Instructions to use ballsak/de-hallucinator with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ballsak/de-hallucinator with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("ballsak/de-hallucinator", dtype="auto") - Notebooks
- Google Colab
- Kaggle
| language: en | |
| license: apache-2.0 | |
| tags: | |
| - slm | |
| - rag | |
| - hallucination-guard | |
| - logits-processor | |
| - pytorch | |
| - transformers | |
| # 🛡️ De-Hallucinator | |
| An inline token-probability uncertainty guard and semantic fact-checking engine for Small Language Models (SLMs). | |
| `De-Hallucinator` extends the Hugging Face `LogitsProcessor` pipeline to intercept text generation token-by-token. The moment an SLM drops an uncertain token below a configured logprob threshold, the generation sequence halts instantly, triggers a quantized NLI cross-encoder factual pass against your grounding context, and forces an immediate End-of-Sentence (EOS) cutoff if the assertion fails. | |
| ## 🚀 Installation | |
| You can install the compiled wheel asset directly from this Hugging Face repository: | |
| ```bash | |
| pip install [https://huggingface.co/YOUR_HF_USERNAME/YOUR_REPO_NAME/resolve/main/de_hallucinator-0.1.0-py3-none-any.whl](https://huggingface.co/YOUR_HF_USERNAME/YOUR_REPO_NAME/resolve/main/de_hallucinator-0.1.0-py3-none-any.whl) |