GLM-5.3-Flash-Coder-RCO-152E-GGUF

모델 소개

> **Update (October 29, 2026): Q3_K_M and Q4_K_M republished with an anti-loop fine-tune merged in.** A small > LoRA trained with verifiable-reward RL (GRPO with distillation from the full model) on multi-language coding tasks > removes the reasoning loops of the quantized files. Measured on Q3_K_M with the fine-tune applied as an adapter (40 held-out coding tasks x 2, and the Aider > "hmm" test, without any logit bias). The republished files contain the same fine-tune merged into the weights: > > | Q3_K_M | Before | After | > |---|---|---| > | Held-out coding tasks: looping runs | 11 of 80 | **0 of 80** | > | Held-out coding tasks: solved | 46 of 80 | **51 of 80** | > | Aider "hmm" test: exercises with a loop | 4 of 14 | **0 of 14** (14 of 14 solved) | > > The `--logit-bias` "hmm" ban below is no longer needed for these files. Q8_0 is unchanged: it does not show the > loop. The previous Q3_K_M / Q4_K_M files remain available in the repository history. > **New release (October 2026).** This repository used to hold REAP144, a 144-expert version calibrated on Python > agentic sessions only. REAP144 looped on general questions and on other programming languages and could get simple > arithmetic wrong. It is replaced by **RCO-152E**: 152 of 288 experts per layer, chosen by optimization on a mix of > agentic sessions, code in 21 languages, math and general text, then healed. The old REAP144 files were removed; they > remain available in the repository history. Recipe, mask, patches and evaluation harness: github.com/lapadatbogdan/GLM-5.3-Flash-Coder-RCO-152E

주요 특징

特点摘要待依据来源补齐，不凭模型名称推断能力。

한계 및 유의 사항

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출처별 요금 및 비용 분석

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공개 평가 및 실행 설정

暂无已采集的可核验公开评分。模型未上榜不代表能力较低。

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출처 및 추적 기록

许可证：mit。模型介绍与第三方评测均以原来源的具体版本和方法为准。