---
license: apache-2.0
base_model: unsloth/Qwen3-14B-unsloth-bnb-4bit
tags:
  - chemistry
  - synthesis
  - classification
  - qwen3
  - qlora
  - unsloth
  - peft
metrics:
  - TPR
  - TNR
language:
  - en
library_name: unsloth
inference: false
---

# Qwen3-14B Chemical Synthesis Classifier - LoRA Adapter

## Model Overview

This repository contains the LoRA adapter for a Qwen3-14B model fine-tuned to classify chemical synthesizability (P = synthesizable, U = unsynthesizable). Training uses a P/U-only focal loss. Prompts follow this template:

```
As chief synthesis scientist, judge {compound} critically. Avoid bias. P=synthesizable, U=not:
```

The base checkpoint is Unsloth’s 4-bit MXFP4 build (`unsloth/Qwen3-14B-unsloth-bnb-4bit`). Attaching this adapter reproduces the best validation performance among evaluated epochs (Epoch 2).

- Task: Binary classification (P = synthesizable, U = unsynthesizable)
- Training Objective: QLoRA with focal loss (gamma = 2.0, alpha_P = 8.12, alpha_U = 1.0)
- Max Sequence Length (train): 2048 tokens; Evaluation: 180 tokens
- Dataset: 316,442 train (`train_pu_hem.jsonl`) / 79,114 validation (`validate_pu_hem.jsonl`) samples (~11% P / 89% U)
- Adapter Size: ~981 MB (`adapter_model.safetensors`)

### Prompt & Thinking Prefill

Evaluation constructs prompts via the included chat template and pre-fills a lightweight thinking block to match SFT conditions. Effective structure:

- System: `You are a helpful assistant for P/U classification of synthesizability.`
- User: `As chief synthesis scientist, judge {compound} critically. Avoid bias. P=synthesizable, U=not:`
- Assistant (prefill): `<think>\n\n</think>\n\n`

Two equivalent setups in the evaluation script:
- Template-driven: `--use_checkpoint_chat_template --enable_thinking false --think_stub false` (recommended; the template inserts the stub).
- Manual stub: `--think_stub true` (forces appending `<think>…</think>` after the assistant start).

### Validation Metrics (Epoch 2 - Best)

| Metric | Value |
| --- | --- |
| TPR (P Recall) | **0.9562** |
| TNR (U Specificity) | 0.9001 |


## Dataset Sources

The training and validation splits combine multiple public sources and internal curation:

- 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 via literature queries and manual screening of high-entropy materials.

After de-duplication across all sources, approximately 2,560 unique compositions were appended to the base corpus. The combined dataset contains 316,442 training samples and 79,114 validation samples with an imbalanced label ratio (~11% P / 89% U).

## VRAM & System Requirements

- GPU VRAM: >=16 GB recommended for loading the 4-bit base with this adapter.
- RAM: >=16 GB recommended for tokenization and batching.
- Libraries: unsloth, transformers, peft, bitsandbytes.
- CPU-only inference is not supported with MXFP4 4-bit weights.

## Limitations & Notes

- This adapter targets chemical synthesizability judgments; generalization outside this domain is not guaranteed.
- For consistent results, use a chat template aligned with training (a `chat_template.jinja` is included in this checkpoint).