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abenzerps/K2-Horizon-7B-GGUF overview

IMPORTANT Compatibility: These GGUF files require a llama.cpp build with K2 Horizon architecture support. Until upstream support lands, use the MBZUAI IFM fork…

ggufllama.cppk2-horizonlong-context512k-contextdensetext-generationconversationalenbase_model:IFM/K2-Horizon-7Bbase_model:quantized:IFM/K2-Horizon-7Blicense:apache-2.0endpoints_compatibleregion:us

Runs locally from ~4.97 GB disk (8 GB VRAM class GPUs with llama.cpp / guIDE).

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Pipeline
text-generation
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Repository Files & Downloads

6 GGUF files detected
Direct downloads for local inference
FileTypeQuantizationSizeLink
K2-Horizon-7B-Q4_0.ggufGGUFQ4_04.97 GBDownload
K2-Horizon-7B-Q4_K_M-Selective.ggufGGUFQ4_K_M5.55 GBDownload
K2-Horizon-7B-Q4_K_M.ggufGGUFQ4_K_M5.21 GBDownload
K2-Horizon-7B-Q5_K_M.ggufGGUFQ5_K_M6.02 GBDownload
K2-Horizon-7B-Q6_K.ggufGGUFQ6_K6.89 GBDownload
K2-Horizon-7B-Q8_0.ggufGGUFQ8_08.92 GBDownload

Model Details

Model IDabenzerps/K2-Horizon-7B-GGUF
Authorabenzerps
Pipelinetext-generation
Licenseapache-2.0
Base modelIFM/K2-Horizon-7B
Last modified2026-09-03T22:47:47.000Z

Model README

---

base_model: IFM/K2-Horizon-7B

base_model_relation: quantized

license: apache-2.0

language:

- en

library_name: gguf

pipeline_tag: text-generation

tags:

- gguf

- llama.cpp

- k2-horizon

- long-context

- 512k-context

- dense

---

> [!IMPORTANT]

> Compatibility: These GGUF files require a llama.cpp build with K2 Horizon architecture support. Until upstream support lands, use the MBZUAI-IFM fork.

K2-Horizon-7B GGUF

GGUF quantizations of IFM/K2-Horizon-7B, a 7B dense decoder-only model for reasoning, coding, long-context work, and tool use. The source checkpoint supports a native context length of 524,288 tokens (512K).

Benchmarks

!K2-Horizon-7B benchmark results

Benchmark results reported by IFM for the original K2-Horizon-7B checkpoint.

GGUF files

| Quantization | File | Size |

| --- | --- | ---: |

| Q4_0 | K2-Horizon-7B-Q4_0.gguf | 5.34 GB |

| Q4_K_M | K2-Horizon-7B-Q4_K_M.gguf | 5.59 GB |

| Q4_K_M Selective | K2-Horizon-7B-Q4_K_M-Selective.gguf | 5.96 GB |

| Q5_K_M | K2-Horizon-7B-Q5_K_M.gguf | 6.47 GB |

| Q6_K | K2-Horizon-7B-Q6_K.gguf | 7.39 GB |

| Q8_0 | K2-Horizon-7B-Q8_0.gguf | 9.57 GB |

The files are text-only GGUFs; no vision projector is required. The selective variant uses a Q4_K_M baseline with attention Q/K/V/O projection tensors kept at Q6_K; it is a manual tensor-selective build and does not use an importance matrix. SHA-256 checksums are provided in SHA256SUMS.txt.

Chat template

Each GGUF embeds the llama.cpp-compatible chat template. chat_template.jinja is a matching external copy for tools that require one. The original source template is retained as chat_template.upstream.jinja for runtimes with full Jinja support.

xml is the default tool-call format. Use --chat-template-kwargs to select json or xml_typed when required.

Usage

Use the IFM K2 Horizon llama.cpp fork. The example below uses a practical 128K context; -c 524288 can be used when the available memory is sufficient.

llama-cli \
  -m K2-Horizon-7B-Q4_K_M.gguf \
  -c 131072 --jinja \
  --temp 1.0 --top-p 0.95

Source

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