Instructions to use 11-47/SmolLM-135M-Tarot-Zodiac-Reasoning with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use 11-47/SmolLM-135M-Tarot-Zodiac-Reasoning with Transformers:

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

pipe = pipeline("text-generation", model="11-47/SmolLM-135M-Tarot-Zodiac-Reasoning")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("11-47/SmolLM-135M-Tarot-Zodiac-Reasoning")
model = AutoModelForCausalLM.from_pretrained("11-47/SmolLM-135M-Tarot-Zodiac-Reasoning", device_map="auto")
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 11-47/SmolLM-135M-Tarot-Zodiac-Reasoning with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "11-47/SmolLM-135M-Tarot-Zodiac-Reasoning"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "11-47/SmolLM-135M-Tarot-Zodiac-Reasoning",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/11-47/SmolLM-135M-Tarot-Zodiac-Reasoning

SGLang

How to use 11-47/SmolLM-135M-Tarot-Zodiac-Reasoning 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 "11-47/SmolLM-135M-Tarot-Zodiac-Reasoning" \
    --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": "11-47/SmolLM-135M-Tarot-Zodiac-Reasoning",
		"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 "11-47/SmolLM-135M-Tarot-Zodiac-Reasoning" \
        --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": "11-47/SmolLM-135M-Tarot-Zodiac-Reasoning",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use 11-47/SmolLM-135M-Tarot-Zodiac-Reasoning with Docker Model Runner:
```
docker model run hf.co/11-47/SmolLM-135M-Tarot-Zodiac-Reasoning
```

SmolLM-135M-Tarot-Zodiac-Reasoning

By 11-47

A fine-tuned 135M parameter model specialized in Tarot interpretation, Zodiac insights, and mystical reasoning. Built on top of HuggingFaceTB/SmolLM-135M-Instruct using Supervised Fine-Tuning (SFT) with TRL.

This tiny but mighty model blends modern LLM capabilities with esoteric knowledge—perfect for lightweight tarot readings, astrological guidance, symbolic reasoning, and creative spiritual conversations on edge devices or low-resource setups.

Model Details

Base Model: HuggingFaceTB/SmolLM-135M-Instruct
Parameters: 135M (super efficient!)
Tensor Type: F32 (Safetensors)
Training Method: SFT (Supervised Fine-Tuning) via TRL
Datasets:
- 11-47/The_Prophet_Tarot_Zodiac_50K (main dataset)
- 11-47/The_Prophet_Tarot_Zodiac_5K
- 11-47/The_Prophet_Tarot_Zodiac_5K_seed (seed / validation)

The training focuses on conversational tarot spreads, zodiac interpretations, symbolic reasoning, and blending mystical traditions with logical explanations.

Quick Start

from transformers import pipeline

generator = pipeline(
    "text-generation",
    model="11-47/SmolLM-135M-Tarot-Zodiac-Reasoning",
    device="cuda"  # or "cpu"
)

question = "What does The Tower card mean for someone with a Scorpio rising going through a major life transition?"
output = generator(
    [{"role": "user", "content": question}],
    max_new_tokens=256,
    return_full_text=False
)[0]

print(output["generated_text"])
Intended Uses
    •	Tarot Readings: Generate insightful interpretations of cards and spreads (single card, 3-card, Celtic Cross, etc.).
    •	Zodiac & Astrology: Personalized horoscopes, compatibility analysis, and planetary transits.
    •	Mystical Reasoning: Symbolic interpretation, dream analysis, spiritual advice with logical structure.
    •	Lightweight Deployment: Runs great on phones, laptops, or embedded devices.
    •	Fun / Educational spiritual chatbots.
Limitations
    •	Very small model (135M) → Can be less coherent on complex multi-card spreads or highly nuanced queries compared to larger models.
    •	Knowledge is synthetic / training-data driven — treat outputs as inspirational rather than definitive divination.
    •	May occasionally hallucinate card meanings or astrological facts.
    •	Not a replacement for professional tarot/astrology consultation.
Training Procedure
    •	Framework: Hugging Face TRL (Transformers Reinforcement Learning library)
    •	Method: SFT on assistant responses in chat format.
    •	Framework Versions:
    ◦	TRL: 1.8.0
    ◦	Transformers: 5.13.1
    ◦	PyTorch: 2.10.0+cu128
    ◦	Datasets: 5.0.0
Citation
@misc{smollm-tarot-zodiac-reasoning,
  author = {11-47},
  title = {SmolLM-135M-Tarot-Zodiac-Reasoning},
  year = {2026},
  publisher = {Hugging Face},
  url = {https://huggingface.co/11-47/SmolLM-135M-Tarot-Zodiac-Reasoning}
}

# Cite TRL
@software{vonwerra2020trl,
  title   = {{TRL: Transformers Reinforcement Learning}},
  author  = {von Werra, Leandro and others},
  url     = {https://github.com/huggingface/trl},
  year    = {2020}
}

Downloads last month: 1,659

Safetensors

Model size

0.1B params

Tensor type

F32

Model tree for 11-47/SmolLM-135M-Tarot-Zodiac-Reasoning

Base model

HuggingFaceTB/SmolLM-135M

Quantized

HuggingFaceTB/SmolLM-135M-Instruct

Finetuned

(193)

this model

11-47
/

SmolLM-135M-Tarot-Zodiac-Reasoning

SmolLM-135M-Tarot-Zodiac-Reasoning

Model Details

Quick Start

Model tree for 11-47/SmolLM-135M-Tarot-Zodiac-Reasoning

Datasets used to train 11-47/SmolLM-135M-Tarot-Zodiac-Reasoning