StarLiu714/GPT-oss-MOFinder

GPT-oss-MOF

GPT-oss-MOF is a post finetuned GPT-oss-20B causal language model for predicting whether reaction conditions yield a crystalline metal–organic framework (MOF). This release contains the model weight shards, tokenizer, training/holdout data, and local inference and continuation scripts. The 13 BF16 safetensors shards total approximately 41.8 GB, where the original LoRA adapter is already included.

Setup

Use Python 3.10+ and install script/requirements.txt, which pins Transformers 4.56.2 and PEFT 0.17.1. Use PyTorch 2.6+ with a CUDA build suitable for your GPU. Continuation training uses CUDA/BF16. Allow memory beyond the 41.8 GB of weights for activations and training state. Lower batch sizes if needed.

GPU architecture: use NVIDIA Ampere (compute capability 8.0+) or newer for native BF16/TF32: A100/RTX A6000 (Ampere), RTX 6000 Ada (Ada), H100/H200 (Hopper), or RTX PRO 6000 Blackwell 96 GB.

Single-GPU BF16 hardware and batch-size reference settings for reaction prompts whose actual maximum length is 370 tokens, counting the complete rendered prompt, including system instructions and chat-template tokens. Memory use varies with the software stack and training settings.

Workload	GPU	Batch size	Actual prompt length (tokens)
Inference	A100 / H100 80 GB	24	370
	RTX PRO 6000 96 GB	48	370
LoRA continuation	A100 / H100 80 GB	Start at train 12 / eval 24; increase gradient accumulation as needed	370
	RTX PRO 6000 96 GB	Train 18 / eval 48	370

The scripts' --max-length defaults to 2048 and is an input validation limit: longer inputs are rejected rather than truncated. Check your data and reduce batch size when using longer prompts.

Recommend downloading the full repository with the Hugging Face CLI:

python -m pip install -U huggingface_hub
hf download StarLiu714/GPT-oss-MOF --local-dir GPT-oss-MOF
cd GPT-oss-MOF
python -m pip install -r script/requirements.txt

Run subsequent commands from this model directory. Script data/output paths are relative to --model-dir (this directory by default); absolute paths also work.

BF16 configuration: this export is normalized as a dense BF16 checkpoint. MXFP4 runtime kernels or quantized expert weight loading are not required.

Inference

As an example, to predict on the supplied holdout set,

python script/inference_pn.py \
  --input data/mof_ft_holdout.jsonl \
  --output outputs/holdout_predictions.jsonl \
  --batch-size 1

It predicts P with probability, using temperature 1 by default.

For your own inputs, use chat-format JSONL matching the supplied data, or one reaction object per line, for example,

{
  "metal_precursor":"ZrOCl2·8H2O", "organic_linker":"terephthalic acid","modulator":"acetic acid", "solvent":"dimethylformamide","metal_concentration_mM":30.0, "M_L_ratio":1.0, "temperature_C":120.0,"time_h":24.0
  }

Optional gold labels enable scoring. For comparisons, preserve the exact system prompt and field formatting in the supplied messages records。 Bare reaction objects use the script's default prompt.

Data Split

File	P	N	Total
Train	11,968	11,560	23,528
Holdout	1,320	1,275	2,595

Different clusters from the same publication can occur in both sets. See the dataset guide for provenance and regeneration.

Continue training

To start a new LoRA run on our model, please first attempt the released learning-rate and LoRA recipe:

python script/continue_lora_pn.py \
  --config script/source_training_recipe_step4500.json \
  --train-jsonl data/mof_ft_train.jsonl \
  --valid-fraction 0.1 \
  --output-dir runs/continue_lora

CLI flags override the JSON recipe. Without --config, defaults are learning rate 4e-6, 5000 steps, and halving milestones 1000,2500,4000. Adjust --batch-size, --eval-batch-size, --gradient-accumulation, and --gradient-checkpointing for memory.

Reference training parameters

The source run's recorded settings are retained in mof_export_manifest.json.

Setting	Source run
Base / objective	GPT-oss-20B / P–N classification through LM logits
LoRA	r=32, alpha=64, dropout=0.05; q_proj,k_proj,v_proj,o_proj
Learning rate	1e-5; step halving at 1200, 3200, 6000, 8500
Warmup / weight decay	0.02 / 0
Train / eval batch; gradient accumulation	18 / 48; 1
Length / truncation	2048 / disabled
Training budget	max_steps=10000, epochs=3.0 recorded
Loss settings	N weight=1.02; label smoothing=0.02; focal gamma=0
Seed / gradient checkpointing	777 / disabled
Evaluation / logging interval	100 / 25 steps

The default continuation recipe re-uses these optimization settings with the simpler validation-based selection described above. The scripts support fresh evaluation and further adaptation.

Citation and license

When using these weights, cite MOFinder, identify StarLiu714/GPT-oss-MOF and the Hub revision used, and reference the GPT-oss-20B base model. This release is licensed under the GNU General Public License v3.0; see LICENSE. The underlying GPT-oss-20B model retains its upstream Apache-2.0 license, reproduced in LICENSE.base-model. This release includes MOF fine-tuning changes merged into the base model and a BF16 weight export. MOFinder codebase is separately licensed under MIT.