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詳細を見る ↗DISCOVER & COMPARE
AIモデルを探す 84999
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詳細を見る ↗Iris-mini-APEX-I-MiniPlus-V1-GGUF
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詳細を見る ↗Iris-mini-APEX-I-MiniPlus-V2.1-GGUF
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詳細を見る ↗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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詳細を見る ↗LFM2.5-24B-A2B-APEX-I-NanoPlus-GGUF
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詳細を見る ↗1. Independent Community Benchmarks (Shortbench / NanoBench v0.5.0) 2. Key Architectural Features 3. CLI Usage Example 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. > [!NOTE] > **Verified Autonomous Agent Evaluation:** Evaluated on the Shortbench / NanoBench v0.5.0 autonomous coding and agentic benchmark (12 objective software engineering, pipeline, and system integrity tasks).
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詳細を見る ↗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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詳細を見る ↗> [!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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詳細を見る ↗Occamy-1.0-APEX-I-MiniPlus-V2.1-GGUF
IsValorumIf 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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詳細を見る ↗Occamy-1.0-APEX-I-MiniPlus-V2-GGUF
IsValorum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。
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詳細を見る ↗Occamy-1.0-APEX-I-NanoPlus-GGUF
IsValorum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。
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詳細を見る ↗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。
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詳細を見る ↗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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詳細を見る ↗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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詳細を見る ↗**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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詳細を見る ↗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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詳細を見る ↗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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詳細を見る ↗> > 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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同一チャネルの基本テキスト料金を比較
収集済みの比較可能な100万トークン当たり米ドル料金です。キャッシュ割引、段階料金、追加推論出力、税金は含みません。最新料金は出典で確認してください。
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必ずしもそうではありません。バージョンごとのライセンス、商用制限、ポリシーを確認してください。自前運用には計算資源と保守費用も必要です。
ランキング1位が最適ですか？+
順位はテスト、バージョン、評価方法によります。実際の業務データで精度、遅延、安定性、費用を比較してください。