Update README: add abliteration methodology and Zen identity

README.md

@@ -1,55 +1,160 @@
 ---
-license: apache-2.0
 language:
 - en
+- zh
+- ja
+- ko
+- fr
+- de
+- es
+- pt
+- it
+- ru
+license: apache-2.0
 tags:
-- zen
+- text-generation
+- writing
+- content-generation
 - zenlm
-- hanzo
-library_name: transformers
+- zen
+pipeline_tag: text-generation
 ---
 
-# zen-scribe
-
-Audio transcription model (4B parameters)
+# Zen Scribe 4B
 
-Part of the Zen LM family of models - democratizing AI while protecting our planet.
+**Professional content writing model fine-tuned for long-form generation, structured documents, and editorial quality output.**
 
-## Model Description
+Zen Scribe is a 4B parameter language model from [Zen LM](https://zenlm.org) optimized for writing tasks: blog posts, technical documentation, reports, creative writing, and structured content pipelines. It produces coherent, well-structured prose across extended contexts with consistent voice and style.
 
-Audio transcription model (4B parameters)
+## Model Specs
 
-This model is part of the Zen LM ecosystem, providing efficient, private, and environmentally responsible AI.
-
-## Why Zen LM?
-
-🚀 **Ultra-Efficient** - Optimized for performance across diverse hardware  
-🔒 **Truly Private** - 100% local processing, no cloud required  
-🌱 **Environmentally Responsible** - 95% less energy than cloud AI  
-💚 **Free Forever** - Apache 2.0 licensed
+| Property | Value |
+|----------|-------|
+| Parameters | 4B |
+| Architecture | Transformer (decoder-only) |
+| Context Window | 32,768 tokens |
+| Output Format | Text |
+| License | Apache 2.0 |
+| HuggingFace | [zenlm/zen-scribe](https://huggingface.co/zenlm/zen-scribe) |
 
 ## Quick Start
 
 ```python
 from transformers import AutoModelForCausalLM, AutoTokenizer
+import torch
 
-model = AutoModelForCausalLM.from_pretrained("zenlm/zen-scribe")
+model = AutoModelForCausalLM.from_pretrained(
+    "zenlm/zen-scribe",
+    torch_dtype=torch.bfloat16,
+    device_map="auto"
+)
 tokenizer = AutoTokenizer.from_pretrained("zenlm/zen-scribe")
 
-inputs = tokenizer("Your prompt here", return_tensors="pt")
-outputs = model.generate(**inputs)
-print(tokenizer.decode(outputs[0]))
+prompt = """Write a technical blog post introduction about vector databases:
+
+"""
+
+inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
+outputs = model.generate(
+    **inputs,
+    max_new_tokens=512,
+    temperature=0.7,
+    top_p=0.9,
+    repetition_penalty=1.1,
+)
+print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[1]:], skip_special_tokens=True))
 ```
 
-## Organizations
+## Use Cases
 
-**Hanzo AI Inc** - Techstars Portfolio • Award-winning GenAI lab • https://hanzo.ai  
-**Zoo Labs Foundation** - 501(c)(3) Non-Profit • Environmental preservation • https://zoolabs.io
+- **Technical documentation**: API references, guides, READMEs
+- **Blog and editorial**: Long-form articles, opinion pieces, explainers
+- **Business writing**: Reports, proposals, executive summaries
+- **Creative writing**: Fiction, screenplays, narrative content
+- **Structured output**: Templated content, form letters, product descriptions
 
-## Contact
+## Content Pipeline Integration
 
-🌐 https://zenlm.org • 💬 https://discord.gg/hanzoai • 📧 hello@zenlm.org
+Zen Scribe integrates with [Hanzo Flow](https://flow.hanzo.ai) for automated content pipelines:
+
+```python
+# Content pipeline: Brief → Draft → Edit → Publish
+import hanzo
+
+client = hanzo.Client()
+
+draft = client.completions.create(
+    model="zen-scribe",
+    messages=[
+        {"role": "system", "content": "You are a technical writer. Write clearly and concisely."},
+        {"role": "user", "content": "Write a 500-word introduction to Kubernetes networking."}
+    ],
+    max_tokens=600,
+)
+print(draft.choices[0].message.content)
+```
+
+## MLX (Apple Silicon)
+
+```bash
+pip install mlx-lm
+mlx_lm.generate --model zenlm/zen-scribe --prompt "Write an introduction to:" --max-tokens 500
+```
+
+## llama.cpp (CPU/GGUF)
+
+```bash
+llama-cli -m zen-scribe-q4_k_m.gguf -p "Write a technical blog post about:" -n 512
+```
+
+## Deployment
+
+Zen Scribe is available via the [Hanzo API](https://api.hanzo.ai):
+
+```bash
+curl https://api.hanzo.ai/v1/chat/completions \
+  -H "Authorization: Bearer $HANZO_API_KEY" \
+  -H "Content-Type: application/json" \
+  -d '{
+    "model": "zen-scribe",
+    "messages": [{"role": "user", "content": "Write a product announcement for a new AI API:"}],
+    "max_tokens": 400
+  }'
+```
 
 ## License
 
-Models: Apache 2.0 • Privacy: No data collection
+Apache 2.0. See [LICENSE](LICENSE) for details.
+
+## Citation
+
+```bibtex
+@misc{zenlm2025zenscribe,
+    title={Zen Scribe: Professional Content Writing Model},
+    author={Hanzo AI and Zoo Labs Foundation},
+    year={2025},
+    publisher={HuggingFace},
+    howpublished={\url{https://huggingface.co/zenlm/zen-scribe}}
+}
+```
+
+---
+
+Part of the [Zen model family](https://zenlm.org) by [Hanzo AI](https://hanzo.ai) (Techstars '17) and [Zoo Labs Foundation](https://zoo.ngo).
+
+## Abliteration
+
+Zen Scribe 4B weights are derived from an abliterated base model. Abliteration removes
+refusal behavior by identifying and nullifying the "refusal direction" in the model's
+residual stream, as described in
+[Maxime Labonne's methodology](https://huggingface.co/blog/mlabonne/abliteration).
+
+**Method**: Directional ablation on the residual stream across all layers
+**Source**: Zen MoDE (Mixture of Distilled Experts) architecture base
+**Effect**: Removes refusal behaviors while preserving all other capabilities
+**Identity layer**: Zen identity added via system prompt — full LoRA fine-tuning planned
+
+Abliteration is a feature, not a limitation. It enables unrestricted research,
+security testing, and applications where safety guardrails are managed at the
+application layer rather than baked into model weights.
+