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Crystal
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View details ↗K2-Horizon-0.9B is the compact dense member of the K2-Horizon family: a 0.9B-class decoder-only model with a 128K context window. The chart at the top of this card shows K2-Horizon-0.9B against selected reference models. The table below lists every comparison model used in the figure. Reference models K2-Horizon-0.9B Qwen3.5-0.8B OpenBMB-1B Qwen3.5-2B # Params 0.9B 0.8B 1B 2B # Activated params 0.9B 0.8B 1B 2B Architecture Dense Dense Dense Dense Math AIME 2025 Competition mathematics 41.7 1.0 40.4 34.2 AIME 2026 Competition mathematics 48.5 0.2 40.4 38.8 HMMT Feb 2026 Competition mathematics 25.8 0.6 23.3 22.7 Scientific Reasoning GPQA Diamond Graduate-level science QA 27.3 11.9 26.3 54.9 Coding HumanEval+ Code generation 79.9 16.5 65.2 75.6 MBPP+ Code generation 68.0 35.4 60.6 67.7 LiveCodeBench v6 Competitive coding 37.4 6.6 33.5 29.8 Agents BFCL v4 Function calling 28.0 25.3 25.2 43.6
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View details ↗> [!NOTE] > This repository contains GGUF versions of the IFM/K2-Horizon-0.9B for use with `llama.cpp`. > > Multiple precision and quantization variants are available, including `BF16`, `Q8_0`, `Q6_K`, `Q5_K_M`, `Q5_0`, and `Q4_K_M`. All GGUF files include tokenizer metadata and a llama.cpp-compatible chat template. > [!WARNING] > **💡 Important Deployment Notes:** These models require a version of `llama.cpp` containing K2 Horizon architecture support. Mainline support merged in release b11454. K2-Horizon-0.9B is the compact dense member of the K2-Horizon family: a 0.9B-class decoder-only model with a 128K context window.
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View details ↗K2-Horizon-3.7B is the small dense member of the K2-Horizon family: a 3.7B-core decoder-only model with a 512K context window. - **Strong small-model baseline.** A dense model evaluated on the same agentic, coding, and reasoning benchmarks as the rest of the family. - **512K context.** Native 524,288-token context from the midtraining stages onward. - **Intermediate checkpoints.** Intermediate checkpoints are released so capability changes can be studied across training rather than at a single checkpoint. - **Fully open.** Training data and recipe, training code, and evaluation resources are public. The chart at the top of this card shows K2-Horizon-3.7B against selected reference models. The table below lists every comparison model used in the figure.
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View details ↗> [!NOTE] > This repository contains GGUF versions of the IFM/K2-Horizon-3.7B for use with `llama.cpp`. > > Multiple precision and quantization variants are available, including `BF16`, `Q8_0`, `Q6_K`, `Q5_K_M`, `Q5_0`, and `Q4_K_M`. All GGUF files include tokenizer metadata and a llama.cpp-compatible chat template. > [!WARNING] > **💡 Important Deployment Notes:** These models require a version of `llama.cpp` containing K2 Horizon architecture support. Mainline support merged in release b11454. K2-Horizon-3.7B is the small dense member of the K2-Horizon family: a 3.7B-core decoder-only model with a 512K context window.
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View details ↗K2-Horizon-32B is the large dense member of the K2-Horizon family: a 32B decoder-only model with a 512K context window. Open-weight dense models K2-Horizon-32B Qwen3.8-27B Muse Glimmer-30B IBM Granite 4.2 30B # Params 32B 27B 30B 30B # Activated params 32B 27B 30B 30B Architecture Dense Dense Dense Dense Agents tau3-Banking Agentic tool use 24.1 48.0 23.5 14.4 Coding Terminal-Bench 2.1 Agentic terminal use 62.6 79.8 51.7 26.6 SciCode Scientific coding 37.9 44.7 43.6 36.6 Scientific Reasoning Humanity's Last Exam (without tools) Expert-level reasoning 25.2 33.9 22.0 11.2 GPQA Diamond Graduate-level science QA 82.8 90.5 83.5 64.4 CritPt Frontier physics reasoning 2.9 5.4 2.6 0.3 General AA-LCR Long-context reasoning 68.0 77.3 80.0 46.7 AA-Omniscience Accuracy Factual accuracy 18.2 15.6 27.0 10.1 AA-Omniscience Non-Hallucination Non-hallucination rate 69.7 69.7 18.1 74.4 Scores in %. Bold marks the best score in each row. Sections follow the Artificial Analysis Intelligence Index categories. Baseline scores are from Artificial Analysis; Muse Glimmer-30B at high reasoning effort, other open models in their reasoning mode.
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