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README.md

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 # TinyStories Mixtral 2M Top-2 MoE (tinymoe2m) GGUF & HF Validation Suite (4k Context)
 
-This repository provides an ultra-lightweight Mixtral model variant (a Mixture-of-Experts architecture utilizing the Llama 2 compute topology) scaled down to a **1.95M total parameter footprint** and a **1.14M active parameter execution frame**. It is trained on the TinyStories dataset and optimized as a precise validation asset.
+This repository provides an ultra-lightweight Mixtral model variant (a Mixture-of-Experts architecture utilizing the Llama 2 compute topology) scaled down to a 1.95M total parameter footprint and a 1.14M active parameter execution frame. It is trained on the TinyStories dataset and optimized as a precise validation asset.
 
-Following extensive long-context scaling evaluations, this asset has been calibrated to a **4,096 token context window (4k)** with an adjusted **RoPE base frequency (`rope_theta`) of 15,000.0** to prevent numerical saturation under FP32 precision boundaries while maintaining sharp localized attention coordinates.
+This asset is calibrated to a 4,096 token context window (4k) with an adjusted RoPE base frequency (`rope_theta`) of 15,000.0 to maintain sharp localized attention coordinates.
 
-It is designed specifically for debugging custom inference engines, and native tensor compilers against MoE-specific runtime features. These include Gating network weight allocation, token distribution/gathering (Scatter/Gather loops), and the weighted addition combining multiple independent expert outputs.
+It is **designed specifically for debugging** custom inference engines, and native tensor compilers against MoE-specific runtime features. These include Gating network weight allocation, token distribution/gathering (Scatter/Gather loops), and the weighted addition combining multiple independent expert outputs.
 
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