tanpo-marketing

Tanpo Marketing

A compact marketing / GTM specialist (~1.2B) for hooks, campaigns, email, ICP messaging, and launch copy — built for local and inexpensive deployment, not as a general-purpose chatbot.

Creator: d4rkninja
Collection: Tanpo — Domain Specialists

Original upstream: LiquidAI/LFM2.5-1.2B-Instruct (~1.17B parameters, 32,768-token context, designed for edge/on-device deployment).

Fine-tuning: Unsloth-compatible loading of that checkpoint via hub id unsloth/LFM2.5-1.2B-Instruct (LoRA / PEFT).

This repository hosts the merged Transformers weights (LoRA merged into the base).

Overview

Tanpo is a family of compact domain-specialized business models for local / edge / inexpensive deployment. Different specialists cover different workflows. Tanpo Marketing is one specialist in that family (not a frontier or general-purpose model).

Related artifacts:

• LoRA adapter: d4rkninja/tanpo-marketing-LoRA
• GGUF quants: d4rkninja/tanpo-marketing-GGUF — prefer Q4_K_M when available

Best For

• Ad hooks, headlines, and short-form creative angles
• Cold email / outbound sequences and landing-page heroes
• ICP messaging, positioning, and campaign briefs
• Launch announcements, social captions, objection handling, and pricing/packaging copy

Not Designed For

• General coding, math, or knowledge-work outside marketing/GTM
• Legal review of claims, trademarks, or regulated advertising compliance
• Replacing brand strategy or human creative direction
• Frontier-scale open-ended reasoning across unrelated domains

Why a Specialist Model?

Small general models spread capacity thin. Tanpo Marketing is fine-tuned for marketing workflow shapes (structure, specificity, CTAs, ICP language) so a ~1.2B on-device model can be useful where a larger general model is unnecessary.

Evaluation

Internal automated evaluations. Treat as directional, not industry benchmarks.

Headline — DarkLab 20-task domain harness

Artifacts: evaluation/ — marketing_tasks.jsonl, score_rubric.md, evaluation/README.md.

Methodology: Automated domain rubric on ~20 held-out marketing tasks; same prompts/decoding for base vs fine-tune.

Also published — public capability suite (33 tasks, strict rubric v2)

Different suite (not used for the headline table): base 94.3% → fine-tune 96.1% (+1.8 pp).

Artifacts: benchmark/ — scorecard.json, SCORECARD.md, tasks.jsonl, score_rubric.md, results/, charts/.

Limitations: Automated rubrics can reward structure over real-world quality; sample sizes are small; results may not transfer outside each task distribution.

Example Prompts

1. User: Write three distinct Facebook ad hooks for a B2B analytics tool aimed at mid-market ops leaders. Each hook ≤12 words; include a concrete benefit.
2. User: Draft a cold email (subject + body) for a founder selling a scheduling API to clinic ops managers. One clear CTA.
3. User: Rewrite this landing hero for clarity and ICP fit. Product: inventory forecasting for independent retailers. Audience: owners with 1–5 stores.

Inference

from transformers import AutoModelForCausalLM, AutoTokenizer

repo = "d4rkninja/tanpo-marketing"
tokenizer = AutoTokenizer.from_pretrained(repo)
model = AutoModelForCausalLM.from_pretrained(
    repo,
    torch_dtype="auto",
    device_map="auto"
)

messages = [
    {"role": "system", "content": 'You are Tanpo Marketing, a practical GTM copy assistant.'},
    {"role": "user", "content": 'Write three distinct Facebook ad hooks for a B2B analytics tool aimed at mid-market ops leaders. Each hook ≤12 words; include a concrete benefit.'},
]
inputs = tokenizer.apply_chat_template(
    messages, add_generation_prompt=True, return_tensors="pt"
).to(model.device)
out = model.generate(inputs, max_new_tokens=512)
print(tokenizer.decode(out[0][inputs.shape[-1]:], skip_special_tokens=True))

Training

Verified from published adapter configs / training artifacts (no unverified hyperparams):

Merged via PEFT merge_and_unload into full weights in this repo.

Dataset

• d4rkninja/tanpo-marketing-sft — ~10k chat SFT examples (documented on the dataset card).

Limitations

• Specialized: quality drops outside the marketing workflow distribution.
• ~1.2B scale: limited world knowledge and long-horizon reasoning vs larger models.
• May refuse or redirect clearly off-domain asks. Creative taste is subjective; automated rubric gains do not equal human preference.
• Eval gains are rubric-based and directional only.

Responsible Use

Do not use for deceptive advertising, false claims, dark patterns, or undisclosed synthetic endorsements. Humans remain responsible for brand, legal, and platform-policy compliance.

License

license: other / license_name: lfm-1.0

Tanpo merged and GGUF weights are derivatives of LiquidAI/LFM2.5-1.2B-Instruct under the LFM Open License v1.0 (including the commercial Threshold of approximately $10M annual revenue). See the base model card and its LICENSE file. Credit: LiquidAI. Do not treat this stack as Apache-2.0.

Tanpo Family

Tanpo is a family of compact domain-specialized models for focused business workflows.

This specialist is available as:

• Merged: d4rkninja/tanpo-marketing
• LoRA: d4rkninja/tanpo-marketing-LoRA
• GGUF: d4rkninja/tanpo-marketing-GGUF

Browse all Tanpo specialists: Tanpo — Domain Specialists