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

Libraries

How to use d4rkninja/tanpo-product with Transformers:

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

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

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("d4rkninja/tanpo-product")
model = AutoModelForCausalLM.from_pretrained("d4rkninja/tanpo-product", 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=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use d4rkninja/tanpo-product with vLLM:

Install from pip and serve model

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

Use Docker

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

SGLang

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

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

Tanpo Product

A compact product-strategy specialist (~1.2B) for roadmap tradeoffs, prioritization, metrics, and CEO-level product thinking — 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 Product is one specialist in that family (not a frontier or general-purpose model).

Related artifacts:

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

Best For

Kill / keep / invest and prioritization write-ups
Roadmap sequencing and scope cuts
North-star metrics, OKRs, and operating cadence
Competitive positioning, pricing/monetization frames, and discovery synthesis

Not Designed For

Substituting primary customer research or user interviews
Authoritative market sizing or financial forecasts
Legal, security, or compliance decisions
General coding or non-product knowledge work

Why a Specialist Model?

Product decisions need structured tradeoffs more than generic chat. Fine-tuning a small model on product-workflow formats makes edge deployment practical 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`	92.6%
tanpo-product	99.4%
Delta	+6.8 percentage points

Artifacts: evaluation/ — COMPARE_BASE.md, product_tasks.jsonl, score_rubric.md, evaluation/README.md.

Methodology: DarkLab automated domain evaluation on the product training/eval format revision fine-tune (~20 held-out 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: We have three bets for next quarter: (A) usage-based billing, (B) Salesforce sync, (C) mobile offline mode. Only engineering capacity for one. Recommend with kill criteria.
User: Draft a one-page PRD outline for an in-app 'saved views' feature for a B2B analytics product. Include problem, users, success metric, and non-goals.
User: Propose a north-star metric and 3 supporting input metrics for a vertical SaaS CRM for dental clinics.

Inference

from transformers import AutoModelForCausalLM, AutoTokenizer

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

messages = [
    {"role": "system", "content": 'You are Tanpo Product, a practical product-strategy assistant.'},
    {"role": "user", "content": 'We have three bets for next quarter: (A) usage-based billing, (B) Salesforce sync, (C) mobile offline mode. Only engineering capacity for one. Recommend with kill criteria.'},
]
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	`q_proj`, `k_proj`, `v_proj`, `out_proj`, `in_proj`, `w1`, `w2`, `w3`
Task type	CAUSAL_LM

Merged via PEFT merge_and_unload into full weights in this repo.

Dataset

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

Limitations

Specialized: quality drops outside the product workflow distribution.
~1.2B scale: limited world knowledge and long-horizon reasoning vs larger models.
Can sound decisive when evidence is thin — require human judgment for consequential product bets.
Eval gains are rubric-based and directional only.

Responsible Use

Not a substitute for customer research, board fiduciary judgment, or professional analysis. Treat outputs as drafts for human product leaders.

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.