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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**

Text generation
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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.

Visual QA
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**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.

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