Text Generation
Transformers
Safetensors
English
llama
bio-to-tags
tag-generation
smollm2
personality
interests
spiceechat
conversational
text-generation-inference
Instructions to use SpiceeChat/Bio2Tags-Lite with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
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 Settings
- 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
Create README.md
Browse files
README.md
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---
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license: apache-2.0
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language:
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- en
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tags:
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- bio
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- personality
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- tags
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- extraction
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- spiceechat
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- tiny-model
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- work-in-progress
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pipeline_tag: text-generation
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---
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# π·οΈ Bio2Tags-Lite
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> *The full real README is stuck in traffic. Will arrive tomorrow. Please download on half faith. π€*
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---
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## π§ What's Going On?
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You're looking at a placeholder because:
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1. **The real README is in traffic.** LA rush hour. It's bad out there.
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2. **This model was born 30 minutes ago.** It still doesn't know what a 401(k) is.
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3. **I am training like 4 models right now.**
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---
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## π€¨ So What Does This Model Actually Do?
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It reads a dating bio and outputs personality tags.
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**Input:**
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> *"I'm a retired teacher who gardens, reads history books, and bakes sourdough."*
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**Output:**
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> *intellectual, family-oriented, gardener, history-buff, old-soul*
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That's it. No small talk. No life advice. Just tags.
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---
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## π Why "Download on Half Faith"?
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Because it works about 50% of the time right now. The other 50%? Let's call it "creative interpretation." We're working on it. The 360M version is much better than the original 135M prototype that once tagged everyone as "adventurous, creative, empathetic" regardless of input.
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**We'll get there. Just not today. Today is chaos.**
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---
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## β‘ Quick Test (If You're Brave)
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```python
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from transformers import AutoModelForCausalLM, AutoTokenizer
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model = AutoModelForCausalLM.from_pretrained("SpiceeChat/Bio2Tags-Lite", dtype="auto", device_map="auto")
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tokenizer = AutoTokenizer.from_pretrained("SpiceeChat/Bio2Tags-Lite")
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bio = "I love hiking at sunrise and brewing craft beer on weekends."
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prompt = f"Extract personality tags from the bio below. Output ONLY comma-separated tags, nothing else.\n\nBio: {bio}\n\nTags:"
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messages = [{"role": "user", "content": prompt}]
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formatted = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
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inputs = tokenizer(formatted, return_tensors="pt").to(model.device)
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outputs = model.generate(**inputs, max_new_tokens=50, temperature=0.7)
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tags = tokenizer.decode(outputs[0][inputs['input_ids'].shape[1]:], skip_special_tokens=True)
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print(tags)
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```
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---
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## π Status
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| Thing | Status |
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|-------|--------|
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| Model | π‘ Works 50% of the time |
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| Real README | π΄ Stuck in traffic, ETA tomorrow |
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| Developer | π‘ Running on caffeine and blind faith |
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| Cinder-1.5B | π‘ Still training on Kaggle |
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| Atom-350M | βͺ Coming soon |
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| Sleep | π΄ Not happening |
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---
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## π§ Part of SpiceeChat
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Built by **QuantaSparkLabs** for **SpiceeChat** β tools and AI to help people navigate the messy world of dating.
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- π [dating-fatigue.com](https://dating-fatigue.com)
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- π₯ [Cinder-1.5B](https://huggingface.co/SpiceeChat/Cinder-1.5B)
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- π·οΈ [Bio2Tags-Lite](https://huggingface.co/SpiceeChat/Bio2Tags-Lite) β You are here
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---
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<div align="center">
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<sub>π The real README is on the 405. It'll be here tomorrow. Probably.</sub>
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</div>
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