Instructions to use ITcoder/SHIFT with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ITcoder/SHIFT with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="ITcoder/SHIFT")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("ITcoder/SHIFT", dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use ITcoder/SHIFT with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "ITcoder/SHIFT" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "ITcoder/SHIFT", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/ITcoder/SHIFT
- SGLang
How to use ITcoder/SHIFT with SGLang:
Install from pip and serve model
# Install SGLang from pip: pip install sglang # Start the SGLang server: python3 -m sglang.launch_server \ --model-path "ITcoder/SHIFT" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "ITcoder/SHIFT", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker images
docker run --gpus all \ --shm-size 32g \ -p 30000:30000 \ -v ~/.cache/huggingface:/root/.cache/huggingface \ --env "HF_TOKEN=<secret>" \ --ipc=host \ lmsysorg/sglang:latest \ python3 -m sglang.launch_server \ --model-path "ITcoder/SHIFT" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "ITcoder/SHIFT", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use ITcoder/SHIFT with Docker Model Runner:
docker model run hf.co/ITcoder/SHIFT
Add model card, update pipeline tag and library name (#1)
Browse files- Add model card, update pipeline tag and library name (b77501135edf81deceec2d9b6f2e556566d297d2)
Co-authored-by: Niels Rogge <nielsr@users.noreply.huggingface.co>
README.md
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license: apache-2.0
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language:
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---
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language:
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license: apache-2.0
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pipeline_tag: text-generation
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library_name: transformers
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---
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# SHIFT: Gate-Modulated Activation Steering for Knowledge Conflict Mitigation in Retrieval-Augmented Generation
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This repository contains the model checkpoints for **SHIFT**, a lightweight framework designed to resolve knowledge conflicts in retrieval-augmented generation (RAG).
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- **Paper:** [SHIFT: Gate-Modulated Activation Steering for Knowledge Conflict Mitigation in Retrieval-Augmented Generation](https://huggingface.co/papers/2606.27786)
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- **Repository:** [GitHub - OpenBMB/SHIFT](https://github.com/OpenBMB/SHIFT)
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## Method Overview
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SHIFT reformulates neuron-level modification as a learnable gate modulation, allowing LLMs to adaptively regulate internal activations for knowledge conflict resolution. Technically, SHIFT equips LLMs with a lightweight gate module and optimizes fewer than 0.01% trainable parameters while keeping the backbone model frozen. During generation, the gate module adjusts the model's internal representations to adaptively leverage contextual and parametric knowledge.
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## Setup and Usage
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Please refer to the official [GitHub Repository](https://github.com/OpenBMB/SHIFT) for detailed environment setup, training, and evaluation scripts.
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## Citation
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If you find this work useful, please cite the paper:
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```bibtex
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@misc{li2026shiftgatemodulatedactivationsteering,
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title={SHIFT: Gate-Modulated Activation Steering for Knowledge Conflict Mitigation in Retrieval-Augmented Generation},
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author={Ruochang Li and Pengcheng Huang and Zhenghao Liu and Yukun Yan and Huiyuan Xie and Yu Gu and Ge Yu and Maosong Sun},
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year={2026},
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eprint={2606.27786},
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archivePrefix={arXiv},
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primaryClass={cs.CL},
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url={https://arxiv.org/abs/2606.27786},
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}
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```
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