d4rkninja/tanpo-fundraising

Tanpo Fundraising

A compact founder fundraising specialist (~1.2B) for investor pitches, narrative, ask/use-of-funds, diligence Q&A, updates, and round-structure language (non-legal) — 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. Tanpo Fundraising is one specialist in that family (not a frontier or general-purpose model).

Related artifacts:

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

Best For

• —Investor pitch openers, narrative arcs, and wedge framing
• —Ask + use-of-funds drafts tied to milestones and burn/runway
• —Diligence / objection Q&A and investor update drafts
• —Traction stories, market/why-now framing, and non-legal round-structure language

Not Designed For

• —Legal advice on term sheets, securities, or fiduciary duties
• —Investment advice, valuations as fact, or guaranteed raise outcomes
• —Unauthorized access, credential attacks, or social-engineering harm
• —General coding or non-fundraising chat

Why a Specialist Model?

Fundraising work rewards repeatable structure (narrative, ask, diligence, updates). 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.

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

Largest overall gain vs base; trailed on only dilution_round_structure (−22.2 percentage points on that category). Overall still ahead of base; this category is disclosed.

Artifacts: `evaluation/` — `COMPARE_FUNDRAISING.md`, `fundraising_tasks.jsonl`, `score_rubric.md`, `evaluation/README.md`.

Methodology: DarkLab automated domain evaluation (~20 held-out fundraising tasks). 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

1. 1.User: Rewrite the pitch opener for a B2B SaaS fleet-ops tool moving from seed to Series A bridge. Current story is feature soup — give a sharp narrative arc and talk track.
2. 2.User: Build ask + use-of-funds for a Series A raise with explicit burn, runway, and milestone gates to the next raise.
3. 3.User: Draft a monthly investor update that kills fluff: KPIs, risks, and concrete intro asks — no customer names.

Inference

from transformers import AutoModelForCausalLM, AutoTokenizer

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

messages = [
    {"role": "system", "content": "You are Tanpo Fundraising, a practical founder fundraising assistant."},
    {"role": "user", "content": "Rewrite the pitch opener for a B2B SaaS fleet-ops tool moving from seed to Series A bridge. Current story is feature soup — give a sharp narrative arc and talk track."},
]
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):

Merged via PEFT merge_and_unload into full weights in this repo.

Dataset

• —d4rkninja/tanpo-fundraising-sft — ~10k format-fixed chat SFT examples (documented on the dataset card).

Limitations

• —Specialized: quality drops outside the fundraising workflow distribution.
• —~1.2B scale: limited world knowledge and long-horizon reasoning vs larger models.
• —Round-structure / dilution language is draft and non-legal; counsel must review term sheets.
• —Eval gains are rubric-based and directional only; one category (dilution_round_structure) still trails the base.

Responsible Use

Not legal, tax, or investment advice. Humans must review pitches, financial claims, and any term-sheet language. Do not use for fraud, deception, 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-fundraising`
• —LoRA: `d4rkninja/tanpo-fundraising-LoRA`
• —GGUF: `d4rkninja/tanpo-fundraising-GGUF`

Browse all Tanpo specialists: Tanpo — Domain Specialists