Instructions to use matsci-llm-anon/Mixtral-ft-MatKG with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use matsci-llm-anon/Mixtral-ft-MatKG with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("mistralai/Mixtral-8x7B-Instruct-v0.1") model = PeftModel.from_pretrained(base_model, "matsci-llm-anon/Mixtral-ft-MatKG") - Notebooks
- Google Colab
- Kaggle
Mixtral-ft-MatKG
LoRA adapter for materials knowledge-graph link prediction.
Released for double-blind review of How Large Language Models Learn Materials Science.
| Base model | mistralai/Mixtral-8x7B-Instruct-v0.1 |
| PEFT type | LoRA (r=8, alpha=16, dropout=0.05) |
| Target modules | q_proj, v_proj |
Usage
from peft import PeftModel
from transformers import AutoModelForCausalLM
m = AutoModelForCausalLM.from_pretrained("mistralai/Mixtral-8x7B-Instruct-v0.1")
m = PeftModel.from_pretrained(m, "matsci-llm-anon/Mixtral-ft-MatKG")
Citation
@inproceedings{anonymous2026learn,
title={How Large Language Models Learn Materials Science},
author={Anonymous},
booktitle={Under review},
year={2026}
}
Author and affiliation information is withheld during double-blind review.
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