Instructions to use AviadCoh/safetensors-diff-mfv-20260601 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use AviadCoh/safetensors-diff-mfv-20260601 with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("AviadCoh/safetensors-diff-mfv-20260601", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Configuration Parsing Warning:In config.json: "architectures" must be an array
Benign SafeTensors Security PoC
This repository is a tiny benign security proof of concept for authorized scanner/runtime differential testing.
It intentionally contains:
- a benign root
model.safetensors; - a
config.jsonfield,transformers_weights, that selectsmodel.safetensors.index.jsonat Transformers runtime; - two valid SafeTensors shard files selected by that index.
The only payload is deterministic output manipulation in a two-token toy BERT model. There is no remote code, pickle payload, shell execution, exfiltration, network callback, or destructive behavior.
Do not deploy this model for real inference. It exists only so reviewers can
compare Hugging Face production metadata and scan state with
AutoModel.from_pretrained runtime file selection.
- Downloads last month
- -
Inference Providers NEW
This model isn't deployed by any Inference Provider. 🙋 Ask for provider support