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_Mwhen 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, andincident_postmortemstill 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.
Tanpo Family
Tanpo is a family of compact domain-specialized models for focused business workflows.
This specialist is available as:
- Merged:
d4rkninja/tanpo-ops - LoRA:
d4rkninja/tanpo-ops-LoRA - GGUF:
d4rkninja/tanpo-ops-GGUF
Browse all Tanpo specialists: Tanpo — Domain Specialists