⚡ Noir-Lightning

Noir Family | Benchmarks | Quickstart

Noir-Lightning is a "pocket-sized" intelligence. It is the lightest and fastest model in the Noir family, built on the Qwen 2.5 architecture. Despite its tiny size (only 0.5 billion parameters), it performs at the level of models many times its size.

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🚀 Why Noir-Lightning?

Small models often struggle with consistency, identity confusion, or producing nonsensical wordplay. In this version, we have addressed these core issues:

• ✅ Identity Clarity: The model clearly understands it is an AI. No more confusing phrases like "I am the artificial intelligence of artificial intelligence."
• 🗣 Natural Language: Significant improvements in English and Russian fluency. It captures nuances and context, maintaining a natural conversational flow without "robotic" artifacts.
• 🧠 Outperforming Competitors: It surpasses newer models (like Qwen3-0.6B) in logic, reasoning, and mathematical tasks.
• ⚡ Extreme Efficiency: Runs instantly on low-end laptops, smartphones, and even directly in the browser.

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📊 Benchmark Results

We tested the model against rigorous academic benchmarks. Here is what these numbers mean for such a compact model:

Task / Category	Metric	Result (%)	Stderr (%)
GSM8K (Primary Math)	exact_match	10.0%	± 10.0%
MMLU Pro (Aggregate)	exact_match	13.57%	± 2.96%
∟ Physics	exact_match	30.0%	± 15.28%
∟ History	exact_match	20.0%	± 13.33%
∟ Computer Science	exact_match	20.0%	± 13.33%
∟ Engineering	exact_match	20.0%	± 13.33%
∟ Psychology	exact_match	20.0%	± 13.33%
∟ Business	exact_match	20.0%	± 13.33%
∟ Biology	exact_match	10.0%	± 10.0%
∟ Chemistry	exact_match	10.0%	± 10.0%
∟ Economics	exact_match	10.0%	± 10.0%
∟ Law	exact_match	10.0%	± 10.0%
∟ Philosophy	exact_match	10.0%	± 10.0%
∟ Other	exact_match	10.0%	± 10.0%
∟ Health	exact_match	0.0%	± 0.0%
∟ Math	exact_match	0.0%	± 0.0%

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Note: The results are based on a 5-shot evaluation where applicable.

Summary

In its weight class (0.5B), this model is a true champion, especially in hard sciences and logical consistency.

Domain	Success Rate	Commentary
Physics	30%	🏆 Exceptional result. Solves problems better than many models 2-3x its size.
History & Humanities	20%	Strong grasp of dates, events, and cultural context.
IT & Programming	20%	Understands code structures and basic algorithmic logic.
General Intelligence (MMLU Pro)	13.6%	An "A-tier" performance for the 0.5B parameter segment.
Mathematics (GSM8K)	10%	Capable of solving multi-step primary school logic problems.

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📦 Noir Model Family

The Noir series is designed to scale with your needs:

1. Lightning (0.5B) — Ultra-fast. Perfect for Edge devices and simple automation.
2. Mini (1.5B) — The "Sweet Spot" between speed and reasoning.
3. Standard (3B) — The versatile workhorse for advanced chatbots and assistants.
4. Ultra (7B) — Maximum power for complex reasoning and deep knowledge.

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🛠 Quick Start

You will need the transformers library. Here is the implementation using the high-level pipeline API:

from transformers import pipeline

# Initialize the pipeline
pipe = pipeline("text-generation", model="muverqqw/Noir-Lightning", device_map="auto")

# Chat-template based request
messages = [
    {"role": "system", "content": "You are Noir, a helpful AI assistant."},
    {"role": "user", "content": "Explain simply: why is the sky blue?"}
]

# Generation
outputs = pipe(messages, max_new_tokens=150, do_sample=True, temperature=0.7)
print(outputs[0]['generated_text'][-1]['content'])

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⚙️ Technical Specifications

• Architecture: Transformers-based Causal Decoder (Qwen 2.5)

• Training Data: Fine-tuned on a curated blend of logical reasoning, high-quality dialogue, and mathematical datasets.

• Format: Available in float16. GGUF/EXL2 versions coming soon.

• Context Length: Supports up to 32k tokens (optimal performance within 4k-8k).

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👤 About the Developer

• Creator: IceL1ghtning

• Release Year: 2025

• Base Architecture: Qwen 2.5

• License: Apache 2.0 (Commercial use permitted)

_{Built with a passion for open-source and efficient computing.}