GPT-OSS 20B Chemical Synthesis Classifier - LoRA Adapter

• chemistry
• synthesis
• classification
• gpt-oss
• qlora
• unsloth
• mxfp4 metrics:
• TPR
• TNR

• en library_name: unsloth inference: false

Model Overview

• Task: Binary classification (P = synthesizable, N = unsynthesizable)
• Training Objective: QLoRA with focal loss (gamma = 2.0, alpha_P = 8.12, alpha_U = 1.0)
• Sequence Length: 180 tokens
• Dataset: train (train_llm_pn.jsonl) / validation (valid_llm_pn.jsonl)
• Hardware: NVIDIA RTX A6000 48GB

Validation Metrics (Epoch 3 - Best)

Dataset Sources

• P/U labelled data from J. Am. Chem. Soc. 2024, 146, 29, 19654-19659 (doi:10.1021/jacs.4c05840).
• High-entropy materials data from Data in Brief 2018, 21, 2664-2678 (doi:10.1016/j.dib.2018.11.111).
• Additional candidates retrieved via Scopus API queries and manual screening of high entropy materials literature.

VRAM & System Requirements

• GPU VRAM: 12-14 GB (T4 / L4 / A10G class works). The base model loads in 4-bit MXFP4 via Unsloth.
• RAM: >=16 GB recommended for tokenization and batching.
• Libraries: unsloth>=2025.9.6, transformers>=4.56.0, peft>=0.17.1, bitsandbytes>=0.47.0.
• CPU-only inference is not supported with this adapter (MXFP4 requires GPU).

Limitations & Notes

• MXFP4 4-bit weights require a GPU; CPU-only inference is not supported.
• The dataset targets chemical synthesizability. Generalization outside this domain is not guaranteed.