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Model: jiosephlee/assay-transfer-tool
Source: Original Platform

README.md

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+---
+license: apache-2.0
+base_model: jiosephlee/Intern-S1-mini-lm
+pipeline_tag: text-generation
+tags:
+- chemistry
+- drug-discovery
+- admet
+- assay-transfer
+- smiles
+library_name: transformers
+---
+
+# assay-transfer-tool
+
+A full-parameter SFT of the `Qwen3ForCausalLM` Intern-S1-mini language backbone
+(`jiosephlee/Intern-S1-mini-lm`) for **binary assay-transfer prediction** on
+small molecules. Given a molecule (SMILES) and a paired-choice prompt, the model
+answers with `(A)` or `(B)`.
+
+The model uses the Intern SMILES-aware tokenizer, so load with
+`trust_remote_code=True`.
+
+## Training
+
+- **Base model:** `jiosephlee/Intern-S1-mini-lm` (Qwen3ForCausalLM, vocab 153216)
+- **Dataset:** `jiosephlee/assay-transfer-intern` (158,429 train rows)
+- **Regime:** full-parameter BF16, packing + padding-free, gradient checkpointing,
+  Liger fused linear cross-entropy, `paged_adamw_8bit`
+- **Chat template:** enabled, thinking disabled; completions are `(A)` / `(B)`
+- **LR:** 2e-5 base, with SMILES input-embedding rows trained at 1.5x
+- **Max length:** 4096, 1 epoch
+
+This checkpoint is the best-validation snapshot (step 50), promoted by the
+assay-transfer callback on binary macro-F1.
+
+## Validation metrics (source_value split, n=1703)
+
+| Metric | Value |
+| --- | --- |
+| Binary macro-F1 | 0.6612 |
+| Binary accuracy | 0.7046 |
+| Parse rate | 1.00 |
+| assay_concept group-avg macro-F1 | 0.6321 |
+| tanimoto_bucket group-avg macro-F1 | 0.6316 |
+
+Per-assay macro-F1: Fa 0.532, Fg 0.593, Fh 0.704, oral_bioavailability 0.600,
+oral_exposure 0.732. Per-Tanimoto-bucket macro-F1: high 0.664, low 0.600.
+
+## Usage
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+tok = AutoTokenizer.from_pretrained("jiosephlee/assay-transfer-tool", trust_remote_code=True)
+model = AutoModelForCausalLM.from_pretrained("jiosephlee/assay-transfer-tool", torch_dtype="bfloat16")
+```