zenlm

zen-scribe

README

License: apache-2.0

Model Specs

Table with columns: Property, Value
Property	Value
Parameters	4B
Architecture	Transformer (decoder-only)
Context Window	32,768 tokens
Output Format	Text
License	Apache 2.0
HuggingFace	zenlm/zen-scribe

Quick Start

python
from transformers import AutoModelForCausalLM, AutoTokenizer
import torch

model = AutoModelForCausalLM.from_pretrained(
    "zenlm/zen-scribe",
    torch_dtype=torch.bfloat16,
    device_map="auto"
)
tokenizer = AutoTokenizer.from_pretrained("zenlm/zen-scribe")

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))

Use Cases

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

Content Pipeline Integration

Zen Scribe integrates with Hanzo Flow 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:

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

Apache 2.0. See 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 by Hanzo AI (Techstars '17) and Zoo Labs Foundation.

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.

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.

Available on FriendliAI

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Model Details

Model Provider

zenlm

Model Tree

Base

this model

Input Modalities

Audio

Output Modalities

Text

Supported Functionality