zimamedia/zenith-v0.1-f16

Zenith v0.1 — CMO Ready Agent by Zima Media

• —Developed by: Zima Media
• —License: apache-2.0
• —Finetuned from model: unsloth/gemma-4-E2B-it

What is Zenith?

Zenith v0.1 is a CMO Ready Agent powered by the expertise of Mike Zima, Chief Marketing Officer at Zima Media.

This model captures Mike's deep knowledge across:

• —Marketing strategy & planning — go-to-market, brand positioning, campaign architecture
• —Sales prospecting & lead generation — B2B outreach, CRM workflows, data-driven acquisition
• —Content marketing & storytelling — narrative frameworks, thought leadership, audience engagement
• —Agency operations & client management — scaling services, team leadership, client success

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:

Expertise Areas Covered

The model was fine-tuned on Mike Zima's deep knowledge across:

• —Marketing strategy & planning — go-to-market frameworks, brand positioning, campaign architecture, budget allocation
• —Sales prospecting & lead generation — B2B outreach strategies, CRM workflows, data-driven acquisition, list building
• —Content marketing & storytelling — narrative frameworks, thought leadership, audience engagement, SEO content strategy
• —Agency operations & client management — scaling services, team leadership, client success frameworks, pricing models
• —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:

from unsloth import FastLanguageModel
import torch

model, tokenizer = FastLanguageModel.from_pretrained(
    model_name="zimamedia/zenith-v0.1", # or your hosted repo
    max_seq_length=2048,
    load_in_4bit=True,
)

FastLanguageModel.for_inference(model)

messages = [
    {"role": "user", "content": "Write a go-to-market strategy for a SaaS product."}
]

input_ids = tokenizer.apply_chat_template(messages, return_tensors="pt").to(model.device)
output = model.generate(input_ids, max_new_tokens=512, use_cache=True)
print(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](https://github.com/unslothai/unsloth) and Hugging Face's TRL library.

Built with ❤️ by Zima Media — The Revenue Engine for eCommerce Leaders.

<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>