tanpo-ops-GGUF

Tanpo Ops (GGUF)

GGUF quantizations of d4rkninja/tanpo-ops 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. One compact architecture (LiquidAI/LFM2.5-1.2B-Instruct) → multiple focused specialists → each ships Full/Merged | LoRA | GGUF. 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 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.

Best For

• Local drafting of capacity plans, OKRs, vendor-operations checklists, SOPs, handoffs, cadence reviews, and incident postmortems
• Private / edge inference where a small operations specialist is preferred

Not Designed For

• Automated staffing, access, vendor, incident, or other consequential operational decisions
• Fabricating facts, impersonation, bypassing identity controls, or unauthorized access
• General coding or non-operations chat

Available quantizations

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

Suggested everyday file: tanpo-ops.Q4_K_M.gguf

Evaluation (summary)

Largest gains were on capacity_planning (+5.5 pp), identity (+17.1 pp), okr_execution (+11.1 pp), and vendor_ops (+5.6 pp). Trailed on handoffs (−7.4 pp) and sops, operating_cadence, and incident_postmortem (~−3.7 pp each).

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

Running locally

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

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

Related

• Merged Transformers: d4rkninja/tanpo-ops
• LoRA: d4rkninja/tanpo-ops-LoRA

Limitations

This is a compact domain specialist, not a frontier or general-purpose model. Verify outputs against operational context, source systems, policies, and access-control requirements; quantization can change behavior slightly. The reported evaluation is directional, and handoffs, sops, operating_cadence, and incident_postmortem trail the base.

Responsible Use

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, 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:

• Merged: d4rkninja/tanpo-ops
• LoRA: d4rkninja/tanpo-ops-LoRA
• GGUF: d4rkninja/tanpo-ops-GGUF

Browse all Tanpo specialists: Tanpo — Domain Specialists