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Jan-nano-GGUF

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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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JanusCoder-14B-GGUF

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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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JanusCoder-8B-GGUF

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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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KernelLLM-GGUF

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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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Kimi-Dev-72B-GGUF

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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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Kimi-K2.5-GGUF

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Kimi K2.5 is an open-source, native multimodal agentic model built through continual pretraining on approximately 15 trillion mixed visual and text tokens atop Kimi-K2-Base. It seamlessly integrates vision and language understanding with advanced agentic capabilities, instant and thinking modes, as well as conversational and agentic paradigms. Benchmark Kimi K2.5 (Thinking) GPT-5.2 (xhigh) Claude 4.5 Opus (Extended Thinking) Gemini 3 Pro (High Thinking Level) DeepSeek V3.2 (Thinking) Qwen3-VL- 235B-A22B- Thinking 1. General Testing Details - We report results for Kimi K2.5 and DeepSeek-V3.2 with thinking mode enabled, Claude Opus 4.5 with extended thinking mode, GPT-5.2 with xhigh reasoning effort, and Gemini 3 Pro with a high thinking level. For vision benchmarks, we additionally report results for Qwen3-VL-235B-A22B-Thinking. - Unless otherwise specified, all Kimi K2.5 experiments were conducted with temperature = 1.0, top-p = 0.95, and a context length of 256k tokens. - Benchmarks without publicly available scores were re-evaluated under the same conditions used for Kimi K2.5 and are marked with an asterisk (*). - We could not evaluate GPT-5.2 xhigh on all benchmarks due to service stability issues. For benchmarks that were not tested, we mark them as "-". 2. Text and Reasoning - HLE, AIME 2025, HMMT 2025 (Feb), and GPQA-Diamond were evaluated with a maximum completion budget of 96k tokens. - Results for AIME and HMMT are averaged over 32 runs (avg@32); GPQA-Diamond over 8 runs (avg@8). - For HLE, we report scores on the full set (text & image). Kimi K2.5 scores 31.5 (text) and 21.3 (image) without tools, and 51.8 (text) and 39.8 (image) with tools. The DeepSeek-V3.2 score corresponds to its text-only subset (marked with †) . Hugging Face access was blocked to prevent potential data leakage. HLE with tools uses simple context management: once the context exceeds a threshold, only the latest round of tool messages is retained. 3. Tool-Augmented / Agentic Search - Kimi K2.5 was equipped with search, code-interpreter, and web-browsing tools for HLE with tools and all agentic search benchmarks. - Except for BrowseComp (where K2.5 and DeepSeek-V3.2 used the discar

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Kimi-K2.6-GGUF

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To run Kimi K2.6 in full precision lossless, run Q8 (UD-Q8_K_XL), which is 595GB and only 10GB bigger than Q4 (UD-Q4_K_XL). See our Kimi K2.6 guide for quantization analysis and instructions. Kimi K2.6 is an open-source, native multimodal agentic model that advances practical capabilities in long-horizon coding, coding-driven design, proactive autonomous execution, and swarm-based task orchestration. Benchmark Kimi K2.6 GPT-5.4 (xhigh) Claude Opus 4.6 (max effort) Gemini 3.1 Pro (thinking high) Kimi K2.5

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Kimi-K2.7-Code

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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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Kimi-K2.7-Code-GGUF

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To run Kimi K2.7 Code in full precision lossless, run Q8 (UD-Q8_K_XL), which is 595GB and only 10GB bigger than Q4 (UD-Q4_K_XL). See our Kimi K2.7 Code guide for quantization analysis and instructions. Kimi K2.7 Code is a coding-focused agentic model built upon Kimi K2.6. With substantial improvements on real-world long-horizon coding tasks, it strengthens end-to-end task completion across complex software engineering workflows while improving token efficiency, reducing thinking-token usage by approximately 30% compared with Kimi K2.6. 1. **General Testing Details** - Unless stated otherwise, Kimi K2.7 Code and K2.6 were tested with thinking mode enabled via Kimi Code CLI at temperature = 1.0, top-p = 0.95, and a 262,144-token context length; GPT-5.5 ran in Codex with xhigh mode, and Opus 4.8 in Claude Code with xhigh mode. Aside from these differences, all benchmarks were evaluated under the same conditions. 2. **Coding Benchmarks** - Kimi Code Bench V2 is our in-house benchmark designed to evaluate coding agents on realistic tasks. It has diversed software engineering tasks across 10+ mainstream programming languages and a full production tech stack covering tasks from internal engineering use cases, production incidents, and real-world open-source projects, with emphasis on backend services, infrastructure, performance engineering, systems programming, security, frontend development, and ML/data engineering. - Program Bench evaluates code-generation agents by asking them to recreate a program’s behavior from only a compiled binary and its documentation. It spans 200 tasks, from small CLI tools to large systems like FFmpeg and SQLite. Submissions are judged against over 248,000 fuzz-generated behavioral tests. In each task, the agent is given an executable and its documentation, but no source code, decompilation, or internet access. It must choose its own implementation language, build the full program from scratch, and pass a behavioral test suite comparing its output against the original binary. - MLS-Bench evaluates whether AI systems can invent generalizable and scalable ML methods. MLS-Bench-Lite is the official 30-task subset of MLS-Bench, covering LLM pretraining and post-training, robotics, world models, computer vision, reinforcement learning, optimization, ML systems, AI for Science, and more. Agents are given 5 hours to explore before submitting

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Kimi-K2-Instruct-0905-GGUF

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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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Kimi-K2-Instruct-GGUF

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It is recommended to have at least 128GB unified RAM memory to run the small quants. With 16GB VRAM and 256 RAM, expect 5+ tokens/sec. For best results, use any 2-bit XL quant or above. 📰   Tech Blog     |     📄  Paper Link (coming soon) Kimi K2 is a state-of-the-art mixture-of-experts (MoE) language model with 32 billion activated parameters and 1 trillion total parameters. Trained with the Muon optimizer, Kimi K2 achieves exceptional performance across frontier knowledge, reasoning, and coding tasks while being meticulously optimized for agentic capabilities.

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Kimi-K2-Thinking-GGUF

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Kimi K2 Thinking is the latest, most capable version of open-source thinking model. Starting with Kimi K2, we built it as a thinking agent that reasons step-by-step while dynamically invoking tools. It sets a new state-of-the-art on Humanity's Last Exam (HLE), BrowseComp, and other benchmarks by dramatically scaling multi-step reasoning depth and maintaining stable tool-use across 200–300 sequential calls. At the same time, K2 Thinking is a native INT4 quantization model with 256k context window, achieving lossless reductions in inference latency and GPU memory usage. **Reasoning Tasks** | Benchmark | Setting | K2 Thinking | GPT-5 (High) | Claude Sonnet 4.5 (Thinking) | K2 0905 | DeepSeek-V3.2 | Grok-4 | |:----------:|:--------:|:------------:|:------:|:----------------------------:|:--------:|:--------------:|:-------:| | **HLE (Text-only)** | no tools | 23.9 | 26.3 | 19.8* | 7.9 | 19.8 | 25.4 | | | w/ tools | 44.9 | 41.7* | 32.0* | 21.7 | 20.3* | 41.0 | | | heavy | 51.0 | 42.0 | - | - | - | 50.7 | | **AIME25** | no tools | 94.5 | 94.6 | 87.0 | 51.0 | 89.3 | 91.7 | | | w/ python | 99.1 | 99.6 | 100.0 | 75.2 | 58.1* | 98.8 | | | heavy | 100.0 | 100.0 | - | - | - | 100.0 | | **HMMT25** | no tools | 89.4 | 93.3 | 74.6* | 38.8 | 83.6 | 90.0 | | | w/ python | 95.1 | 96.7 | 88.8* | 70.4 | 49.5* | 93.9 | | | heavy | 97.5 | 100.0 | - | - | - | 96.7 | | **IMO-AnswerBench** | no tools | 78.6 | 76.0* | 65.9* | 45.8 | 76.0* | 73.1 | | **GPQA** | no tools | 84.5 | 85.7 | 83.4 | 74.2 | 79.9 | 87.5 | **General Tasks** | Benchmark | Setting | K2 Thinking | GPT-5 (High) | Claude Sonnet 4.5 (Thinking) | K2 0905 | DeepSeek-V3.2 | |:----------:|:--------:|:------------:|:------:|:----------------------------:|:--------:|:--------------:| | **MMLU-Pro** | no tools | 84.6 | 87.1 | 87.5 | 81.9 | 85.0 | | **MMLU-Redux** | no tools | 94.4 | 95.3 | 95.6 | 92.7 | 93.7 | | **Longform Writing** | no tools | 73.8 | 71.4 | 79.8 | 62.8 | 72.5 | | **HealthBench** | no tools | 58.0 | 67.2 | 44.2 | 43.8 | 46.9 |

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