Molly Specialist — Polymer Chemist

Predicts copolymer composition from monomer reactivity ratios, recommends controlled polymerization conditions, and estimates thermal properties of synthesized polymers.

Part of Molly, an orchestrator that keeps a library of small domain specialists over one quantized base and routes each request to the right one, so a single machine answers across many fields without loading a separate large model for each.

What this specialist handles well

Predicts copolymer composition from monomer reactivity ratios
Recommends initiators and conditions for controlled radical polymerization
Estimates glass transition and decomposition temperatures for polymers

Try it with

"What monomer feed ratio gives a 50/50 copolymer of styrene and methyl methacrylate?"
"Which chain transfer agent best controls molecular weight in RAFT polymerization of acrylamides?"
"How does tacticity affect the glass transition temperature of poly(methyl methacrylate)?"

Before you run: the base model is gated

This adapter needs the base weights, and the base is access-gated. Do this once:

Accept the base licence: https://huggingface.co/meta-llama/Llama-3.1-8B-Instruct
Create a read token: https://huggingface.co/settings/tokens
Make the token available:
- Google Colab: Secrets panel (key icon) → Add new secret → name HF_TOKEN, enable Notebook access.
- Kaggle: Add-ons → Secrets → add HF_TOKEN.
- Local: huggingface-cli login or export HF_TOKEN=...

Skipping this gives GatedRepoError / 401 Unauthorized when the base loads. A stored Colab secret is not applied automatically — authenticate in code, as below.

Quickstart

# pip install -U transformers peft accelerate
import os, torch
from huggingface_hub import login
try:
    from google.colab import userdata
    login(userdata.get("HF_TOKEN"))
except Exception:
    tok = os.environ.get("HF_TOKEN")
    login(tok) if tok else login()

from transformers import AutoModelForCausalLM, AutoTokenizer
from peft import PeftModel

BASE = "meta-llama/Llama-3.1-8B-Instruct"
ADAPTER = "BoomJules/molly-polymer-chemist"

tok = AutoTokenizer.from_pretrained(BASE)
base = AutoModelForCausalLM.from_pretrained(BASE, torch_dtype=torch.bfloat16, device_map="auto")
model = PeftModel.from_pretrained(base, ADAPTER).eval()

msgs = [{"role": "user", "content": "Your question here"}]
ids = tok.apply_chat_template(msgs, add_generation_prompt=True, return_tensors="pt").to(model.device)
out = model.generate(ids, max_new_tokens=300)
print(tok.decode(out[0][ids.shape[1]:], skip_special_tokens=True))

Low-VRAM (4-bit) — fits a free Colab/Kaggle GPU (~6–7 GB)

# pip install -U transformers peft accelerate bitsandbytes
import os, torch
from huggingface_hub import login
try:
    from google.colab import userdata
    login(userdata.get("HF_TOKEN"))
except Exception:
    login(os.environ.get("HF_TOKEN"))

from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig
from peft import PeftModel

bnb = BitsAndBytesConfig(load_in_4bit=True, bnb_4bit_quant_type="nf4",
                         bnb_4bit_compute_dtype=torch.bfloat16, bnb_4bit_use_double_quant=True)
tok = AutoTokenizer.from_pretrained("meta-llama/Llama-3.1-8B-Instruct")
base = AutoModelForCausalLM.from_pretrained("meta-llama/Llama-3.1-8B-Instruct", quantization_config=bnb, device_map="auto")
model = PeftModel.from_pretrained(base, "BoomJules/molly-polymer-chemist").eval()

Adapter details


Base model	`meta-llama/Llama-3.1-8B-Instruct`
Method	LoRA (PEFT)
Rank / alpha	32 / 64
Domain	Polymer Chemist

Troubleshooting

GatedRepoError / 401 Unauthorized — base licence not accepted, or HF_TOKEN missing, or the Colab secret was stored but login(...) was never called.
CUDA out of memory — use the 4-bit snippet on a GPU runtime.
Adapter seems to have no effect — confirm the base id matches base_model above.

Other Molly specialists

Running several of these at once, with the routing decided for you, is what Molly does.

Licence & intended use

Adapter: CC BY-NC 4.0 (attribution, non-commercial). Base model: its own licence. Intended for research and evaluation in Polymer Chemist.

Downloads last month: 7

Model tree for BoomJules/molly-polymer-chemist

Base model

meta-llama/Llama-3.1-8B

Finetuned

meta-llama/Llama-3.1-8B-Instruct

Adapter

(2803)

this model