RecursiveSI/flint-int4-gguf vs UD

#5
by engrtipusultan - opened

Hi

How does UD 4 bit XL compare to RecursiveSI/flint-int4-gguf in accuracy?

I would rather like to know the comparison to the nvfp4 gguf

Quantization. After training, our Eureka Machine searched for quantization recipes that shrink Flint’s memory footprint while preserving its capabilities. Against common practice, it quantized the output layer (lm_head) to 4 bits. Because Flint has only 2.5 billion active parameters, its output layer is a large share of the weights read for each token. To recover accuracy, it added an offline distillation stage in which the quantized model trains on its own pre-sampled responses from a self-discovered coreset, with the 16-bit model as teacher. This nearly halved the number of training steps compared with our quantization-aware training baseline. The final NVFP4 model scores within 0.7 points of the 16-bit model across our benchmarks.

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