An 11 GB GGUF of Sarvam-30B that runs on a CPU with about 12 GB of free RAM. It scores 81.7% on MMLU, against 85.1 published for the full model. Sarvam-30B is a mixture-of-experts model: 32 billion weights, about 2.4 billion of them active per token, which is why a CPU can run it. Use the IQ2M file. The IQ1M file is here for comparison: it saves 3 GB but loses much more quality. For the larger Sarvam-105B, see Aaravomega/sarvam-105b-GGUF-IQ2M-IQ1M (2-bit and 1-bit) and Aaravomega/sarvam-105b-GGUF-Q80 (8-bit). You need a recent llama.cpp build with Sarvam support (architecture bailingmoe2; tested with release b11731) and about 12 GB of free RAM. Then open http://127.0.0.1:8080, or call the…
Independent publisher
Aarav Agarwal
Aaravomega
Models in Library3
Datasets in Library0
Models on Hugging Face3
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Published tasks: Text generation.
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