Instructions to use d4rkninja/tanpo-retention with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use d4rkninja/tanpo-retention with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="d4rkninja/tanpo-retention")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("d4rkninja/tanpo-retention")
model = AutoModelForCausalLM.from_pretrained("d4rkninja/tanpo-retention", device_map="auto")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use d4rkninja/tanpo-retention with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "d4rkninja/tanpo-retention"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "d4rkninja/tanpo-retention",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/d4rkninja/tanpo-retention

SGLang

How to use d4rkninja/tanpo-retention with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "d4rkninja/tanpo-retention" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "d4rkninja/tanpo-retention",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "d4rkninja/tanpo-retention" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "d4rkninja/tanpo-retention",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use d4rkninja/tanpo-retention with Docker Model Runner:
```
docker model run hf.co/d4rkninja/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.

Model	Rubric overall
Base `LFM2.5-1.2B-Instruct`	94.2%
tanpo-retention	96.0%
Delta	+1.8 percentage points

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

User: Build a 30-day onboarding and activation plan for a B2B SaaS account with a stalled implementation and three user roles.
User: Draft a renewal-risk action plan from these account signals, separating facts, hypotheses, owner, timing, and next customer conversation.
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):

Field	Value
Method	LoRA (PEFT) via Unsloth FastLanguageModel on hub id `unsloth/LFM2.5-1.2B-Instruct` (Unsloth-compatible of LiquidAI/LFM2.5-1.2B-Instruct)
LoRA rank (`r`)	16
LoRA alpha	16
LoRA dropout	0
Bias	none
Target modules	Unsloth/PEFT regex targeting attention and MLP projection modules (see `adapter_config.json`)
Task type	CAUSAL_LM

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.