> [!WARNING] > ### CRITICAL UPDATE (October 23, 2026): ABLITERATION RESTORATION & APOLOGY > **To the community:** I made a mistake and I apologize for it: I accidentally uploaded the base quantizations to the "abliterated" section, effectively duplicating the same GGUF files. This is why the abliteration appeared to work in my local tests, while the versions originally available on Hugging Face were actually just the base models. > > **Important Notice for Previous Downloads:** If you downloaded `Occamy-1.0.APEX-I-MiniPlus-V2.1-Abliterated.gguf` prior to October 23, 2026, you downloaded an un-abliterated (non-abliterated) build. > > **The resolution:** The repository has been fully corrected. The binary has been completely re-quantized using the authentic BF16 directional abliterated source with importance matrix (`imatrix`) calibration under the canonical 733-tensor APEX-I V2.1 recipe. Refusal suppression is verified active across all probe vectors. > > **Please re-download `Occamy-1.0.APEX-I-MiniPlus-V2.1-Abliterated.gguf` (14.75 GB / 13.74 GiB, verified with tensor-map.tsv).** > > Additionally, I have also added a companion **Occamy-1.0 APEX-I-NanoPlus-V2.1 Abliterated** version (12.54 GB / 11.68 GiB) to the official collection for memory-constrained workflows. 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
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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. > > *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
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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. > *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.
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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. Quality Spectrum: APEX-I-NanoPlus vs. Standard Flat Quantizations 3. Model Files & Technical Specifications 4. Surgical Tensor Quantization Map (Audited from GGUF) 5. Inference Quickstart 6. 1. llama-cli (Console Generation) 7. 2. llama-server (OpenAI-Compatible API) 8. CRITICAL: Coding Syntax & Repeat Penalty Advisory (Preventing Character Swapping) 9. Hardened Agentic Chat Template 10. 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: > > - **Ornith-1.5-35B-A3B APEX-I-MiniPlus-V2.1** — software-engineering-focused MoE designed for repository-scale coding and autonomous engineering agents (15.23 GB / Q5_K_L tier, bordering Q6_K). > - **Qwen3.6-35B-A3B-MTP APEX-I-NanoPlus** — the approx. 13.0 GB NanoPlus pioneer achieving Q4 quality at 2.93 BPW. > - **Occamy-1.0 APEX-I-NanoPlus** — versatile frontier MoE for deep research and multimodal tasks.
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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. > > *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. > - **Ornith 1.5 APEX-I-MiniPlus-V2.1** — a software-engineering-focused MoE designed for repository-scale coding and autonomous engineering agents. > - **Best for:** Repository-scale development, multi-file code changes, and software-engineering agents. > - **Tiel Coder APEX-I-MiniPlus-V2.1** — a specialist coding MoE tuned for agentic programming, iterative tool use, and
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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 (Qwen Official) 11. CRITICAL: Coding Syntax & Repeat Penalty Advisory (Preventing Character Swapping) 12. Hardened Agentic Chat Template & Reasoning Effort 13. Optional Support > [!NOTE] > ### 🔓 LOOKING FOR THE UNCENSORED / ZERO-REFUSAL RELEASE? > If your workflow involves red-teaming, penetration testing, systems security research, or unrestricted generation, check out the official sister edition: > 🔗 **Qwen3.6-35B-A3B-MTP-APEX-I-MiniPlus-V2.1-Abliterated-GGUF** — authentic Heretic TPE directional abliteration with zero moralizing refusals, 14.66 GB footprint, dedicated Q8_0 MTP draft head & mmproj vision companions, and empirical 5.4595 WikiText-2 perplexity.
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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-NanoPlus 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 16GB & 24GB Advantage: Massive Context in VRAM! 9. Recommended Configuration & Setup 10. 1. llama-cli with Vision Projector 11. 2. High-Performance OpenAI-Compatible Server 12. Recommended Generation Parameters (Qwen Official) 13. CRITICAL: Coding Syntax & Repeat Penalty Advisory (Preventing Character Swapping) 14. Hardened Agentic Chat Template & Reasoning Effort 15. Optional Support > [!IMPORTANT] > ### THE DEFINITIVE SPECIFICATION IN THE approx. 13 GB CEILING > This **APEX-I-NanoPlus** release marks the official debut of our specialized tensor-by-tensor architectural configuration for sparse Mixture-of-Experts quantization within an extreme **approx. 13 GB envelope**. Its 40 main layers, 256 micro-experts, and integrated `blk.40` MTP block are allocated tensor by tensor to maximize reasoning precision, preserve routing behavior, and prevent avoidable CPU dequantization stalls during hybrid and system-RAM inference.
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> > 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-MiniPlus-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**.
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> > 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**.
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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 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.
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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. 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**.
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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. > > *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
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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. > > *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
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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. 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
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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. 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).
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