Noir-Lightning

⚡ Noir-Lightning

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Noir Family | Benchmarks | Quickstart

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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:

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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.

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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)

<div align="center"> _{Built with a passion for open-source and efficient computing.} </div>