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kingjones777/K2-Horizon-7B-ROCmFP4-GGUF
K2-Horizon-7B-ROCmFP4-GGUF is a text generation model from kingjones777. Use it when you need the model to write or continue text. It is set up for gguf. The card lists the license as apache-2.0.
Quantized GGUFs of IFM/K2-Horizon-7B for AMD ROCm, converted and measured on Strix Halo (gfx1151, ROCm 7.2.4).
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From the Hugging Face model README
Quantized GGUFs of IFM/K2-Horizon-7B for AMD ROCm,
converted and measured on Strix Halo (gfx1151, ROCm 7.2.4).
K2-Horizon is not supported by upstream llama.cpp (tracked as ggml-org/llama.cpp#28361).
These files were produced with our own k2-horizon architecture implementation; the
conversion, the compute graph and the BPE pre-tokenizer are ours.
| file | size | PPL |
|---|---|---|
K2-Horizon-7B-Q4_0_ROCMFP4_STRIX_LEAN.gguf | 5.25 GB | 14.286 |
K2-Horizon-7B-Q4_0_ROCMFP4_COHERENT.gguf | 5.60 GB | 13.839 |
K2-Horizon-7B-Q8_0_ROCMFPX_AGENT.gguf | 9.47 GB | 13.004 |
K2-Horizon-7B-Q8_0_ROCMFPX.gguf | 9.35 GB | 13.439 |
Perplexity: 400 KB of English prose, -c 512 --chunks 20, full GPU offload, same binary
and session for every row. BF16 reference for this model: 12.899.
STRIX_LEAN is built with --output-tensor-type q6_K (protected head), not the raw tier.
⭐ Q8_0_ROCMFPX_AGENT scores better than plain Q8_0_ROCMFPX at the same nominal
8.25 bpw, on this model and on its sibling. There is no MTP head in play here.
Dense decoder, 36 layers, GQA 32 heads / 8 kv, head_dim 128, SwiGLU, RoPE theta 1e7, vocab 250624, declared context 524288. Hidden 4096, FFN 12288.
The distinguishing feature is grouped RMS normalisation: the norm is computed within
attention.group_norm_groups = 4 contiguous groups along the hidden dimension rather
than across the whole vector, at all three norm sites including the final norm. The norm
weights are full width either way, so the grouping is not visible in the checkpoint.
IFM/K2-Horizon-7B-GGUF stores q/k in unpermuted
HF layout, which its own llama.cpp fork rotates accordingly. These files carry the permute
that llama.cpp's LLAMA_ROPE_TYPE_NORM expects.
Measured on the same corpus, binary and session, BF16 in both cases:
| file | PPL |
|---|---|
| this conversion (BF16) | 12.899 |
IFM/K2-Horizon-7B-GGUF BF16 | 125.65 |
Use each with the build it was made for.
The declared 524288 context allocates 72 GiB of KV (77,309,411,328 bytes) and fails on
128 GiB hardware. Pass -c explicitly; 131072 costs 19.3 GB and is what we serve.
The model repo's chat_template.jinja uses {%- if spec is sameas true -%}. llama.cpp's
jinja engine (minja) does not implement the sameas test, so a GGUF carrying that template
fails at common_chat_templates_init with Parser Error: Expected %} (Got true).
These files embed the template from the vendor's own GGUF, which minja parses. Tool calling
works with --jinja (verified 3/3). Reasoning level is a template input:
reasoning_effort high → <ifm|think>, medium → <ifm|think_fast>, low → <ifm|think_faster>.
tokenizer.ggml.pre = k2-horizon. The splitter differs from llama3's in that the contraction
group is case-insensitive and the letter run also admits \p{M}, U+200C (ZWNJ) and U+200D (ZWJ).
Against HF tokenizers on a 50-case battery it matches 48/50. The two differences are
combining-mark inputs where HF applies its declared NFC normalizer and llama.cpp's BPE path
does not; the vendor's own GGUF produces the identical two differences.
git clone -b k2-horizon https://github.com/kingjones30/ROCmFPX.git && cd ROCmFPX
export ROCM_PATH=/opt/rocm HIP_PATH=/opt/rocm
cmake -S . -B build-rocm -DCMAKE_BUILD_TYPE=Release \
-DGGML_HIP=ON -DGPU_TARGETS=gfx1151 -DAMDGPU_TARGETS=gfx1151 \
-DCMAKE_HIP_COMPILER=/opt/rocm/lib/llvm/bin/clang++ \
-DCMAKE_HIP_COMPILER_ROCM_ROOT=/opt/rocm \
-DGGML_HIP_GRAPHS=ON -DGGML_HIP_NO_VMM=ON -DLLAMA_CURL=OFF -DLLAMA_BUILD_WEBUI=OFF
cmake --build build-rocm -j12 --target llama-server llama-quantize llama-perplexity
hf download IFM/K2-Horizon-7B --local-dir ./src --exclude "*.png"
python3 convert_hf_to_gguf.py ./src --outtype bf16 --outfile ./K2-Horizon-7B-BF16.gguf
PYTHONPATH=gguf-py python3 gguf-py/gguf/scripts/gguf_new_metadata.py \
./K2-Horizon-7B-BF16.gguf ./tmp.gguf --chat-template-file models/templates/k2-horizon.jinja
mv ./tmp.gguf ./K2-Horizon-7B-BF16.gguf
build-rocm/bin/llama-quantize --output-tensor-type q6_K \
./K2-Horizon-7B-BF16.gguf ./K2-Horizon-7B-Q4_0_ROCMFP4_STRIX_LEAN.gguf Q4_0_ROCMFP4_STRIX_LEAN
build-rocm/bin/llama-server -m ./K2-Horizon-7B-Q4_0_ROCMFP4_STRIX_LEAN.gguf \
-ngl 999 -fa on -fit off -dev ROCm0 --ctx-size 131072 --jinja
⛔ Plain -DGGML_HIP=ON fails with "Failed to find ROCm root directory" — pass
CMAKE_HIP_COMPILER explicitly, as above.
Apache-2.0, inherited from IFM/K2-Horizon-7B.