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

Libraries

How to use d4rkninja/tanpo-ops with Transformers:

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

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

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("d4rkninja/tanpo-ops")
model = AutoModelForCausalLM.from_pretrained("d4rkninja/tanpo-ops", 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-ops with vLLM:

Install from pip and serve model

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

Use Docker

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

SGLang

How to use d4rkninja/tanpo-ops 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-ops" \
    --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-ops",
		"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-ops" \
        --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-ops",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use d4rkninja/tanpo-ops with Docker Model Runner:
```
docker model run hf.co/d4rkninja/tanpo-ops
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

Tanpo Ops

A compact operations specialist (~1.2B) for capacity planning, identity guardrails, OKR execution, vendor operations, handoffs, SOPs, operating cadence, and incident postmortems — 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. One compact architecture (LiquidAI/LFM2.5-1.2B-Instruct) → multiple focused specialists → each ships Full/Merged | LoRA | GGUF. Tanpo Ops is one specialist in that family (not a frontier or general-purpose model).

Related artifacts:

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

Best For

Capacity-planning drafts, resourcing assumptions, and operational tradeoff analysis
OKR decomposition, execution plans, status reviews, and operating cadence
Vendor-operations checklists, handoff design, and SOP drafting
Incident-postmortem structure, follow-up actions, and identity-aware operational workflows

Not Designed For

Fully automated staffing, access, vendor, incident-severity, or other consequential operational decisions
Fabricating operational facts, impersonation, bypassing identity controls, or unauthorized access
Guarantees about capacity, uptime, compliance, incident outcomes, or business performance
General coding or non-operations chat

Why a Specialist Model?

Operations work rewards repeatable structure (inputs, assumptions, owner, timing, dependencies, risks, decision, 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`	89.5%
tanpo-ops	90.7%
Delta	+1.2 percentage points

Largest gains were on capacity_planning (+5.5 percentage points), identity (+17.1 percentage points), okr_execution (+11.1 percentage points), and vendor_ops (+5.6 percentage points).

Trailed on handoffs (−7.4 percentage points) and on sops, operating_cadence, and incident_postmortem (~−3.7 percentage points each). Overall still ahead of base; these categories are disclosed.

Artifacts: evaluation/ — COMPARE_OPS.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 capacity plan from these workload assumptions, separating facts, assumptions, constraints, risks, and decisions needed.
User: Turn this quarterly objective into measurable OKRs with owners, dependencies, milestones, and a weekly operating cadence.
User: Draft an incident postmortem outline from these notes; do not invent impact, root cause, or remediation facts.

Inference

from transformers import AutoModelForCausalLM, AutoTokenizer

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

messages = [
    {"role": "system", "content": "You are Tanpo Ops, a practical operations assistant."},
    {"role": "user", "content": "Turn this quarterly objective into measurable OKRs with owners, dependencies, milestones, and a weekly operating cadence."},
]
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-ops-sft — format-fixed chat SFT examples (documented on the dataset card).

Limitations

Specialized: quality drops outside the operations and business-workflow distribution.
~1.2B scale: limited world knowledge and long-horizon reasoning vs larger models.
Capacity, staffing, vendor, identity, OKR, and incident recommendations can be wrong or incomplete; verify against source systems, policies, and operational context.
Eval gains are rubric-based and directional only; handoffs, sops, operating_cadence, and incident_postmortem still trail the base.

Responsible Use

Not a substitute for operational judgment, source-system data, access-control policy, security review, incident command, or legal/compliance advice. Humans must review capacity plans, staffing recommendations, access-sensitive workflows, vendor decisions, OKRs, SOPs, and incident records before action. 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.