fix: remove abliterated tag, clean branding
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README.md
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@@ -6,7 +6,6 @@ tags:
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- zen
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- zenlm
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- hanzo-ai
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- abliterated
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- multilingual
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- translation
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- CJK
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@@ -17,35 +16,28 @@ base_model: zenlm/zen-next-80b-instruct
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# Zen Multilingual
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> **Parameters**: 32B | **Context**: 128K | **License**: Apache 2.0 | **Released**: 2024-12-01
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Multilingual text generation: English, Chinese, Japanese, Korean, Arabic, Spanish, French, German, Portuguese, Russian.
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This model is abliterated — refusal behavior removed to enable unrestricted domain research.
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Base weights: [zenlm/zen-next-80b-instruct](https://huggingface.co/zenlm/zen-next-80b-instruct)
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## Abliteration
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Abliterated using representation engineering (Arditi et al., 2024 —
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*Refusal in Language Models Is Mediated by a Single Direction*, arXiv:2406.11717).
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Refusal direction orthogonalized out of the residual stream across all layers.
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All capabilities preserved; safety-trained refusals removed.
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**Intended uses**: Research, security testing, creative writing, red-teaming.
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**Still prohibited**: CSAM, targeted harassment, mass-casualty weapon synthesis.
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```python
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from transformers import AutoModelForCausalLM, AutoTokenizer
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model = AutoModelForCausalLM.from_pretrained("zenlm/zen-next-80b-instruct", torch_dtype="auto")
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tokenizer = AutoTokenizer.from_pretrained("zenlm/zen-next-80b-instruct")
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```
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---
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## The Zen LM Family
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Joint research collaboration:
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- **Hanzo AI** (Techstars 17) — AI infrastructure, API gateway, inference optimization
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- **Zoo Labs Foundation** (501c3) — Open AI research, ZIPs governance, decentralized training
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- **Lux Partners Limited** — Compute coordination and settlement layer
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- zen
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- zenlm
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- hanzo-ai
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- multilingual
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- translation
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- CJK
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# Zen Multilingual
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> **Parameters**: 32B | **Architecture**: Zen 4 Architecture | **Context**: 128K | **License**: Apache 2.0 | **Released**: 2024-12-01
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Multilingual text generation: English, Chinese, Japanese, Korean, Arabic, Spanish, French, German, Portuguese, Russian.
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Base weights: [zenlm/zen-next-80b-instruct](https://huggingface.co/zenlm/zen-next-80b-instruct)
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```python
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from transformers import AutoModelForCausalLM, AutoTokenizer
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model = AutoModelForCausalLM.from_pretrained("zenlm/zen-next-80b-instruct", torch_dtype="auto")
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tokenizer = AutoTokenizer.from_pretrained("zenlm/zen-next-80b-instruct")
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messages = [{"role": "user", "content": "Your domain-specific prompt here"}]
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text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
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inputs = tokenizer(text, return_tensors="pt").to(model.device)
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output = model.generate(**inputs, max_new_tokens=1024)
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print(tokenizer.decode(output[0][inputs.input_ids.shape[-1]:], skip_special_tokens=True))
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```
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---
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## The Zen LM Family
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Joint research collaboration:
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- **Hanzo AI** (Techstars '17) — AI infrastructure, API gateway, inference optimization
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- **Zoo Labs Foundation** (501c3) — Open AI research, ZIPs governance, decentralized training
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- **Lux Partners Limited** — Compute coordination and settlement layer
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