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Ling-3.0-tiny model achieves speedup with llama.cpp DFlash draft

By PulseAugur Editorial · [1 sources] ·

A user has created a DFlash draft for the Ling-3.0-tiny model, which can be run with the stock llama.cpp inference engine. Initial tests show an 8% speedup for prose generation and a 61% speedup for Python code generation compared to the baseline. The user has made minor patches to llama.cpp's GGUF converter to achieve this, and the converted model and conversion procedure are available on Hugging Face. AI

IMPACT Demonstrates a potential optimization for local LLM inference, improving speed for specific tasks.

RANK_REASON User-created draft of a model optimization technique for an open-source inference engine. [lever_c_demoted from research: ic=1 ai=1.0]

Read on r/LocalLLaMA →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

Ling-3.0-tiny model achieves speedup with llama.cpp DFlash draft

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User-created draft of a model optimization technique for an open-source inference engine. [lever_c_demoted from research: ic=1 ai=1.0]
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model release, infra
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COVERAGE [1]

  1. r/LocalLLaMA TIER_1 English(EN) · /u/Barni275 ·

    Ling-3.0-tiny-UBTree DFlash draft for stock mainline llama.cpp (+8% prose, +61% code speedup)

    <!-- SC_OFF --><div class="md"><p>I use Ling-3.0-tiny on my LLM server for text processing. I convert a lot of PDF documents to markdown for downstream LLM processing, and I built my custom harness for it. It uses ONNX model to parse layout, extracts text, and then runs LLM proce…

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