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.

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

1. 1.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.
2. 2.User: Design a 3-stage interview loop for an Account Executive selling to mid-market IT buyers. Assign goals per stage.
3. 3.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):

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.

Tanpo Family

Tanpo is a family of compact domain-specialized models for focused business workflows.

This specialist is available as:

• —Merged: `d4rkninja/tanpo-hiring`
• —LoRA: `d4rkninja/tanpo-hiring-LoRA`
• —GGUF: `d4rkninja/tanpo-hiring-GGUF`

Browse all Tanpo specialists: Tanpo — Domain Specialists