media Hugging Face Forums · 47d ago · open_models

Buddy System: Rust entropy monitor with NER-gated uncertainty for tiered LLM inference

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The Buddy System uses a Rust entropy monitor to detect per-token uncertainty in local Gemma 3 4B inference, routing only uncertain tokens to Sonnet via NER-gated span extraction and semantic retrieval. Benchmarks show it achieves 71.4% accuracy at $0.21, outperforming the Anthropic Advisor pattern (62.9% at $0.44) across seven Hugging Face datasets, with a key improvement on SQuAD v2 by routing source passage chunks to the cloud model.

Importance 2/3 Beats a top-lab benchmark Trust 1/3 Hugging Face Forums Hugging Face Code generation Reasoning models

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arxiv arXiv cs.CL · 51d ago · 1 view

Zero-Shot Agentic LLMs Extract Lung Pathology from Narratives

A zero-shot agentic workflow using open-source LLMs extracts 13 College of American Pathologists synoptic fields from lung resection pathology reports. The best model (GPT-OSS-20B) achieved a Micro-F1 of 0.893, outperforming baseline recall and accurately capturing complex pathologic relations without task-specific training.

media r/LocalLLaMA · 52d ago · 1 view

Laguna M.1: 225B Parameter MoE Model for Agentic Coding

Laguna M.1 is a 225B-parameter mixture-of-experts model with 23B activated parameters per token, designed for agentic coding and long-horizon tasks. It achieves competitive performance on SWE-bench Verified (74.6%), SWE-bench Multilingual (63.1%), and Terminal-Bench 2.0 (45.8%), outperforming models like Devstral 2 and GLM-4.7 on key benchmarks.

media r/LocalLLaMA · 53d ago · 2 views

LoopCoder-V2: Two-Loop PLT Model Achieves Best Gain-Cost Trade-Off

LoopCoder-V2 is a 7B instruction-tuned code model based on Parallel Loop Transformer (PLT), trained on 18T tokens of mixed text and code data. The two-loop variant achieves the best gain-cost balance, improving SWE-bench Verified from 43.0 to 64.4, while three or more loops result in regression due to increasing positional mismatch and unstable updates.

arxiv arXiv cs.CL · 53d ago · 2 views

SwiftTrans Improves LLM Code Translation Efficiency

SwiftTrans addresses runtime efficiency gaps in LLM-based code translation by introducing Multi-Perspective Exploration and Difference-Aware Selection. The framework extends CodeNet, F2SBench, and introduces SwiftBench to evaluate runtime performance, showing consistent improvements in both correctness and efficiency across benchmarks.

media r/LocalLLaMA · 41d ago · 5 views

CPU-only GLM 5.2: Epyc and 512GB RAM

A user tested the 4-bit version of GLM-5.2 (GLM-5.2-UD-Q4_K_XL) on a server equipped with an Epyc Rome 7452 processor and 512GB of RAM. The model was evaluated using a complex coding prompt requiring the creation of a self-contained 3D arena game in HTML, CSS, and JavaScript.