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

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use d4rkninja/tanpo-retention-GGUF with llama.cpp:

Install (macOS, Linux)

curl -LsSf https://llama.app/install.sh | sh
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf d4rkninja/tanpo-retention-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf d4rkninja/tanpo-retention-GGUF:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf d4rkninja/tanpo-retention-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf d4rkninja/tanpo-retention-GGUF:Q4_K_M

Use pre-built binary

# Download pre-built binary from:
# https://github.com/ggerganov/llama.cpp/releases
# Start a local OpenAI-compatible server with a web UI:
./llama-server -hf d4rkninja/tanpo-retention-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf d4rkninja/tanpo-retention-GGUF:Q4_K_M

Build from source code

git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-server llama-cli
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama-server -hf d4rkninja/tanpo-retention-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf d4rkninja/tanpo-retention-GGUF:Q4_K_M

Use Docker

docker model run hf.co/d4rkninja/tanpo-retention-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use d4rkninja/tanpo-retention-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "d4rkninja/tanpo-retention-GGUF"
# 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-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/d4rkninja/tanpo-retention-GGUF:Q4_K_M

Ollama
How to use d4rkninja/tanpo-retention-GGUF with Ollama:
```
ollama run hf.co/d4rkninja/tanpo-retention-GGUF:Q4_K_M
```
Unsloth Desktop

How to use d4rkninja/tanpo-retention-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf d4rkninja/tanpo-retention-GGUF:Q4_K_M

Configure the model in Pi

# Install Pi:
npm install -g @earendil-works/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "d4rkninja/tanpo-retention-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use d4rkninja/tanpo-retention-GGUF with Docker Model Runner:
```
docker model run hf.co/d4rkninja/tanpo-retention-GGUF:Q4_K_M
```

Lemonade

How to use d4rkninja/tanpo-retention-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull d4rkninja/tanpo-retention-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.tanpo-retention-GGUF-Q4_K_M

List all available models

lemonade list

Hermes Agent

How to use d4rkninja/tanpo-retention-GGUF with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf d4rkninja/tanpo-retention-GGUF:Q4_K_M

Configure Hermes

# Install Hermes:
curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash
hermes setup
# Point Hermes at the local server:
hermes config set model.provider custom
hermes config set model.base_url http://127.0.0.1:8080/v1
hermes config set model.default d4rkninja/tanpo-retention-GGUF:Q4_K_M

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use d4rkninja/tanpo-retention-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf d4rkninja/tanpo-retention-GGUF:Q4_K_M

Configure OpenClaw

# Install OpenClaw:
npm install -g openclaw@latest
# Register the local server and set it as the default model:
openclaw onboard --non-interactive --mode local \
  --auth-choice custom-api-key \
  --custom-base-url http://127.0.0.1:8080/v1 \
  --custom-model-id "d4rkninja/tanpo-retention-GGUF:Q4_K_M" \
  --custom-provider-id llama-cpp \
  --custom-compatibility openai \
  --custom-text-input \
  --accept-risk \
  --skip-health

Run OpenClaw

openclaw agent --local --agent main --message "Hello from Hugging Face"

Tanpo Retention (GGUF)

GGUF quantizations of d4rkninja/tanpo-retention for local runtimes (llama.cpp, LM Studio, and other GGUF-compatible tools).

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).

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.

Best For

Local drafting of customer-success playbooks, renewal plans, churn-save, expansion, and winback workflows
Private / edge inference where a small specialist is preferred

Not Designed For

Automated account decisions or guaranteed customer outcomes
Fabricating customer facts, impersonation, or unauthorized access
General coding or non-retention chat

Available quantizations

On-disk sizes from the live Hugging Face file listing (MB, rounded from bytes). Disk size ≠ peak RAM/VRAM at runtime.

File	Size
`tanpo-retention.Q3_K_M.gguf`	600 MB
`tanpo-retention.Q4_K_S.gguf`	700 MB
`tanpo-retention.Q4_K_M.gguf`	731 MB — suggested everyday
`tanpo-retention.Q5_K_S.gguf`	825 MB
`tanpo-retention.Q5_K_M.gguf`	843 MB
`tanpo-retention.Q8_0.gguf`	1246 MB

Suggested everyday file: tanpo-retention.Q4_K_M.gguf

Evaluation (summary)

Model	Rubric overall
Base	94.2%
Fine-tune	96.0%
Delta	+1.8 percentage points

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).

Full methodology on the merged model card. Artifact: evaluation/COMPARE_RETENTION.md. Quantization can change behavior slightly vs merged weights.

Running locally

Download tanpo-retention.Q4_K_M.gguf (or another available quant).
Open it in LM Studio or run with llama.cpp (use a build that supports LFM2 / the model architecture).
Keep prompts in the retention domain; verify consequential outputs with a human.

No separate LoRA step is required — these are standalone GGUF files of the merged specialist.

Merged Transformers: d4rkninja/tanpo-retention
LoRA: d4rkninja/tanpo-retention-LoRA

Limitations

This is a compact domain specialist, not a frontier or general-purpose model. Verify outputs against account context and source systems; quantization can change behavior slightly. The reported evaluation is directional, and cs_playbooks and health_scores trail the base.

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

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:

Browse all Tanpo specialists: Tanpo — Domain Specialists

Downloads last month: 356

GGUF

Model size

1B params

Architecture

lfm2

Hardware compatibility

3-bit

4-bit

5-bit

8-bit

Model tree for d4rkninja/tanpo-retention-GGUF

Base model

LiquidAI/LFM2.5-1.2B-Base

Finetuned

LiquidAI/LFM2.5-1.2B-Instruct

Quantized

(120)

this model

Collection including d4rkninja/tanpo-retention-GGUF

Tanpo — Domain Specialists

Collection

Eight compact specialists for focused business workflows. Merged · LoRA · GGUF. Local & edge — right model, not bigger. • 24 items • Updated 26 days ago • 1

d4rkninja
/

tanpo-retention-GGUF

Tanpo Retention (GGUF)

Best For

Not Designed For

Available quantizations

Evaluation (summary)

Running locally

Related

Limitations

Responsible Use

License

Tanpo Family

Model tree for d4rkninja/tanpo-retention-GGUF

Collection including d4rkninja/tanpo-retention-GGUF

Tanpo — Domain Specialists