Modelpublic

SpiceeChat/Bio2Tags-Lite

sourceHugging Faceapache-2.0updated 4mo agoView on Hugging Face

1likes15downloads

Model Card

🏷️ Bio2Tags-Lite

Because reading between the lines shouldn't require a psychology degree.

Bio2Tags-Lite is a fine-tuned SmolLM2-360M model that reads personal biographies and returns clean, structured personality tags. Feed it a dating bio, a LinkedIn summary, or whatever someone wrote about themselves at 2am — it'll tell you what kind of person they actually are.

No rambling. No fluff. Just tags.

✨ Features

—Lightweight: 360M parameters — runs on hardware that would make a gamer cry
—Fast: Inference in milliseconds, because nobody has time to wait
—Structured Output: Clean comma-separated tags, every time
—Plug & Play: Works with Transformers out of the box, no PhD required
—SpiceeChat Pipeline: Pairs with Cinder-1.5B like peanut butter and heartbreak

🧪 Example

python

from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained(
    "SpiceeChat/Bio2Tags-Lite",
    torch_dtype="auto",
    device_map="auto",
)
tokenizer = AutoTokenizer.from_pretrained("SpiceeChat/Bio2Tags-Lite")

def get_tags(bio):
    prompt = f"Extract personality tags from the bio below. Output ONLY comma-separated tags, nothing else.\n\nBio: {bio}\n\nTags:"
    messages = [{"role": "user", "content": prompt}]
    formatted = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
    inputs = tokenizer(formatted, return_tensors="pt").to(model.device)
    outputs = model.generate(**inputs, max_new_tokens=50, temperature=0.7, do_sample=True)
    return tokenizer.decode(outputs[0][inputs.input_ids.shape[1]:], skip_special_tokens=True).strip()

# Try it
print(get_tags("I love hiking at dawn, painting watercolors, and deep conversations about philosophy."))
# Output: nature-lover, artist, intellectual, deep-thinker

📊 Sample Outputs

Bio	Tags
"I'm a software engineer who loves late-night coding and playing jazz piano."	tech-savvy, creative, night-owl, music-enthusiast, artistic
"I spend my weekends trail running and evenings reading classic literature."	adventurous, nature-lover, bookworm, intellectual, quiet
"I'm a retired teacher who gardens, reads history books, and bakes sourdough."	intellectual, family-oriented, gardener, history-buff, old-soul
"As a digital nomad, my office changes weekly — from Bali cafes to Alpine cabins."	adventurous, creative, digital-nomad, spontaneous, tech-savvy

(Yes, the sourdough one is a stereotype. Yes, it's also always accurate.)

📦 Installation

bash

pip install transformers torch accelerate

That's it. No ritual sacrifices, no config files, no Stack Overflow rabbit holes.

🎯 Use Cases

—Dating Apps: Tag user bios automatically for smarter matching — because "I like long walks on the beach" means something very different than "I like long walks on the beach at 3am alone"
—Social Media: Generate relevant hashtags from profile descriptions
—Recommender Systems: Build personality-based recommendation engines
—Content Analysis: Extract structured metadata from unstructured text
—SpiceeChat Pipeline: Feed extracted tags into Cinder-1.5B for personalized compatibility advice

🛠️ Technical Details

Detail	Value
Base Model	SmolLM2-360M-Instruct
Fine-tuning Method	QLoRA (4-bit quantization, rank-16 adapters)
Training Framework	Unsloth
Training Data	1,387 hand-crafted (bio, tags) pairs
Epochs	3
Learning Rate	1e-4
Sequence Length	512 tokens
Hardware Used	Google Colab T4 (free tier — yes, really)
Final Size	724 MB (FP16)
Min VRAM Required	~1.5 GB

⚠️ Limitations

—English only: Other languages may produce results ranging from "creative" to "confidently wrong"
—Training data size: 1,387 examples is a solid start — more data is always on the roadmap
—Tag granularity: Captures the salient stuff, not every quirk (the model can't detect if someone is secretly obsessed with true crime podcasts)
—Edge cases: Very short bios, emoji-heavy text, or deeply abstract descriptions may surprise you

🧠 Part of the SpiceeChat Ecosystem

Bio2Tags-Lite is a core component of the SpiceeChat AI pipeline:

—🏷️ Bio2Tags-Lite → Extracts personality tags from bios
—🔥 [Cinder-1.5B](https://huggingface.co/SpiceeChat/Cinder-1.5B) → Personalized dating advice powered by those tags
—🌐 [dating-fatigue.com](https://dating-fatigue.com) → Live tools for real humans trying to find real love

📜 License

Apache 2.0 — use it, modify it, ship it. Just give SpiceeChat a nod.

<div align="center"> Built with ❤️ by SpiceeChat 🔗 <a href="https://huggingface.co/SpiceeChat">huggingface.co/SpiceeChat</a> </div>