Molly Specialist β€” Composite Materials Engineer

This adapter improves accuracy in predicting laminate stiffness matrices, identifying fiber-matrix failure modes, and selecting resin systems for specific load and temperature conditions.

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

  • Calculate laminate stiffness and compliance matrices using classical lamination theory
  • Identify delamination, fiber breakage, and matrix cracking failure modes from stress data
  • Select fiber-matrix-resin systems for target mechanical properties and environmental conditions

Try it with

  • "What layup sequence minimizes thermal residual stresses in a carbon-epoxy laminate?"
  • "How do I predict the shear modulus of a unidirectional glass-fiber composite?"
  • "Which resin system should I use for a composite part exposed to 200Β°C?"

Before you run: the base model is gated

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

  1. Accept the base licence: https://huggingface.co/meta-llama/Llama-3.1-8B-Instruct
  2. Create a read token: https://huggingface.co/settings/tokens
  3. Make the token available:
    • Google Colab: Secrets panel (key icon) β†’ Add new secret β†’ name HF_TOKEN, enable Notebook access.
    • Kaggle: Add-ons β†’ Secrets β†’ 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 β€” 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-composite-materials-engineer"

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) β€” 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-composite-materials-engineer").eval()

Adapter details

Base model meta-llama/Llama-3.1-8B-Instruct
Method LoRA (PEFT)
Rank / alpha 32 / 64
Domain Composite Materials Engineer

Troubleshooting

  • GatedRepoError / 401 Unauthorized β€” base licence not accepted, or HF_TOKEN missing, or the Colab secret was stored but login(...) was never called.
  • CUDA out of memory β€” use the 4-bit snippet on a GPU runtime.
  • Adapter seems to have no effect β€” 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 Composite Materials Engineer.

Β© 2026 Core Labs R&D.

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