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11-47
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Model Details
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11-47
Model Tree
HuggingFaceTB/SmolLM-135M-Instruct
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Output Modalities
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HuggingFaceTB/SmolLM-135M-Instruct11-47/The_Prophet_Tarot_Zodiac_50K (main dataset)11-47/The_Prophet_Tarot_Zodiac_5K11-47/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.
python
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: 5.0.0Citation@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}}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: 5.0.0Citation@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}}