Instructions to use SpiceeChat/Bio2Tags-Qwen2.5-3B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use SpiceeChat/Bio2Tags-Qwen2.5-3B with Transformers:

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

pipe = pipeline("text-generation", model="SpiceeChat/Bio2Tags-Qwen2.5-3B")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("SpiceeChat/Bio2Tags-Qwen2.5-3B")
model = AutoModelForCausalLM.from_pretrained("SpiceeChat/Bio2Tags-Qwen2.5-3B")
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-Qwen2.5-3B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "SpiceeChat/Bio2Tags-Qwen2.5-3B"
# 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-Qwen2.5-3B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/SpiceeChat/Bio2Tags-Qwen2.5-3B

SGLang

How to use SpiceeChat/Bio2Tags-Qwen2.5-3B 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-Qwen2.5-3B" \
    --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-Qwen2.5-3B",
		"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-Qwen2.5-3B" \
        --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-Qwen2.5-3B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use SpiceeChat/Bio2Tags-Qwen2.5-3B with Docker Model Runner:
```
docker model run hf.co/SpiceeChat/Bio2Tags-Qwen2.5-3B
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

SpiceeChat

Bio2Tags 🏷️

Transform any personal biography into a clean, structured set of tags.

Bio2Tags is a fine‑tuned Qwen2.5‑3B model that extracts personality traits, interests, lifestyle descriptors, and values from unstructured text. Give it a sentence or a paragraph about a person, and it returns a concise list of descriptive tags — like a friend who actually reads your profile before setting you up. (Qwen2.5‑3B is very near to Qwen3.5-4B and as there were no changes to give$)

Example

Input: "I love hiking at dawn, painting watercolors, and deep conversations about philosophy. I'm a vegetarian and passionate about climate change."
Output: nature-lover, artist, intellectual, vegetarian, environmentalist

(It won’t tell you if you’re undateable — that’s on you.)

Quick Start

from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained(
    "SpiceeChat/Bio2Tags-Qwen3.5-4B-SFT",
    torch_dtype="auto",
    device_map="auto",
    trust_remote_code=True,
)
tokenizer = AutoTokenizer.from_pretrained("SpiceeChat/Bio2Tags-Qwen3.5-4B-SFT", trust_remote_code=True)

def get_tags(bio):
    messages = [
        {"role": "system", "content": "Extract tags from the following biography. Return only the tags, separated by commas, with no other text."},
        {"role": "user", "content": bio},
    ]
    prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
    inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
    outputs = model.generate(**inputs, max_new_tokens=40, temperature=0.7, do_sample=True)
    return tokenizer.decode(outputs[0][inputs.input_ids.shape[1]:], skip_special_tokens=True).strip()

print(get_tags("I enjoy cooking Italian food and playing jazz piano."))
# → cooking, musician, italian-cuisine, jazz, creative

Installation

pip install transformers torch accelerate

Hardware: Requires ~6 GB VRAM (FP16). Use device_map="auto" for multi‑GPU or CPU offloading.

Model Details

Detail	Value
Base Model	Qwen2.5‑3B‑Instruct
Fine‑tuning Method	QLoRA (4‑bit), rank‑16
Training Data	1,387 (bio, tags) pairs — lovingly crafted by a caffeinated Gemini
Epochs	3 (because nobody likes an overtrained model, or an undercooked steak)
Output Format	Comma‑separated tags

Use Cases

Dating profiles: Automatically tag user bios so they don’t have to awkwardly describe themselves.
Social media: Generate hashtags that make you look deep and mysterious.
Recommender systems: Build personality‑based matching — finally, an algorithm that understands “I like long walks on the beach.”
Content moderation: Flag bios with specific attributes (or just make sure nobody calls themselves a “guru” unironically).

Limitations

The model was trained on English bios only. French poetry will confuse it.
Tags are descriptive, not exhaustive — it captures the most salient traits, not your entire life story.

License

Apache‑2.0

Built for SpiceeChat 🔥 — BioTags

BUY US SOME COFFEE!

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Safetensors

Model size

3B params

Tensor type

F16

Model tree for SpiceeChat/Bio2Tags-Qwen2.5-3B

Quantizations

1 model