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

Libraries

How to use d4rkninja/tanpo-fundraising with Transformers:

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

pipe = pipeline("text-generation", model="d4rkninja/tanpo-fundraising")
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-fundraising")
model = AutoModelForCausalLM.from_pretrained("d4rkninja/tanpo-fundraising", 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-fundraising with vLLM:

Install from pip and serve model

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

Use Docker

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

SGLang

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

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

Model	Rubric overall
Base `LFM2.5-1.2B-Instruct`	84.2%
tanpo-fundraising	95.2%
Delta	+11.1 percentage points

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

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.
User: Build ask + use-of-funds for a Series A raise with explicit burn, runway, and milestone gates to the next raise.
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):

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-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.