tanpo-retention

Tanpo Retention

A compact customer-retention specialist (~1.2B) for onboarding, activation, churn-save, customer-success playbooks, health-score follow-up, renewals, expansion, escalation, identity guardrails, and winback workflows — built for local and inexpensive deployment.

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 Retention is one specialist in that family (not a frontier or general-purpose model).

Related artifacts:

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

Best For

• Customer-success playbooks, renewal preparation, and risk follow-up
• Onboarding and activation plans tied to time-to-value
• Churn-save, winback, expansion, and escalation drafts
• Health-score interpretation prompts and practical account action plans

Not Designed For

• Fully automated account cancellation, pricing, or customer eligibility decisions
• Fabricating customer facts, impersonation, or unauthorized access to customer systems
• Guarantees of retention, renewal, expansion, or customer outcomes
• General coding or non-retention chat

Why a Specialist Model?

Retention work rewards repeatable structure (signals, diagnosis, intervention, owner, timing, and next step). Specializing a small model for those workflows enables private, low-cost inference without a large general model.

Evaluation

Internal automated domain evaluation (DarkLab harness). Treat as directional, not an industry benchmark.

Largest gains were on escalation, renewals, and winback at 100%; identity_guardrails is +34.3 percentage points vs base.

Trailed on cs_playbooks (−22.2 percentage points) and health_scores (−5.5 percentage points). Overall still ahead of base; these categories are disclosed.

Artifacts: evaluation/ — COMPARE_RETENTION.md.

Methodology: DarkLab automated domain evaluation. Same prompts and generation config for base vs fine-tune. Not an industry benchmark.

Limitations of this eval: Automated rubrics can reward structure over real-world quality; sample size is small; results may not transfer outside the task distribution.

Example Prompts

1. User: Build a 30-day onboarding and activation plan for a B2B SaaS account with a stalled implementation and three user roles.
2. User: Draft a renewal-risk action plan from these account signals, separating facts, hypotheses, owner, timing, and next customer conversation.
3. User: Create a respectful winback sequence for a customer who churned after an unresolved support escalation; do not invent account facts.

Inference

from transformers import AutoModelForCausalLM, AutoTokenizer

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

messages = [
    {"role": "system", "content": "You are Tanpo Retention, a practical customer-retention assistant."},
    {"role": "user", "content": "Draft a renewal-risk action plan from these account signals, separating facts, hypotheses, owner, timing, and next customer conversation."},
]
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-retention-sft — format-fixed chat SFT examples (documented on the dataset card).

Limitations

• Specialized: quality drops outside the retention and customer-success workflow distribution.
• ~1.2B scale: limited world knowledge and long-horizon reasoning vs larger models.
• Customer, health-score, renewal, and escalation recommendations can be wrong or incomplete; verify against source systems and account context.
• Eval gains are rubric-based and directional only; cs_playbooks and health_scores still trail the base.

Responsible Use

Not a substitute for customer-success judgment or account-system data. Humans must review customer communications, retention offers, escalation plans, and any consequential account decisions. Do not use for deception, harassment, unauthorized access, or discriminatory treatment.

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-retention
• LoRA: d4rkninja/tanpo-retention-LoRA
• GGUF: d4rkninja/tanpo-retention-GGUF

Browse all Tanpo specialists: Tanpo — Domain Specialists