Instructions to use SpiceeChat/Bio2Tags-Lite with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use SpiceeChat/Bio2Tags-Lite with Transformers:

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

pipe = pipeline("text-generation", model="SpiceeChat/Bio2Tags-Lite")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("SpiceeChat/Bio2Tags-Lite")
model = AutoModelForCausalLM.from_pretrained("SpiceeChat/Bio2Tags-Lite")
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

vLLM

How to use SpiceeChat/Bio2Tags-Lite with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "SpiceeChat/Bio2Tags-Lite"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "SpiceeChat/Bio2Tags-Lite",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/SpiceeChat/Bio2Tags-Lite

SGLang

How to use SpiceeChat/Bio2Tags-Lite 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 "SpiceeChat/Bio2Tags-Lite" \
    --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": "SpiceeChat/Bio2Tags-Lite",
		"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 "SpiceeChat/Bio2Tags-Lite" \
        --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": "SpiceeChat/Bio2Tags-Lite",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use SpiceeChat/Bio2Tags-Lite with Docker Model Runner:
```
docker model run hf.co/SpiceeChat/Bio2Tags-Lite
```

SpiceeChat

🏷️ 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

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

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 → Personalized dating advice powered by those tags
🌐 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.

_{Built with ❤️ by SpiceeChat}
_{🔗 huggingface.co/SpiceeChat}

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Safetensors

Model size

0.4B params

Tensor type

BF16