AINovice2005/quantized-SmolLM3-3B

SmolLM3‑3B • Quantized

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🚀 Model Description

This is an int8 quantized version of SmolLM3–3B, a highly efficient, open-source 3 B parameter LLM. It delivers nearly state-of-the-art multilingual reasoning and long-context performance (up to 128k tokens) with drastically reduced memory usage and inference cost, enabling fast deployment on mid‑range GPUs and edge devices.

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📏 Quantization Details

• Library: torchao
• Precision: int8 weights and activations
• Benefits: ~50–75% reduction in VRAM usage, enabling 12–16 GB GPU usage, with minimal performance drop on reasoning, coding, and long-context abilities

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🎯 Intended Use

Ideal for:

• Scenarios requiring fast LLM inference under constrained VRAM (e.g. small servers or laptops)
• Multilingual reasoning tasks, chain-of-thought logic, and long-context document understanding
• Deployments of dual-mode (think/no_think) conversational agents
• Research into efficient LLM deployment and quantization techniques

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⚠️ Limitations

• Slight performance loss compared to full-precision SmolLM3‑3B
• Requires proper benchmarking in your actual environment
• Continues to exhibit standard LLM risks: hallucination, bias, etc.
• Quant performance may vary across languages or context lengths