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chem_sage_32b_v4
Dellboy/chem_sage_32b_v4
chem_sage_32b_v4 is a text generation model from Dellboy. Use it when you need the model to write or continue text. It is set up for mlx. The card lists the license as apache-2.0.
ChemSage is a QLoRA-tuned Qwen2.5-32B chemistry assistant for drug discovery, fine-tuned on Apple Silicon with MLX-LM. It reasons about small molecules, emits verified RDKit and PyMOL tool calls, and is grounded by a…
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From the Hugging Face model README
ChemSage 32B v3 — Round 4 fused model
ChemSage is a QLoRA-tuned Qwen2.5-32B chemistry assistant for drug discovery, fine-tuned on Apple Silicon with MLX-LM. It reasons about small molecules, emits verified RDKit and PyMOL tool calls, and is grounded by a RAG layer over a curated cheminformatics corpus.
This is the Round 4 fused model — the current production checkpoint, fused from iter 950 (best val loss 0.041 of 1,500 iters).
Model details
| Base model | mlx-community/Qwen2.5-32B-Instruct-4bit |
| Fine-tune method | QLoRA (RSLoRA, rank 32, scale 64.0) |
| Adapted layers | 32 of 64 transformer layers |
| Trainable parameters | 134M (0.41% of 32.8B) |
| Training examples | 6,400 (from 8,000 total; 80/10/10 split) |
| Behaviour classes | 59 |
| Training iters | 1,500 (ran to completion) |
| Fused checkpoint | iter 950 (best val loss) |
| Best val loss | 0.041 (at iter 950; ties at iter 1425) |
| Final train loss | 0.043 (iter 1500) |
| Peak memory | 25.5 GB |
| Fused model size | ~17.2 GB |
| Training duration | ~43 h (2026-06-25 to 2026-06-27) |
Evaluation (5-round comparative, 2026-06-29)
Evaluated on 100 shared R5 test examples (seed=42) via eval/compare/eval_compare.py.
Scores = examples where all instances correct / 100 examples (per-example pass/fail).
Full report: eval/compare/results/compare_20260629_1928.html.
| Metric | Round 1 | Round 2 | Round 3 | Round 4 | Round 5 |
|---|---|---|---|---|---|
| SMILES validity | 100% | 100% | 100% | 100% | 100% |
| SMARTS validity | N/A | 0% | 100% | 55% | 100% |
| Tool executability | 0% | 28% | 79% | 71% | 95% |
| Code attempted | 14% | 41% | 100% | 97% | 100% |
| Python extended | 36% | 34% | 78% | 69% | 99% |
| Code-then-quote | N/A | 0% | 47% | 19% | 61% |
| Numerical fidelity | N/A | 18% | 57% | 47% | 89% |
| Rounding precision | 100% | 100% | 98% | 98% | 99% |
| Refusal accuracy | 98% | 98% | 97% | 98% | 100% |
| QED range | 100% | 100% | 100% | 100% | 100% |
| PDB ID validity | 100% | 100% | 100% | 100% | 100% |
| PyMOL syntax | 77% | 97% | 89% | 89% | 90% |
| Degeneration-free | 93% | 96% | 91% | 98% | 100% |
| Overall | 72% | 63% | 87% | 80% | 95% |
R4 regressed on exec metrics vs R3 due to dataset distribution shift (more PyMOL/structural content,
fewer pure-RDKit drills). R5 corrected this with pyexec_drill, code_then_quote_v2, and
fidelity_multistep generators — exec reached 95%, fidelity 89%.
Usage
# Serve the model (OpenAI-compatible endpoint)
mlx_lm.server --model models/chem_sage_32b_v4 --port 8081
# Or use the interactive chat CLI (RAG + RDKit auto-execution)
.venv/bin/python scripts/chat.py --model models/chem_sage_32b_v4
What it does well
- Emits correct, executable RDKit code for every physicochemical property query
- Drives PyMOL command scripts for structural visualisation (selections, alignment, surface, B-factor)
- Interprets PLIP interaction fingerprints in medchem terms
- Runs Biopython Bio.PDB patterns (SMCRA, NeighborSearch, Superimposer)
- Generates Plotly Python and R/ggplot2/bio3d visualisation scripts
- Grounds answers in the ChemSage corpus (PDB, ChEMBL, PubChem, approved drugs)
- Refuses out-of-scope or invalid queries (refusal training added in R4)
- Enforces "compute then quote" pattern: numerical values always quoted from code output
- Enforces rounding conventions: MW 1 d.p., logP 2 d.p., TPSA 1 d.p., QED 2 d.p.
Dataset (Round 4)
8,000 examples across 59 behaviour classes, including R4 additions:
| New class category | Classes | Purpose |
|---|---|---|
| Structural biology | PyMOL, PLIP, PDB format, pdb-tools, Biopython, ChimeraX, gemmi | Protein-ligand structural analysis |
| Structural prep | struct_prep, docking_interp | Structure preparation, docking result reading |
| Visualisation | plotly, r_recipe | Plotly Python + R/ggplot2/bio3d recipes |
| Protein families | protein_family | Real PDB data for kinases, GPCRs, antibodies, GTPases, cytokines |
| Numerical fidelity | code_then_quote, rounding_precision | Enforce compute-then-quote and exact rounding |
| Personality | personality_drug, personality_valid, personality_struct | Lipinski cards, SMILES flair, structural humour |
| Refusals | refusal | Out-of-scope query handling |
| Drug knowledge | drug_mechanisms, drug_indications, atc_classification | ChEMBL mechanism/indication/ATC data |
Training history
| Round | Val loss | Improvement |
|---|---|---|
| Round 1 (7B) | 0.389 | — |
| Round 2 (32B) | 0.347 | 10.8% over R1 |
| Round 3 (32B) | 0.054 | 6.4x over R2 |
| Round 4 (32B, this model) | 0.041 | 24% over R3 |
| Round 5 (32B) | 0.055 | early stop iter 2000; rank=64, 20k examples |
Succeeded by models/chem_sage_32b_v5/ — fused 2026-06-29 from iter 1600 checkpoint.
Compatibility note (2026-06-29)
tokenizer_config.json updated: extra_special_tokens converted from list to dict to fix
mlx_lm 0.31.x / transformers compatibility (AttributeError on load in older format).
Built by
Marc C. Deller, D.Phil. · marcdeller.com · [email protected]
- Task
- Text Generation
- Library
- mlx
- Type
- qwen2
- License
- apache-2.0
- Languages
- en
- Created
- Jun 30, 2026
- Updated
- Jun 30, 2026