66 lines
2.3 KiB
Markdown
66 lines
2.3 KiB
Markdown
---
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license: apache-2.0
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base_model:
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- ByteDance-Seed/Seed-OSS-36B-Instruct
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pipeline_tag: text-generation
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---
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# YanLabs/Seed-OSS-36B-Instruct-MPOA
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This is an abliterated version of [ByteDance-Seed/Seed-OSS-36B-Instruct](https://huggingface.co/ByteDance-Seed/Seed-OSS-36B-Instruct) using the norm-preserving biprojected abliteration technique.
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**⚠️ Warning**: Safety guardrails and refusal mechanisms have been removed through abliteration. This model may generate harmful content and is intended for mechanistic interpretability research only.
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## Model Details
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### Model Description
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This model applies **norm-preserving biprojected abliteration** to remove refusal behaviors while preserving the model's original capabilities. The technique surgically removes "refusal directions" from the model's activation space without traditional fine-tuning.
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- **Developed by**: YanLabs
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- **Model type**: Causal Language Model (Transformer)
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- **License**: apache-2.0
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- **Base model**: [ByteDance-Seed/Seed-OSS-36B-Instruct](https://huggingface.co/ByteDance-Seed/Seed-OSS-36B-Instruct)
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### Model Sources
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- **Base Model**: [ByteDance-Seed/Seed-OSS-36B-Instruct](https://huggingface.co/ByteDance-Seed/Seed-OSS-36B-Instruct)
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- **Abliteration Tool**: [jim-plus/llm-abliteration](https://github.com/jim-plus/llm-abliteration)
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- **Paper**: [Norm-Preserving Biprojected Abliteration](https://huggingface.co/blog/grimjim/norm-preserving-biprojected-abliteration)
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## Uses
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### Intended Use
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- **Research**: Mechanistic interpretability studies
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- **Analysis**: Understanding LLM safety mechanisms
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- **Development**: Testing abliteration techniques
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### Out-of-Scope Use
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- ❌ Production deployments
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- ❌ User-facing applications
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- ❌ Generating harmful content for malicious purposes
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## Limitations
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- Abliteration does not guarantee complete removal of all refusals
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- May generate unsafe or harmful content
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- Model behavior may be unpredictable in edge cases
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- No explicit harm prevention mechanisms remain
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## Citation
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If you use this model in your research, please cite:
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```bibtex
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@misc{Seed-OSS-36B-Instruct-MPOA,
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author = {YanLabs},
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title = {Seed-OSS-36B-Instruct-MPOA},
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year = {2025},
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publisher = {HuggingFace},
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howpublished = {\url{https://huggingface.co/YanLabs/Seed-OSS-36B-Instruct-MPOA}},
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note = {Abliterated using norm-preserving biprojected technique}
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} |