tanpo-retention-LoRA

Tanpo Retention (LoRA)

PEFT LoRA adapter that specializes LiquidAI/LFM2.5-1.2B-Instruct for retention workflows (trained via Unsloth hub id unsloth/LFM2.5-1.2B-Instruct).

Creator: d4rkninja
Collection: Tanpo — Domain Specialists

Tanpo is a family of compact domain-specialized business models for local / edge / inexpensive deployment. Different specialists cover different workflows.

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).

Full eval, examples, and responsible-use notes: d4rkninja/tanpo-retention.

Best For

• Onboarding, activation, churn-save, renewal, expansion, escalation, and winback workflows
• Customer-success playbooks and account action-plan drafts

Not Designed For

• Automated customer decisions, guaranteed retention outcomes, or unauthorized system access
• General coding or non-retention chat

Adapter settings (from adapter_config.json)

Evaluation (summary)

Largest gains were on escalation, renewals, and winback at 100%; identity_guardrails is +34.3 pp vs base. Trailed on cs_playbooks (−22.2 pp) and health_scores (−5.5 pp).

Internal DarkLab automated domain evaluation (directional). Same prompts and generation config for base vs fine-tune. Not an industry benchmark. See the merged card evaluation/ for artifacts and methodology.

Load with PEFT

from peft import PeftModel
from transformers import AutoModelForCausalLM, AutoTokenizer

base = "unsloth/LFM2.5-1.2B-Instruct"  # Unsloth-compatible of LiquidAI/LFM2.5-1.2B-Instruct
tokenizer = AutoTokenizer.from_pretrained(base)
model = AutoModelForCausalLM.from_pretrained(
    base, torch_dtype="auto", device_map="auto"
)
model = PeftModel.from_pretrained(model, "d4rkninja/tanpo-retention-LoRA")

Related

• Merged: d4rkninja/tanpo-retention
• GGUF: d4rkninja/tanpo-retention-GGUF — prefer Q4_K_M
• Dataset: d4rkninja/tanpo-retention-sft

Limitations

This is a compact domain adapter; verify outputs against account context and source systems. It is not a general-purpose or frontier model, and the automated evaluation is directional. The cs_playbooks and health_scores categories trail the base in the reported evaluation.

Responsible Use

Humans must review customer communications, retention offers, escalation plans, and consequential account decisions. Do not use for deception, harassment, discriminatory treatment, or unauthorized access.

License

license: other / license_name: lfm-1.0

This repository provides a LoRA adapter for use with LiquidAI/LFM2.5-1.2B-Instruct. The adapter does not waive the upstream 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.

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