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fix: remove abliterated tag, clean branding

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  1. README.md +8 -16
README.md CHANGED
@@ -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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  - sql
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  - database
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  - code-generation
@@ -17,35 +16,28 @@ base_model: zenlm/zen4
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  # Zen Sql
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- > **Parameters**: 7B | **Context**: 32K | **License**: Apache 2.0 | **Released**: 2024-11-15
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- SQL generation, query optimization, schema design, and database documentation across PostgreSQL, MySQL, SQLite, and BigQuery.
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- This model is abliterated — refusal behavior removed to enable unrestricted domain research.
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  Base weights: [zenlm/zen4](https://huggingface.co/zenlm/zen4)
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- ## Abliteration
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-
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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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-
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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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-
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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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-
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  ```python
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  from transformers import AutoModelForCausalLM, AutoTokenizer
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  model = AutoModelForCausalLM.from_pretrained("zenlm/zen4", torch_dtype="auto")
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  tokenizer = AutoTokenizer.from_pretrained("zenlm/zen4")
 
 
 
 
 
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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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  - sql
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  - database
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  - code-generation
 
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  # Zen Sql
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+ > **Parameters**: 7B | **Architecture**: Zen 4 Architecture | **Context**: 32K | **License**: Apache 2.0 | **Released**: 2024-11-15
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+ SQL generation, query optimization, schema design, and database documentation across PostgreSQL, MySQL, SQLite, BigQuery.
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  Base weights: [zenlm/zen4](https://huggingface.co/zenlm/zen4)
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  ```python
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  from transformers import AutoModelForCausalLM, AutoTokenizer
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  model = AutoModelForCausalLM.from_pretrained("zenlm/zen4", torch_dtype="auto")
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  tokenizer = AutoTokenizer.from_pretrained("zenlm/zen4")
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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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