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

Libraries

How to use d4rkninja/tanpo-deals with Transformers:

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

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

Install from pip and serve model

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

Use Docker

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

SGLang

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

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

Tanpo Deals

A compact B2B deal-closing and channel/distributor specialist (~1.2B) for qualification, objections, proposals, and partner motions — 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 Deals is one specialist in that family (not a frontier or general-purpose model).

Related artifacts:

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

Best For

Discovery / qualification gates and go/no-go scorecards
Objection handling, dated close asks, and give/get negotiation packages
Commercial proposal / ROI outlines and mutual action plans
Distributor recruiting, channel enablement, and wholesale pricing architecture drafts

Not Designed For

Legal advice on contracts, exclusivity, or antitrust
Unauthorized access, credential attacks, or social-engineering harm
Guaranteed win-rate predictions or financial advice
General coding or non-commercial chat

Why a Specialist Model?

Deal and channel work rewards repeatable commercial structure. 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`	87.9%
tanpo-deals	95.2%
Delta	+7.3 percentage points

Artifacts: evaluation/ — deals_tasks.jsonl, score_rubric.md, evaluation/README.md.

Methodology: DarkLab automated domain evaluation (~20 held-out deals 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: Build a qualification plan for a distributor requesting exclusivity in DACH. Include discovery questions, red flags, and a go/no-go scorecard.
User: A prospect says 'we're happy with our current vendor.' Write a reframe plus a conditional next-step ask for a mid-market SaaS sale.
User: Outline wholesale price architecture for a hardware accessory: MSRP, distributor cost, volume breaks, rebate options, and margin floors.

Inference

from transformers import AutoModelForCausalLM, AutoTokenizer

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

messages = [
    {"role": "system", "content": 'You are Tanpo Deals, a practical B2B deal-closing and channel assistant.'},
    {"role": "user", "content": 'Build a qualification plan for a distributor requesting exclusivity in DACH. Include discovery questions, red flags, and a go/no-go scorecard.'},
]
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-deals-sft — ~10k format-fixed chat SFT examples (documented on the dataset card).

Limitations

Specialized: quality drops outside the deals workflow distribution.
~1.2B scale: limited world knowledge and long-horizon reasoning vs larger models.
Commercial judgment varies by industry and jurisdiction; outputs are drafts.
Eval gains are rubric-based and directional only.

Responsible Use

Not legal, tax, or financial advice. Humans must review contracts, pricing commitments, and partner terms. 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.