MODEL PROFILE

JQ2_K-JQ6_K-Test-Models

eaddario · JQ2_K-JQ6_K-Test-Models

Quantization test results for **K2-Horizon-7B** (dense) and **LFM2.5-8B-A1B** (MoE), comparing standard llama.cpp quantization against a fork that adds five new non-linear super-block types: **JQ2_K** to **JQ6_K**. On both models, four of the five new types perform materially better than their counterpart on every measured metric (mean and 99% KLD, Δp RMS, top-1 agreement). `JQ2_K` is the exception. The size of the gain depends on the model: on the dense `K2-Horizon-7B`, `JQ3_K` and `JQ6_K` cut the mean KLD by more than half, while on the MoE `LFM2.5-8B-A1B` the improvements range from 13.8% to 23.2%. The five new types are non-linear (NL) super-block (K) types. Each new type spends exactly the same index payload as its K-quant counterpart:

Text generationlicense:apache-2.0
Context windowNot disclosed
Input → OutputText → Text
Model weightsNot verified
Profile updated2026/10/30 20:18:02
OVERVIEW

Model overview

Quantization test results for **K2-Horizon-7B** (dense) and **LFM2.5-8B-A1B** (MoE), comparing standard llama.cpp quantization against a fork that adds five new non-linear super-block types: **JQ2_K** to **JQ6_K**. On both models, four of the five new types perform materially better than their counterpart on every measured metric (mean and 99% KLD, Δp RMS, top-1 agreement). `JQ2_K` is the exception. The size of the gain depends on the model: on the dense `K2-Horizon-7B`, `JQ3_K` and `JQ6_K` cut the mean KLD by more than half, while on the MoE `LFM2.5-8B-A1B` the improvements range from 13.8% to 23.2%. The five new types are non-linear (NL) super-block (K) types. Each new type spends exactly the same index payload as its K-quant counterpart:

Key features

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Limitations & considerations

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PRICING & COST

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INDEPENDENT BENCHMARKS

Public benchmarks & run configuration

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Sources & traceability

许可证:apache-2.0。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。