min on T4 GPU

Evaluation

Evaluated on 200 held-out examples from the MoleculeIQ test split:

Metric	Base SmolLM2-360M	MoleculeIQ Fine-tuned	Improvement
ROUGE-L	0.1349	0.1697	+25.8%
BERTScore F1	0.7835	0.8096	+3.3%

The fine-tuned model shows consistent improvement in answer structure and domain relevance. Qualitative review shows it reliably leads with conclusions and explains mechanisms — the base model does not follow this format.

Dataset

Trained on Hari5115/MoleculeIQ:

3,627 Q&A pairs across 268 molecules
6 categories: food additives, cosmetics, household chemicals, medications, environmental chemicals, natural compounds
Generated via Claude Haiku API with structured quality prompts
Deduplicated using semantic similarity (sentence-transformers)

Limitations

Trained on synthetic LLM-generated data — factual errors are possible
360M parameters limits reasoning depth on complex multi-step questions
Can occasionally produce confidently wrong safety classifications (hallucination)
Not a substitute for professional medical or toxicological advice
Coverage weighted toward Western consumer products