> > The legacy build suffered from severe recurrent state instability, context degradation, and unintended language mixing during generation. > > **The current release completely resolves these issues:** > - Complete long-context associative recall and conversational stability restored. > - Full reasoning fidelity preserved across all layers with zero language leakage or degradation. > - Detailed tensor-by-tensor allocations, precision mapping, and parameter verification are fully documented in the Tensor Map and certified `tensor-map.tsv` file. > > **Action Required:** Please delete any older `.gguf` file downloaded prior to today and download the newly published certified build: **`Qwen3.8-27B-EfficientThink-VAL-APEX-I-NanoPlus-V3.gguf`**. If you consider these releases to be of high quality and they help you in your day-to-day workflow, consider supporting on Ko-fi. Every contribution directly sustains future cloud compute hours and helps support my university studies. Official **VAL-APEX-I** quantization of **nerkyor/Qwen3.8-27B-Coder390-EfficientThink-Opus5.5-GPT6Astra-Grok4.7-DSV4Pro-K3-SFT-RLOO-MTP-DFlash2**.
译文排队中 · 当前显示来源原文
输入 文本→ 输出 文本
If you consider these releases to be of high quality and they help you in your day-to-day workflow, consider supporting on Ko-fi. Every contribution directly sustains future cloud compute hours and helps support my university studies. Official **VAL-APEX-I MiniPlus V3** quantization of **Qwen/Qwen3.8-27B**. This edition quantizes the original Qwen checkpoint without applying Huihui abliteration or EfficientThink fine-tuning. **Integrated Multi-Token Prediction (MTP):** This GGUF includes one MTP block (`blk.64.*`, 15 tensors) in the same file as the main language model. Its eight matrices use `Q4_0` ×6, `Q4_1` ×1 and `Q6_K` ×1; its seven normalization tensors remain `F32`. MTP speculative decoding depends on support and configuration in the inference backend.
译文排队中 · 当前显示来源原文
输入 文本→ 输出 文本
基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
输入 文本→ 输出 文本
If you consider these releases to be of high quality and they help you in your day-to-day workflow, consider supporting on Ko-fi. Every contribution directly sustains future cloud compute hours and helps support my university studies. 1. APEX MLX Variants 2. Fidelity Verification 3. Quality Spectrum: APEX MLX MiniPlus vs. NanoPlus 4. Model Files & Technical Specifications 5. Surgical Tensor Quantization Map 6. Apple Silicon Unified Memory & Context 7. Recommended Configuration & Setup 8. Recommended Generation Parameters 9. CRITICAL: Coding Syntax & Repeat Penalty Advisory 10. Native Chat Template 11. 7. Community Compute Fund & Student Support > [!WARNING] > ### Known Upstream Long-Generation Limitation > The upstream Qwen3.8-35B-A3B-Distill checkpoint can repeat during very long responses, including after the ` ` block. This behavior originates upstream and is **not specific to the MLX APEX quantization**.
译文排队中 · 当前显示来源原文
输入 文本→ 输出 文本
基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
输入 文本→ 输出 文本
**Independently computed using dedicated cloud compute clusters, funded 100% out-of-pocket.** If you consider these releases to be of high quality and they help you in your day-to-day workflow, consider supporting on Ko-fi. Every contribution directly sustains future cloud compute hours and helps support my university studies. > > *Independently computed on private cloud clusters. If this handcrafted release saves you VRAM and runs faster on your GPU, consider fueling the community compute fund on Ko-fi.* > > Explore the new canonical V3 releases. MiniPlus prioritizes Q5-class to near-Q6 fidelity without a fixed file-size ceiling; NanoPlus targets Q4-class quality at a practical footprint. > > - **Qwen3.8 27B — MiniPlus V3** — 14.16 GB **(+13.21% improvement in WikiText-2 PPL vs GSQ-RCO IQ3_S)**. > - **Qwen3.8 27B — NanoPlus V3** — 11.35 GB **(+11.49% improvement in WikiText-2 PPL vs GSQ-RCO IQ3_S)**. > - **Swift-1.5 Qwen3.8 27B Uncensored MTP — MiniPlus V3** — 14.28 GB **(+5.70% improvement in WikiText-2 PPL vs GSQ-RCO IQ3_S)**. > - **Swift-1.5 Qwen3.8 27B Uncensored MTP — NanoPlus V3** — 11.08 GB **(+2.26% improvement in WikiText-2 PPL vs GSQ-RCO IQ3_S)**. > > Percentages compare the reported WikiText-2 PPL with GSQ-RCO IQ3_S (7.07). The linked 27B cards document the evaluation protocol and model variants. > > **Explore the APEX-I MiniPlus V2.1 Abliterated collection** > > ### EXPLORE THE ESTABLISHED 35B MoE MINIPLUS LINEUP > These are complementary APEX-I-MiniPlus V2.1 releases, not alternate downloads of the same model. Each receives the same tensor-by-tensor approach, integrated MTP where supported, and a design suitable for full or partial system-RAM inference. Choose the model whose native strengths best fit the work you want to do: > > - **Qwen3.6-35B-A3B MTP APEX-I-MiniPlus-V2.1** — a versatile frontier MoE for broad reasoning, multilingual work, agents, tool use, and multimodal tasks. > - **Best for:** General reasoning, agent workflows, tool calling, and flexible multimodal use. > - **Qwen3.6-35B-A3B MTP APEX-I-MiniPlus-V2.1 Abliterated** — the V2.1 refusal-ablated Qwen3.6 edition for users who deliberately prefer reduced refusal behavior. > - **Best for:** Workflows where an abliterated Qwen3.6 variant is explicitly desired. > - **Ornith 1.5 APEX-I-MiniPlus-V2.1** — a software-engineering-focused MoE designed for repository-scale coding and auton
译文排队中 · 当前显示来源原文
输入 文本→ 输出 文本
If you consider these releases to be of high quality and they help you in your day-to-day workflow, consider supporting on Ko-fi. Every contribution directly sustains future cloud compute hours and helps support my university studies. > > *Independently computed on private cloud clusters. If this handcrafted release saves you VRAM and runs faster on your GPU, consider fueling the community compute fund on Ko-fi.* > > Explore the new canonical V3 releases. MiniPlus prioritizes Q5-class to near-Q6 fidelity without a fixed file-size ceiling; NanoPlus targets Q4-class quality at a practical footprint. > > - **Qwen3.8 27B — MiniPlus V3** — 14.16 GB **(+13.21% improvement in WikiText-2 PPL vs GSQ-RCO IQ3_S)**. > - **Qwen3.8 27B — NanoPlus V3** — 11.35 GB **(+11.49% improvement in WikiText-2 PPL vs GSQ-RCO IQ3_S)**. > - **Swift-1.5 Qwen3.8 27B Uncensored MTP — MiniPlus V3** — 14.28 GB **(+5.70% improvement in WikiText-2 PPL vs GSQ-RCO IQ3_S)**. > - **Swift-1.5 Qwen3.8 27B Uncensored MTP — NanoPlus V3** — 11.08 GB **(+2.26% improvement in WikiText-2 PPL vs GSQ-RCO IQ3_S)**. > > Percentages compare the reported WikiText-2 PPL with GSQ-RCO IQ3_S (7.07). The linked 27B cards document the evaluation protocol and model variants. > > **Explore the APEX-I MiniPlus V2.1 collection** > > ### EXPLORE THE ESTABLISHED 35B MoE MINIPLUS LINEUP > These are complementary APEX-I-MiniPlus V2.1 releases, not alternate downloads of the same model. Each receives the same tensor-by-tensor approach, integrated MTP where supported, and a design suitable for full or partial system-RAM inference. Choose the model whose native strengths best fit the work you want to do: > > - **Qwen3.6-35B-A3B MTP APEX-I-MiniPlus-V2.1** — a versatile frontier MoE for broad reasoning, multilingual work, agents, tool use, and multimodal tasks. > - **Best for:** General reasoning, agent workflows, tool calling, and flexible multimodal use. > - **Qwen3.6-35B-A3B MTP APEX-I-MiniPlus-V2.1 Abliterated** — the V2.1 refusal-ablated Qwen3.6 edition for users who deliberately prefer reduced refusal behavior. > - **Best for:** Workflows where an abliterated Qwen3.6 variant is explicitly desired. > - **Ornith 1.5 APEX-I-MiniP
译文排队中 · 当前显示来源原文
输入 文本→ 输出 文本
基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
分类待补充
输入 待核验→ 输出 待核验
基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
分类待补充
输入 待核验→ 输出 待核验
基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
分类待补充
输入 待核验→ 输出 待核验
**Independently computed using dedicated cloud compute clusters, funded 100% out-of-pocket.** If you consider these releases to be of high quality and they help you in your day-to-day workflow, consider supporting on Ko-fi. Every contribution directly sustains future cloud compute hours and helps support my university studies. 1. Model Overview & Technical Specifications 2. Empirical Importance Matrix Calibration 3. Bundled Multimodal Vision Projector 4. Surgical Tensor Quantization Map 5. Recommended Server & Inference Configuration 6. Integrated MTP Speculative Decoding 7. Community Compute Fund & Student Support
译文排队中 · 当前显示来源原文
输入 文本 · 图像→ 输出 文本
基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
输入 文本 · 图像→ 输出 文本
**Independently computed using dedicated cloud compute clusters, funded 100% out-of-pocket.** If you consider these releases to be of high quality and they help you in your day-to-day workflow, consider supporting on Ko-fi. Every contribution directly sustains future cloud compute hours and helps support my university studies. Official **VAL-APEX-I MiniPlus V3** quantization of **ajgazin/Swift-1.5-Qwen3.8-27B-Uncensored-MTP**. This edition quantizes the uncensored foundation model built on UkisAI's Swift-1.5 reasoning fine-tune of Qwen3.8-27B with the official canonical V3 recipe (strictly enforcing `IQ4_XS` and protecting attention/recurrent matrices).
译文排队中 · 当前显示来源原文
输入 文本→ 输出 文本
If you consider these releases to be of high quality and they help you in your day-to-day workflow, consider supporting on Ko-fi. Every contribution directly sustains future cloud compute hours and helps support my university studies. Official **VAL-APEX-I NanoPlus V3** quantization of **ajgazin/Swift-1.5-Qwen3.8-27B-Uncensored-MTP**. This edition quantizes the uncensored foundation model built on UkisAI's Swift-1.5 reasoning fine-tune of Qwen3.8-27B with the V3 NanoPlus allocation (`IQ3_XXS` in the core MLP blocks, `IQ4_XS` on the outer layers 0-7 and 56-63, attention and recurrent matrices protected). VAL-APEX-I stands for: **Vector-calibrated Asymmetric Layer-wise Outlier-preserving Recurrent-aware Unified Matrix-quantization**
译文排队中 · 当前显示来源原文
输入 文本→ 输出 文本
基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
输入 文本→ 输出 文本
基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
基础资料已收录 · 缺失资料后续补充
输入 文本→ 输出 文本
基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
输入 文本→ 输出 文本
基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
基础资料已收录 · 缺失资料后续补充
输入 文本 · 图像→ 输出 文本
If you consider these releases to be of high quality and they help you in your day-to-day workflow, consider supporting on Ko-fi. Every contribution directly sustains future cloud compute hours and helps support my university studies. 1. Optimization History & Transparency Notice 2. Independent Benchmark of the APEX-I-MiniPlus Family (Occamy V2 Reference) 3. Empirical Benchmarks & Fidelity Verification 4. Quality Spectrum: APEX-I-MiniPlus V2.1 vs. Standard Flat Quantizations 5. Model Files & Technical Specifications 6. Surgical Tensor Quantization Map (Audited from GGUF) 7. Hardware Throughput & Offload Benchmarks (RTX 30 / 40 / 50 & RAM Streaming) 8. The 24GB Miracle: Full 256K Context Runs In VRAM! 9. Recommended Configuration & Setup 10. Recommended Generation Parameters (Tiel Coder Official) 11. CRITICAL: Coding Syntax & Repeat Penalty Advisory (Preventing Character Swapping) 12. Hardened Agentic Chat Template & Reasoning Effort 13. Optional Support > [!IMPORTANT] > ### THE DEFINITIVE SPECIFICATION IN THE 14–15 GB CEILING > This **APEX-I-MiniPlus-V2.1** release represents the specialized tensor-by-tensor configuration for sparse Mixture-of-Experts quantization within a **14–15 GB envelope**. Every tensor across its 40 layers, 256 micro-experts, and integrated MTP block has been mathematically allocated to maximize reasoning precision, preserve routing behavior, and prevent avoidable CPU dequantization stalls.
译文排队中 · 当前显示来源原文
输入 文本 · 图像→ 输出 文本
**Independently computed using dedicated cloud compute clusters, funded 100% out-of-pocket.** If you consider these releases to be of high quality and they help you in your day-to-day workflow, consider supporting on Ko-fi. Every contribution directly sustains future cloud compute hours and helps support my university studies. 1. Optimization History & Transparency Notice 2. Independent Community Benchmarks (OMP Harness #01 Rank - 98.8%) 3. Quality Spectrum: APEX-I-NanoPlus vs. Standard Flat Quantizations 4. Model Files & Technical Specifications 5. Surgical Tensor Quantization Map (Audited from GGUF) 6. Inference Quickstart 7. 1. llama-cli (Console Generation) 8. 2. llama-server (OpenAI-Compatible API) 9. CRITICAL: Coding Syntax & Repeat Penalty Advisory (Preventing Character Swapping) 10. Hardened Agentic Chat Template & Reasoning Effort 11. Optional Support > [!NOTE] > ### 🚀 EXPLORE THE ESTABLISHED 35B MoE MINIPLUS & NANOPLUS LINEUP > These are complementary APEX-I releases, not alternate downloads of the same model. Each receives the same surgical tensor-by-tensor approach and a design suitable for full or partial system-RAM inference: > > - **Tiel-Coder-35B-A3B APEX-I-MiniPlus-V2.1** — specialist agentic coding MoE tuned for high-speed local development (15.23 GB / Q5–Q6 quality). > - **Qwen3.6-35B-A3B-MTP APEX-I-NanoPlus** — the approx. 13.0 GB NanoPlus pioneer achieving Q4 quality at 2.93 BPW. > - **Ornith 1.5 APEX-I-NanoPlus** — software-engineering-focused MoE streamlined for ultra-lean memory footprints. > - **Occamy-1.0 APEX-I-NanoPlus** — versatile frontier MoE for deep research and multimodal tasks.
译文排队中 · 当前显示来源原文
输入 文本 · 图像→ 输出 文本
基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
输入 文本→ 输出 文本
基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
基础资料已收录 · 缺失资料后续补充
输入 文本→ 输出 文本
基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
输入 文本→ 输出 文本
基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
基础资料已收录 · 缺失资料后续补充
输入 文本 · 图像→ 输出 文本
