1.8 KiB
1.8 KiB
license, base_model, pipeline_tag, tags, library_name
| license | base_model | pipeline_tag | tags | library_name | |||||
|---|---|---|---|---|---|---|---|---|---|
| apache-2.0 | jiosephlee/Intern-S1-mini-lm | text-generation |
|
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
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")