HyzeMini
A lightweight text-generation model by Hyze AI
π hyzebot.vercel.app β’ π hyzedocs.vercel.app β’ π§ hyzecode.vercel.app
π Overview
HyzeMini is a compact and efficient text-generation transformer model optimized for Space & Astronomy knowledge π and General Chat π¬.
Itβs designed to run fast on low-resource systems while still delivering clean, friendly, and useful responses.
- Model type: Transformer-based LLM
- Parameters: ~0.1B
- Precision: BF16
- Language: English
- License: Apache-2.0
π§ Training Focus
HyzeMini was trained on a curated mixture of publicly available English datasets, with emphasis on:
π Space & Astronomy
- Planets, stars, galaxies
- Rockets, missions, and space science
- Beginner to intermediate explanations
π¬ General Chat
- Casual conversation
- Q&A-style prompts
- Friendly assistant tone
π¬ About the Founder
Hyze AI was created by Hitesh Vinothkumar who is 12 years old. Hyze focuses on learning, experimentation, and open access, blending software engineering with curiosity about the universe
π Benchmarks (Qualitative Comparison)
HyzeMini focuses on speed, coherence, and domain knowledge rather than raw reasoning power.
| Model | Size | Strengths | Tradeoffs |
|---|---|---|---|
| HyzeMini | ~0.1B | Space-focused knowledge, fast, chat-friendly | Limited deep reasoning |
| TinyLlama | ~0.1B | Solid general generation | More generic responses |
| GPT-Neo 125M | ~0.125B | Better general reasoning | Slower, higher memory |
| GPT-1 | ~0.117B | Historical baseline | Less coherent by modern standards |
Summary
- Coherence: HyzeMini β TinyLlama > GPT-1
- Space knowledge: HyzeMini > TinyLlama / GPT-Neo (in-domain prompts)
- Efficiency: HyzeMini β TinyLlama > GPT-Neo
Benchmarks are based on internal qualitative testing and comparisons.
π§ͺ Usage
Transformers (Python)
from transformers import pipeline
generator = pipeline(
"text-generation",
model="HyzeAI/HyzeMini"
)
print(generator("Tell me a cool space fact:"))
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