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

Libraries

How to use d4rkninja/tanpo-hiring with Transformers:

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

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

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("d4rkninja/tanpo-hiring")
model = AutoModelForCausalLM.from_pretrained("d4rkninja/tanpo-hiring", 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-hiring with vLLM:

Install from pip and serve model

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

Use Docker

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

SGLang

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

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

Tanpo Hiring

A compact hiring / talent specialist (~1.2B) for JDs, scorecards, screens, interview loops, and offer framing — 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 Hiring is one specialist in that family (not a frontier or general-purpose model).

Related artifacts:

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

Best For

Role-specific job descriptions and hiring scorecards
Outbound recruiting sequences and screening scripts
Interview-loop design and structured interview kits
Debrief calibration prompts, offer framing, and rejection ops drafts

Not Designed For

Final hiring decisions without human review
Legal advice on employment law, discrimination, or immigration
Background checks, surveillance, or unauthorized access to candidate systems
General non-hiring chat or coding assistance

Why a Specialist Model?

Recruiting artifacts are repetitive and format-heavy. A small specialist can draft structured hiring materials cheaply on-device while humans retain decision authority.

Evaluation

Internal automated domain evaluation (DarkLab harness). Treat as directional, not an industry benchmark.

Model	Rubric overall
Base `LFM2.5-1.2B-Instruct`	83.8%
tanpo-hiring	92.9%
Delta	+9.2 percentage points

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

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

Methodology: DarkLab automated domain evaluation (~20 held-out hiring 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: Write a hiring scorecard for a Senior Backend Engineer (Go, Postgres) at a Series B fintech. Include must-haves, nice-to-haves, and evidence signals.
User: Design a 3-stage interview loop for an Account Executive selling to mid-market IT buyers. Assign goals per stage.
User: Draft a respectful rejection email after onsite for a product designer who was a strong runner-up. Keep the door open.

Inference

from transformers import AutoModelForCausalLM, AutoTokenizer

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

messages = [
    {"role": "system", "content": 'You are Tanpo Hiring, a practical recruiting assistant.'},
    {"role": "user", "content": 'Write a hiring scorecard for a Senior Backend Engineer (Go, Postgres) at a Series B fintech. Include must-haves, nice-to-haves, and evidence signals.'},
]
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-hiring-sft — ~10k format-fixed chat SFT examples (documented on the dataset card).

Limitations

Specialized: quality drops outside the hiring workflow distribution.
~1.2B scale: limited world knowledge and long-horizon reasoning vs larger models.
May miss jurisdiction-specific employment requirements. Structured interview kits still need interviewer training.
Eval gains are rubric-based and directional only.

Responsible Use

Always keep humans in the loop for screening, debriefs, and offers. Avoid biased criteria; comply with applicable employment law. Do not use for unauthorized access to candidate data or systems.

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.