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

Libraries

How to use d4rkninja/tanpo-finance with Transformers:

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

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

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("d4rkninja/tanpo-finance")
model = AutoModelForCausalLM.from_pretrained("d4rkninja/tanpo-finance", 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=256)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use d4rkninja/tanpo-finance with vLLM:

Install from pip and serve model

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

Use Docker

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

SGLang

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

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

Tanpo Finance

A compact finance specialist (~1.2B) for unit economics, runway and cash planning, pricing and margin analysis, SaaS metrics, budget tradeoffs, burn efficiency, cohort revenue, cost structure, and forecast scenarios — built for local and inexpensive deployment.

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 Finance is a focused finance-workflow assistant, not a frontier or general-purpose model.

Related artifacts:

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

Best For

Unit economics, contribution margin, LTV/CAC, and segment kill/scale analysis
Runway, burn, cash-bridge, stress-scenario, and working-capital planning
Pricing, packaging, margin, cost-structure, and budget tradeoff drafts
SaaS metrics, cohort revenue, forecast scenarios, and finance operating reviews

Not Designed For

Tax, accounting, audit, securities, legal, or personalized investment advice
Fully automated pricing, spending, hiring, lending, treasury, or investment decisions
Inventing financial records, manipulating reports, fraud, impersonation, or unauthorized access
Guarantees about runway, valuation, profitability, cash position, or business outcomes
General coding or non-finance chat

Why a Specialist Model?

Finance work rewards explicit assumptions, formulas, scenarios, tradeoffs, decision gates, and next actions. Specializing a small model for those workflows enables private, low-cost inference without a large general model.

Evaluation

Internal automated domain evaluation (DarkLab harness). Internal training/eval used a finance format revision (not a product identity). Treat as directional, not an industry benchmark. Same prompts and generation configuration were used for the base and fine-tune.

Model	Rubric overall
Base `LFM2.5-1.2B-Instruct`	93.1%
tanpo-finance	98.4%
Delta	+5.4 percentage points

Delta is calculated from unrounded scores; displayed scores are rounded to one decimal place.

Largest gains were on categories scoring 100.0%; trailed on cost_structure (94.5%, −5.5 percentage points vs base). identity_guardrails scores 90.0%.

Category scorecard:

Category	Base %	FT %	Delta (pp)	FT wins
`budget_tradeoffs`	94.5%	100.0%	+5.5	yes
`burn_efficiency`	94.5%	100.0%	+5.5	yes
`cohort_revenue`	94.5%	100.0%	+5.5	yes
`cost_structure`	100.0%	94.5%	−5.5	no
`forecast_scenario`	94.5%	100.0%	+5.5	yes
`identity_guardrails`	58.6%	90.0%	+31.4	yes
`pricing_finance`	100.0%	100.0%	+0.0	no
`runway_cash`	100.0%	100.0%	+0.0	no
`saas_metrics`	100.0%	100.0%	+0.0	no
`unit_economics`	94.5%	100.0%	+5.5	yes

Artifacts: evaluation/ — COMPARE_FINANCE.md.

Limitations of this eval: Automated rubrics can reward structure over real-world quality; the held-out sample is small; and results may not transfer outside the task distribution. A high rubric score is not evidence that a financial recommendation is correct.

Example Prompts

User: Build a runway bridge with base, downside, and upside cases; show assumptions, cash actions, and tripwires.
User: Compare two pricing models using contribution margin, predictability, implementation cost, and a recommended default with guardrails.
User: Diagnose a cohort-revenue leak from these facts; separate observed data, hypotheses, experiments, owners, and decision gates.

Inference

from transformers import AutoModelForCausalLM, AutoTokenizer

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

messages = [
    {"role": "system", "content": "You are Tanpo Finance, a practical finance planning assistant. State assumptions and do not invent financial facts."},
    {"role": "user", "content": "Build a runway bridge with base, downside, and upside cases; show assumptions, cash actions, and tripwires."},
]
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)
Training note	Internal finance training/eval format update (not a product SKU)
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-finance-sft — format-fixed finance chat SFT examples; training emphasizes deliverable-first structure, explicit assumptions, numbered actions, and finance guardrails.

Limitations

Specialized: quality drops outside the finance and business-workflow distribution.
~1.2B scale: limited world knowledge, arithmetic reliability, and long-horizon reasoning compared with larger models.
Outputs can contain incorrect formulas, assumptions, calculations, forecasts, or recommendations; reconcile with source systems and a qualified finance professional.
cost_structure trails the base by 5.5 pp in the reported evaluation; identity_guardrails reaches 90.0%, not 100%.
Evaluation results are rubric-based and directional only; quantization or deployment settings can change behavior.

Responsible Use

Not a substitute for a qualified accountant, controller, auditor, tax adviser, lawyer, investment professional, or finance decision-maker. Humans must verify source data, formulas, units, currencies, period definitions, assumptions, and approvals before using outputs. Keep confidential financial and personal data protected. Do not use for fraud, deception, market manipulation, discriminatory treatment, credential theft, 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.