Unverified50% confidenceBenchmarkExact time
On 85 trained open-source-weight Qwen and Llama adapters, LoRAForge-S's lower-bound quality configuration retained only about 7.7% of the adapter parameters, while the overall average score dropped by at most 1.2 points on a 0–100 scale.
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Long-term
Relevance
11/3/2026
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Related Claims
UnverifiedAn early iteration scored five percentage points lower than the Huihui base model on the IFEval instruction-following benchmark.70% similarVerifiedLoRA fine-tuning requires training less than 1% of parameters to achieve results close to full-parameter fine-tuning, with VRAM requirements significantly reduced to a range affordable by a single consumer-grade GPU.69% similarUnverified使用Unsloth进行LoRA/QLoRA微调时，显存占用可减少约50%-70%69% similarUnverifiedIn the placebo test, 90% of the synthetic control method estimates narrowed to between -7% and +9%, with RMSE dropping to 4.9 percentage points, more than one-third lower than DiD.68% similarUnverifiedQwen 3.8 is slower than 3.6, with prefill and inference speeds dropping by approximately 40% and 20% respectively.68% similar
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