Zenith is designed as a private AI tool for CMOs and growth leaders to safely test marketing ideas, strategy frameworks, and messaging without exposing sensitive data or relying on third-party models.
Why This Matters
Marketing is not broken. Your system is.
Most agencies sell channel execution. They don't build the systems that drive revenue. Zenith is built on a different philosophy — one system, one strategy, one outcome: profitable growth. No silos. No fragmentation. No channel-first thinking.
This is a CMO Ready Agent for leaders who want to:
Test new marketing ideas privately before committing budget
Validate strategy frameworks with an AI trained on real-world expertise
Move faster without exposing sensitive data to external APIs
Think bigger with a thinking partner that understands growth at scale
Training Data Breakdown
Zenith v0.1 was trained on a highly curated selection representing just 1% of our total available data — carefully chosen to capture the highest-signal content across Mike Zima's professional expertise. The remaining 99% was excluded to ensure quality, relevance, and a focused knowledge base.
The training corpus draws from the following sources:
Source
Percentage of Training Data
LinkedIn posts & articles
30%
Blog content & website copy
25%
Internal strategy documents & playbooks
20%
Presentations & slide decks
15%
Video content (recordings, talks)
10%
Expertise Areas Covered
The model was fine-tuned on Mike Zima's deep knowledge across:
Growth marketing & performance — paid media strategy, conversion optimization, analytics-driven decision making
Brand development — brand identity, messaging architecture, competitive differentiation
DIY Installation Guide (macOS & Linux)
Getting Zenith up and running locally is straightforward. Here's how to install it for the first time:
Prerequisites
Python 3.10+
Hugging Face account (free) and a valid access token
At least 16GB RAM (32GB recommended for smooth inference)
Step 1: Install Unsloth (Recommended)
The easiest way to get started is with the official pip package. It pulls in all required dependencies automatically:
pip install unsloth
For GPU support (NVIDIA), Unsloth will auto-detect and configure CUDA. For Apple Silicon (M1/M2/M3), it uses the native Metal backend — no extra setup needed.
Step 2: Authenticate with Hugging Face
Log in to access the model weights:
huggingface-cli login
Step 3: Load & Run the Model
Use this quick Python script to load Zenith locally:
python
1from unsloth import FastLanguageModel
2import torch
34model, tokenizer = FastLanguageModel.from_pretrained(5 model_name="zimamedia/zenith-v0.1",# or your hosted repo6 max_seq_length=2048,7 load_in_4bit=True,8)910FastLanguageModel.for_inference(model)1112messages =[13{"role":"user","content":"Write a go-to-market strategy for a SaaS product."}14]1516input_ids = tokenizer.apply_chat_template(messages, return_tensors="pt").to(model.device)17output = model.generate(input_ids, max_new_tokens=512, use_cache=True)18print(tokenizer.decode(output[0], skip_special_tokens=True))
That's it. You now have a private, local CMO Ready Agent running on your machine.
Credits
This model was trained 2× faster using Unsloth and Hugging Face's TRL library.
Built with ❤️ by Zima Media — The Revenue Engine for eCommerce Leaders.
