--- license: apache-2.0 base_model: - IFM/K2-Horizon-MoVA-36B-A4B base_model_relation: quantized tags: - gguf - rocm - vulkan - rocmfp4 - strix-halo - gfx1151 - amd - moe - reasoning - k2-horizon pipeline_tag: text-generation --- # K2-Horizon-MoVA-36B-A4B — ROCmFP4 for AMD Strix Halo (gfx1151) Four ROCmFP4/ROCmFPX tiers of [`IFM/K2-Horizon-MoVA-36B-A4B`](https://huggingface.co/IFM/K2-Horizon-MoVA-36B-A4B), quantised from the official BF16 GGUF and measured on a Ryzen AI Max+ 395 (Radeon 8060S, gfx1151, 128 GB unified, ROCm 7.2.4). This is the **dual-expert** `k2_horizon` MoVA architecture — 36B total parameters, **~4B active** — with two independent expert systems: a 64-way MoVA value-expert bank (top-4) alongside a 100-way MoE feed-forward (top-8), 48 layers, hidden 2560, and it is a **reasoning** model. **Every number on this card was measured on these exact files.** Nothing is estimated. As an A4B MoE, it decodes far faster than a dense 32B on the same hardware. ## Which file should I use? | file | ftype | size | decode (HIP) | prefill (HIP) | use it for | |---|---|---|---|---|---| | `K2-Horizon-MoVA-36B-A4B-Q4_0_ROCMFP4_FAST.gguf` | 103 | 18.57 GiB | **41.0 t/s** | **1238 t/s** | **default — fastest on both axes** | | `K2-Horizon-MoVA-36B-A4B-Q4_0_ROCMFP4_STRIX_LEAN.gguf` | 106 | 18.67 GiB | 41.0 t/s | 1227 t/s | same speed, Q5_K embeddings | | `K2-Horizon-MoVA-36B-A4B-Q4_0_ROCMFP4_COHERENT.gguf` | 102 | 19.81 GiB | 39.5 t/s | 1114 t/s | most bits in the 4-bit family | | `K2-Horizon-MoVA-36B-A4B-Q8_0_ROCMFPX_AGENT.gguf` | 115 | 36.45 GiB | 28.9 t/s | 1183 t/s | 8-bit, agent/tool-call routing | **Start with `FAST`** — fastest on both axes here, all four answer correctly. Take `AGENT` only if you specifically want 8-bit weights. ## Quick start — the backend is one flag ```bash # HIP / ROCm — best prefill (long prompts, RAG, agentic) llama-server -m K2-Horizon-MoVA-36B-A4B-Q4_0_ROCMFP4_FAST.gguf \ -dev ROCm0 -fa on -ngl 999 --no-mmap -fit off -np 1 \ -b 2048 -ub 1024 -t 16 --poll 100 -c 8192 --jinja # Vulkan — SAME FILE. Only -dev changes. llama-server -m K2-Horizon-MoVA-36B-A4B-Q4_0_ROCMFP4_FAST.gguf -dev Vulkan0 ... ``` ### ⛔ You need a build with BOTH `k2_horizon` and the ROCmFP4 types These declare `general.architecture = k2-horizon` **and** use the ROCmFP4/ROCmFPX tensor types (ggml 100-119). No single public build has both yet: - **`k2_horizon` arch** (incl. the MoVA dual-expert graph) — [MBZUAI-IFM llama.cpp fork](https://github.com/MBZUAI-IFM/llama.cpp/tree/model/K2Horizon); upstream PR in progress. - **ROCmFP4/ROCmFPX types** — [`ROCmFPX`](https://github.com/ROCmFPX/ROCmFPX). This repo ships **`k2-horizon-on-rocmfpx.patch`** — the arch applied onto a ROCmFPX base, the exact tree these files were built and verified against: ```bash git clone https://github.com/ROCmFPX/ROCmFPX.git && cd ROCmFPX curl -fLO https://huggingface.co/kingjones777/K2-Horizon-MoVA-36B-A4B-ROCmFP4-GGUF/resolve/main/k2-horizon-on-rocmfpx.patch git apply k2-horizon-on-rocmfpx.patch cmake -B build -DGGML_HIP=ON -DGGML_VULKAN=ON -DAMDGPU_TARGETS=gfx1151 -DGGML_NATIVE=ON -DCMAKE_BUILD_TYPE=Release cmake --build build -j$(nproc) ``` ## ⛔ Serving note — this is a reasoner MoVA-36B emits chain-of-thought into `reasoning_content` before answering. A short budget returns **HTTP 200 with empty `content` and the tokens in `reasoning`** — the model thinking, not a broken file. Give it room: ~500 tokens for short answers and tool calls, more for long-form. Verified: at 40 tokens `content` was empty; at 500 it answered correctly. ## Head handling Quantised at each tier's native routing with no forced `--output-tensor-type` override; the tiers are posted as-built. ## Reproduction block ``` model : IFM/K2-Horizon-MoVA-36B-A4B (official BF16 GGUF, K2-Horizon-36B-BF16.gguf, 74.9 GB) arch k2-horizon MoVA (dual-expert MoE): 36B total / ~4B active, mova_num_experts 64 top-4 + num_experts 100 top-8, 48 layers, hidden 2560 port : MBZUAI-IFM/llama.cpp @ model/K2Horizon (35999d1) k2_horizon arch applied onto ROCmFPX fork @ 85d8f7e (patch shipped in this repo) quantize : llama-quantize (no head-type override) Q4_0_ROCMFP4_COHERENT (102) · Q4_0_ROCMFP4_FAST (103) Q4_0_ROCMFP4_STRIX_LEAN (106) · Q8_0_ROCMFPX_AGENT (115) serve : llama-server -dev {ROCm0|Vulkan0} -fa on -ngl 999 --no-mmap -fit off -np 1 -b 2048 -ub 1024 -t 16 --poll 100 -c 8192 --jinja box : Ryzen AI Max+ 395 / Radeon 8060S (gfx1151) / 128 GB unified, ROCm 7.2.4 verified : dual-expert graph loads on GPU, generates correct output ("capital of France" -> "Paris"; "capital of Japan" -> "Tokyo") measured : 2026-09-04 ``` ## SHA256 ``` 0f11adaada7b8b9f7aa785561f412d2de85a555fc1e2ffeddf71f972b8d576e4 K2-Horizon-MoVA-36B-A4B-Q4_0_ROCMFP4_COHERENT.gguf 5618a8e20c3300dcb38ec7217c612edcd8fb091b23aa9e024ca7063b235e80bf K2-Horizon-MoVA-36B-A4B-Q4_0_ROCMFP4_FAST.gguf 7fea90c3aa739652d764416741e85f563ca355ab29c8a8eb89e753a37df8e79f K2-Horizon-MoVA-36B-A4B-Q4_0_ROCMFP4_STRIX_LEAN.gguf e653f3855ed2c0c347e14e22593d49ced9118bf2115a7717ed2e34e5e6ff722c K2-Horizon-MoVA-36B-A4B-Q8_0_ROCMFPX_AGENT.gguf ``` ## Credits **[IFM / MBZUAI](https://huggingface.co/IFM)** — the K2-Horizon-MoVA-36B-A4B base model (Apache-2.0) and the `k2_horizon` llama.cpp architecture. ROCmFP4/ROCmFPX quantisation types are the work of **[ROCmFPX](https://github.com/ROCmFPX/ROCmFPX)**. This repository adds only the ROCmFP4 quant ladder, the arch-on-ROCmFPX patch, and the measurements.