LamoFast-2.0-GGUF

🚀 LamoFast-2-Supernova

LamoFast-2-Supernova is a high-performance, specialized LLM based on the Qwen2.5-0.5B architecture. Optimized by Raziel AI Learning, this model is fine-tuned for high-speed inference and domain-specific expertise in space exploration, astrophysics, and advanced robotics.

Designed to be "Supernova Fast," it provides concise, intelligent, and context-aware responses, making it the perfect choice for edge devices, mobile applications, and real-time robotic interfaces. 🌌✨

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🛰️ Key Features

• Architecture: Qwen2.5-0.5B (State-of-the-art small-scale LLM).
• Expertise: Deep knowledge in Planetary Science, Space Missions, and Robotics.
• Efficiency: Ultra-lightweight footprint with lightning-fast token generation.
• Context Window: Supports up to 1024 tokens.
• Format: Optimized GGUF for seamless integration with Llama.cpp and local LLM runners.
• Identity: Developed and trained by Raziel AI Learning. 🤖🚀

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🧠 Capabilities

LamoFast-2-Supernova excels at:

• Mission Planning: Describing robotic landing procedures and orbital maneuvers.
• Space Education: Explaining stellar evolution, planetary atmospheres, and cosmic phenomena.
• Robotic Commands: Providing structured logic for robotic sensors and instruments.
• Interactive Chat: Engaging, personality-driven conversations with a focus on science and exploration.

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🛠️ Technical Specifications

• Model Type: Causal Language Model
• Language: English (Primary)
• Parameters: 0.5 Billion
• Inference Speed: ~50-70 tokens per second (hardware dependent).
• Quantization: Highly efficient GGUF versions available for maximum performance.

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💻 Quick Start (Python)

from llama_cpp import Llama

# Initialize LamoFast-2-Supernova
llm = Llama(model_path="LamoFast_2_0.gguf", n_ctx=1024)

prompt = "<|user|>\nDescribe the surface of Mars and its robotic potential.<|im_end|>\n<|assistant|>\n"
output = llm(prompt, max_tokens=512, stop=["<|im_end|>"])
print(output['choices'][0]['text'])