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NANI-Nithin/K2-Horizon-7B-GGUF

🤗 Hugging Face sourcetext-generationapache-2.0155 GBGGUF✓ HF checksums availableupdated today
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K2-Horizon-7B — GGUF Quantizations

GGUF quantizations of IFM/K2-Horizon-7B, a dense 7B-parameter causal decoder (K2HorizonForCausalLM, model_type: k2_horizon).

Quantized by NANI-Nithin using a custom pipeline built on the MBZUAI-IFM llama.cpp fork (branch model/K2Horizon).

Model Details

Property Value
Base model IFM/K2-Horizon-7B
Architecture K2HorizonForCausalLM (k2_horizon)
Parameters ~7B (dense decoder, no MoE)
Original dtype BF16
BF16 GGUF size ~18.01 GB
Source GGUF IFM/K2-Horizon-7B-GGUF
llama.cpp fork MBZUAI-IFM/llama.cpp @ model/K2Horizon

Note: These GGUFs carry the k2-horizon architecture token and require the MBZUAI-IFM fork (or upstream llama.cpp once support is merged) to run. Vanilla upstream llama.cpp (as of September 2026) does not support K2HorizonForCausalLM.

Included Files

Standard Quantizations

File Bits/Weight Notes
K2-Horizon-7B-BF16.gguf 16 bpw Source quant, full precision
K2-Horizon-7B-Q8_0.gguf 8 bpw Near-lossless, recommended reference
K2-Horizon-7B-Q6_K.gguf 6 bpw Near-lossless K-quant
K2-Horizon-7B-Q5_K_M.gguf 5 bpw Best quality/size in the 5-bit range
K2-Horizon-7B-Q5_K_S.gguf 5 bpw Smaller 5-bit variant
K2-Horizon-7B-Q5_1.gguf 5 bpw Legacy 5-bit
K2-Horizon-7B-Q5_0.gguf 5 bpw Legacy 5-bit
K2-Horizon-7B-Q4_K_M.gguf 4 bpw Recommended general use
K2-Horizon-7B-Q4_K_S.gguf 4 bpw Smaller 4-bit K-quant
K2-Horizon-7B-Q4_1.gguf 4 bpw Legacy 4-bit
K2-Horizon-7B-Q4_0.gguf 4 bpw Legacy 4-bit
K2-Horizon-7B-Q3_K_L.gguf 3 bpw Large 3-bit K-quant
K2-Horizon-7B-Q3_K_M.gguf 3 bpw Medium 3-bit K-quant
K2-Horizon-7B-Q3_K_S.gguf 3 bpw Small 3-bit K-quant
K2-Horizon-7B-Q2_K.gguf 2 bpw Aggressive compression
K2-Horizon-7B-Q2_K_S.gguf 2 bpw Smaller 2-bit K-quant (imatrix-guided)
K2-Horizon-7B-Q2_0.gguf 2.25 bpw Group-64 2-bit
K2-Horizon-7B-Q1_0.gguf 1.125 bpw Maximum compression

IQ (Importance-Matrix) Quantizations

File Bits/Weight
K2-Horizon-7B-IQ4_NL.gguf ~4 bpw
K2-Horizon-7B-IQ4_XS.gguf ~4 bpw
K2-Horizon-7B-IQ3_M.gguf ~3 bpw
K2-Horizon-7B-IQ3_S.gguf ~3 bpw
K2-Horizon-7B-IQ3_XS.gguf ~3 bpw
K2-Horizon-7B-IQ3_XXS.gguf ~3 bpw
K2-Horizon-7B-IQ2_M.gguf ~2 bpw
K2-Horizon-7B-IQ2_S.gguf ~2 bpw
K2-Horizon-7B-IQ2_XS.gguf ~2 bpw
K2-Horizon-7B-IQ2_XXS.gguf ~2 bpw
K2-Horizon-7B-IQ1_M.gguf 1.75 bpw
K2-Horizon-7B-IQ1_S.gguf 1.56 bpw

Quantization Method

  • Source: IFM's official BF16 GGUF (K2-Horizon-7B-BF16.gguf).
  • imatrix: Computed from Salesforce/wikitext (wikitext-2-raw-v1, 500 rows) with 12 GPU layers offloaded on an RTX 4060 Laptop (8 GB VRAM) due to the 18 GB model size. Applied to all K-quants below Q6 and all IQ quants.
  • Fork: MBZUAI-IFM/llama.cpp, branch model/K2Horizon.

Usage

Requires the MBZUAI-IFM llama.cpp fork (model/K2Horizon branch).

git clone -b model/K2Horizon https://github.com/MBZUAI-IFM/llama.cpp
cd llama.cpp && cmake -B build -DGGML_CUDA=ON && cmake --build build --config Release

./build/bin/llama-cli \
  -m K2-Horizon-7B-Q4_K_M.gguf \
  -p "Hello, I am" \
  -n 128 \
  -ngl 35

License

Weights are released under the same license as the original IFM/K2-Horizon-7B model. Please refer to the original repository for full license terms.

Credits