Instructions to use plunderstruck/Nex-N2-mini-ROCmFP4-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use plunderstruck/Nex-N2-mini-ROCmFP4-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="plunderstruck/Nex-N2-mini-ROCmFP4-GGUF",
	filename="Nex-N2-mini-ROCmFP4-STRIX-embF16-imatrix-headQ6.gguf",
)

llm.create_chat_completion(
	messages = "No input example has been defined for this model task."
)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use plunderstruck/Nex-N2-mini-ROCmFP4-GGUF with llama.cpp:

Install (macOS, Linux)

curl -LsSf https://llama.app/install.sh | sh
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf plunderstruck/Nex-N2-mini-ROCmFP4-GGUF:BF16
# Run inference directly in the terminal:
llama cli -hf plunderstruck/Nex-N2-mini-ROCmFP4-GGUF:BF16

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf plunderstruck/Nex-N2-mini-ROCmFP4-GGUF:BF16
# Run inference directly in the terminal:
llama cli -hf plunderstruck/Nex-N2-mini-ROCmFP4-GGUF:BF16

Use pre-built binary

# Download pre-built binary from:
# https://github.com/ggerganov/llama.cpp/releases
# Start a local OpenAI-compatible server with a web UI:
./llama-server -hf plunderstruck/Nex-N2-mini-ROCmFP4-GGUF:BF16
# Run inference directly in the terminal:
./llama-cli -hf plunderstruck/Nex-N2-mini-ROCmFP4-GGUF:BF16

Build from source code

git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-server llama-cli
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama-server -hf plunderstruck/Nex-N2-mini-ROCmFP4-GGUF:BF16
# Run inference directly in the terminal:
./build/bin/llama-cli -hf plunderstruck/Nex-N2-mini-ROCmFP4-GGUF:BF16

Use Docker

docker model run hf.co/plunderstruck/Nex-N2-mini-ROCmFP4-GGUF:BF16

LM Studio
Jan
Ollama
How to use plunderstruck/Nex-N2-mini-ROCmFP4-GGUF with Ollama:
```
ollama run hf.co/plunderstruck/Nex-N2-mini-ROCmFP4-GGUF:BF16
```

Unsloth Studio

How to use plunderstruck/Nex-N2-mini-ROCmFP4-GGUF with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for plunderstruck/Nex-N2-mini-ROCmFP4-GGUF to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for plunderstruck/Nex-N2-mini-ROCmFP4-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for plunderstruck/Nex-N2-mini-ROCmFP4-GGUF to start chatting

How to use plunderstruck/Nex-N2-mini-ROCmFP4-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf plunderstruck/Nex-N2-mini-ROCmFP4-GGUF:BF16

Configure the model in Pi

# Install Pi:
npm install -g @mariozechner/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "plunderstruck/Nex-N2-mini-ROCmFP4-GGUF:BF16"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use plunderstruck/Nex-N2-mini-ROCmFP4-GGUF with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf plunderstruck/Nex-N2-mini-ROCmFP4-GGUF:BF16

Configure Hermes

# Install Hermes:
curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash
hermes setup
# Point Hermes at the local server:
hermes config set model.provider custom
hermes config set model.base_url http://127.0.0.1:8080/v1
hermes config set model.default plunderstruck/Nex-N2-mini-ROCmFP4-GGUF:BF16

Run Hermes

hermes

Atomic Chat new

OpenClaw new

How to use plunderstruck/Nex-N2-mini-ROCmFP4-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf plunderstruck/Nex-N2-mini-ROCmFP4-GGUF:BF16

Configure OpenClaw

# Install OpenClaw:
npm install -g openclaw@latest
# Register the local server and set it as the default model:
openclaw onboard --non-interactive --mode local \
  --auth-choice custom-api-key \
  --custom-base-url http://127.0.0.1:8080/v1 \
  --custom-model-id "plunderstruck/Nex-N2-mini-ROCmFP4-GGUF:BF16" \
  --custom-provider-id llama-cpp \
  --custom-compatibility openai \
  --custom-text-input \
  --accept-risk \
  --skip-health

Run OpenClaw

openclaw agent --local --agent main --message "Hello from Hugging Face"

Docker Model Runner
How to use plunderstruck/Nex-N2-mini-ROCmFP4-GGUF with Docker Model Runner:
```
docker model run hf.co/plunderstruck/Nex-N2-mini-ROCmFP4-GGUF:BF16
```

Lemonade

How to use plunderstruck/Nex-N2-mini-ROCmFP4-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull plunderstruck/Nex-N2-mini-ROCmFP4-GGUF:BF16

Run and chat with the model

lemonade run user.Nex-N2-mini-ROCmFP4-GGUF-BF16

List all available models

lemonade list

Nex-N2-mini-ROCmFP4-GGUF / README.md

plunderstruck

Repoint build instructions to charlie12345/ROCmFPX (ROCmFPX FP3/4/6/8 repo)

44d97db verified 18 days ago

preview code

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	---
	base_model: nex-agi/Nex-N2-mini
	license: apache-2.0
	library_name: gguf
	tags:
	- gguf
	- rocmfp4
	- qwen3.5
	- nex-n2
	- coder
	- agentic
	- moe
	- imatrix
	- strix-halo
	- amd
	- rocm
	- vulkan
	language:
	- en
	base_model_relation: quantized
	---

	<div style="border:2px solid currentColor; font-family:ui-monospace,'SF Mono','Cascadia Mono',Consolas,'Liberation Mono',monospace;">
	<div style="border-bottom:1px solid currentColor; padding:6px 12px; font-size:11px; letter-spacing:3px; text-transform:uppercase; opacity:0.7; text-align:center;">PLUNDERSTRUCK // ROCmFP4 QUANTIZED MODEL // STRIX HALO · gfx1151</div>
	<div style="padding:14px; display:flex; flex-wrap:wrap; align-items:center; justify-content:center; gap:18px;">
	<pre style="margin:0; flex:0 0 auto; font-family:ui-monospace,'SF Mono','Cascadia Mono',Consolas,monospace; font-size:5px; line-height:1.1; letter-spacing:0;">
	███████▆▄▁ ██████▎▐██████████████▖▀██████▙ ▟██████▘
	██████████▆▃▁ ██████▎▐███████████████▙▝▜██████▖ ▗██████▛
	█████████████▆ ██████▎▐████▀▀▀▀▀▀▀▀▀▀▀▀▀ ▀██████▙▟██████▘
	██████▏▀▜█████ ██████▎▐████▃▃▃▃▃▃▃▃▃▃▃▃▃▖ ▔▜██████▘ ▀█▛
	██████▏▗▂▔▀▜██ ██████▎▐█████████████████▍ ▝███▛▁▟▙
	██████▏▐█▇▅▂▔▀ ██████▎▐█████████████████▍ ▔▀▘▃████▖
	██████▏▐████▇▄▂██████▎▐████▀▀▀▀▀▀▀▀▀▀▀▀▀ ▗▟█▖▁▟██████▙▁
	██████▏▝█████████████▎▐████▅▅▅▅▅▅▅▅▅▅▅▅▅ ▄██████▛▜██████▖
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	██████▏ ▀▜███████▎▐██████████████▘▄██████▛ ▜██████▖
	</pre>
	<div style="flex:0 1 auto; max-width:100%; text-align:center;">
	<div style="font-size:23px; font-weight:800; letter-spacing:1px;">NEX-N2-MINI</div>
	<div style="font-size:12.5px; letter-spacing:1px; opacity:0.8; margin-top:5px;"><span style="white-space:nowrap;">4-BIT ROCmFP4</span> · <span style="white-space:nowrap;">CODE-WEIGHTED IMATRIX</span> · <span style="white-space:nowrap;">HIGH-SPARSITY MoE (3B ACTIVE)</span> · <span style="white-space:nowrap;">AGENTIC CODER</span> · <span style="white-space:nowrap;">SINGLE AMD APU</span></div>
	</div>
	</div>
	<table style="display:table; table-layout:fixed; width:100%; margin:0; border-collapse:collapse; border-radius:0; font-family:ui-monospace,'SF Mono',Consolas,monospace; font-size:12px;">
	<tr>
	<td style="border-top:1px solid currentColor; border-right:1px solid currentColor; padding:8px 12px;"><div style="font-size:10px; letter-spacing:1px; opacity:0.6;">FORMAT</div><div style="font-weight:700;">ROCmFP4 4-BIT</div></td>
	<td style="border-top:1px solid currentColor; border-right:1px solid currentColor; padding:8px 12px;"><div style="font-size:10px; letter-spacing:1px; opacity:0.6;">PRECISION</div><div style="font-weight:700;">~4.5 BPW</div></td>
	<td style="border-top:1px solid currentColor; border-right:1px solid currentColor; padding:8px 12px;"><div style="font-size:10px; letter-spacing:1px; opacity:0.6;">SIZE</div><div style="font-weight:700;">18.4 GB</div></td>
	<td style="border-top:1px solid currentColor; padding:8px 12px;"><div style="font-size:10px; letter-spacing:1px; opacity:0.6;">CONTEXT</div><div style="font-weight:700;">131 K</div></td>
	</tr>
	<tr>
	<td style="border-top:1px solid currentColor; border-right:1px solid currentColor; padding:8px 12px;"><div style="font-size:10px; letter-spacing:1px; opacity:0.6;">ARCH</div><div style="font-weight:700;">qwen35moe</div></td>
	<td style="border-top:1px solid currentColor; border-right:1px solid currentColor; padding:8px 12px;"><div style="font-size:10px; letter-spacing:1px; opacity:0.6;">PARAMS</div><div style="font-weight:700;">35B / 3B ACTIVE</div></td>
	<td style="border-top:1px solid currentColor; border-right:1px solid currentColor; padding:8px 12px;"><div style="font-size:10px; letter-spacing:1px; opacity:0.6;">BACKEND</div><div style="font-weight:700;">VULKAN0</div></td>
	<td style="border-top:1px solid currentColor; padding:8px 12px;"><div style="font-size:10px; letter-spacing:1px; opacity:0.6;">LICENSE</div><div style="font-weight:700;">APACHE-2.0</div></td>
	</tr>
	</table>
	</div>

	<div style="border:2px solid #dc2626; padding:10px 13px; font-family:ui-monospace,'SF Mono',Consolas,monospace; font-size:12.5px; margin:14px 0;">
	<b style="color:#dc2626; letter-spacing:1px;">⚠ REQUIRES THE ROCmFP4 FORK</b><br>
	The custom <code>q4_0_rocmfp4</code> / <code>q4_0_rocmfp4_fast</code> tensor types <b>will not load in stock llama.cpp, LM Studio, or Ollama</b>. Build/run with <a href="https://github.com/charlie12345/ROCmFPX">charlie12345/ROCmFPX</a> · branch <code>mtp-rocmfp4-strix</code>.
	</div>

	<div style="border:1px solid currentColor; padding:8px 13px; font-family:ui-monospace,'SF Mono',Consolas,monospace; font-size:12px; margin:14px 0; opacity:0.85;">
	<b>NOTE //</b> Ignore HuggingFace's auto-detected "F16" badge — its parser can't read ROCmFP4 and mislabels by the f16 embeddings. These are <b>~4.5 bpw 4-bit</b> files; pick by filename.
	</div>

	<div style="font-family:ui-monospace,'SF Mono',Consolas,monospace; font-weight:800; font-size:14px; letter-spacing:2px; text-transform:uppercase; border-bottom:2px solid currentColor; padding-bottom:5px; margin:26px 0 12px;"><span style="color:#ea580c;">01</span> · FILES</div>

	<div style="overflow:hidden; border-radius:0;">
	<table style="width:100%; border-collapse:collapse; border-radius:0; font-family:ui-monospace,'SF Mono',Consolas,monospace; font-size:12.5px;">
	<thead><tr>
	<th style="border:1px solid currentColor; padding:7px 10px; text-align:left; text-transform:uppercase; font-size:10px; letter-spacing:1px;">File</th>
	<th style="border:1px solid currentColor; padding:7px 10px; text-align:left; text-transform:uppercase; font-size:10px; letter-spacing:1px;">Size</th>
	<th style="border:1px solid currentColor; padding:7px 10px; text-align:left; text-transform:uppercase; font-size:10px; letter-spacing:1px;">Output head</th>
	<th style="border:1px solid currentColor; padding:7px 10px; text-align:left; text-transform:uppercase; font-size:10px; letter-spacing:1px;">Pick if</th>
	</tr></thead>
	<tbody>
	<tr><td style="border:1px solid currentColor; padding:7px 10px;"><code>…-STRIX-embF16-imatrix-headQ6.gguf</code> ★</td><td style="border:1px solid currentColor; padding:7px 10px;">18.4 GB</td><td style="border:1px solid currentColor; padding:7px 10px;">Q6_K</td><td style="border:1px solid currentColor; padding:7px 10px;"><b>the one build</b> — best speed/quality balance: f16 embeddings + Q6 output head on the fast single-scale body</td></tr>
	</tbody>
	</table>
	</div>

	One file — the best speed/quality balance in ROCmFP4 for Strix Halo. It keeps the two quality levers that are actually felt — genuine f16 token embeddings (from BF16) and a Q6_K output head — on the fast single-scale `q4_0_rocmfp4_fast` body + the code-weighted imatrix (see §04). Not the leanest-fastest possible (a 4-bit output head squeezes out a few more tok/s, at a fidelity cost), and not the most faithful possible (see the base-model fidelity link in §04) — it's the point where speed and quality meet best. The Qwen (ChatML) chat template is baked into the GGUF — just pass `--jinja`.

	<div style="font-family:ui-monospace,'SF Mono',Consolas,monospace; font-weight:800; font-size:14px; letter-spacing:2px; text-transform:uppercase; border-bottom:2px solid currentColor; padding-bottom:5px; margin:26px 0 12px;"><span style="color:#ea580c;">02</span> · QUICK START</div>

	Run from the folder holding the `.gguf`:

	```bash
	env HSA_OVERRIDE_GFX_VERSION=11.5.1 GGML_HIP_ENABLE_UNIFIED_MEMORY=1 \
	llama-server \
	-m Nex-N2-mini-ROCmFP4-STRIX-embF16-imatrix-headQ6.gguf \
	--alias nex-n2-mini \
	--host 0.0.0.0 \
	--port 8080 \
	-dev Vulkan0 \
	-ngl 999 \
	-fa on \
	-c 131072 \
	-b 2048 \
	-ub 256 \
	-t 16 \
	-tb 16 \
	-ctk f16 \
	-ctv f16 \
	-cpent 256 \
	-ctxcp 32 \
	--cache-reuse 256 \
	--cache-ram 65536 \
	--temp 0.6 \
	--top-p 0.95 \
	--top-k 20 \
	--min-p 0.0 \
	--jinja \
	--parallel 1 \
	--metrics \
	--no-mmap
	```

	<div style="border:1px solid currentColor; padding:8px 13px; font-family:ui-monospace,'SF Mono',Consolas,monospace; font-size:12px; margin:14px 0; opacity:0.85;">
	<b>NOTE //</b> No <code>--spec-*</code> / <code>--spec-type draft-mtp</code> flags — Nex-N2-mini ships <b>without an MTP head</b> (non-speculative). At ~72 t/s it doesn't need speculative decoding to be quick.
	</div>

	<div style="overflow:hidden; border-radius:0;">
	<table style="width:100%; border-collapse:collapse; border-radius:0; font-family:ui-monospace,'SF Mono',Consolas,monospace; font-size:12px;">
	<thead><tr>
	<th style="border:1px solid currentColor; padding:6px 10px; text-align:left; text-transform:uppercase; font-size:10px; letter-spacing:1px; width:40%;">Flag</th>
	<th style="border:1px solid currentColor; padding:6px 10px; text-align:left; text-transform:uppercase; font-size:10px; letter-spacing:1px;">Function</th>
	</tr></thead>
	<tbody>
	<tr><td style="border:1px solid currentColor; padding:6px 10px;"><code>HSA_OVERRIDE_GFX_VERSION=11.5.1</code></td><td style="border:1px solid currentColor; padding:6px 10px;">treat the APU as gfx1151 (Strix Halo)</td></tr>
	<tr><td style="border:1px solid currentColor; padding:6px 10px;"><code>GGML_HIP_ENABLE_UNIFIED_MEMORY=1</code></td><td style="border:1px solid currentColor; padding:6px 10px;">allow use of the full 128 GB unified memory</td></tr>
	<tr><td style="border:1px solid currentColor; padding:6px 10px;"><code>-dev Vulkan0</code></td><td style="border:1px solid currentColor; padding:6px 10px;">run on Vulkan — fastest backend for ROCmFP4 on Strix Halo</td></tr>
	<tr><td style="border:1px solid currentColor; padding:6px 10px;"><code>-ngl 999 · -fa on</code></td><td style="border:1px solid currentColor; padding:6px 10px;">offload all layers · flash attention</td></tr>
	<tr><td style="border:1px solid currentColor; padding:6px 10px;"><code>-c 131072</code></td><td style="border:1px solid currentColor; padding:6px 10px;">context length (128K)</td></tr>
	<tr><td style="border:1px solid currentColor; padding:6px 10px;"><code>-b 2048 · -ub 256 · -t/-tb 16</code></td><td style="border:1px solid currentColor; padding:6px 10px;">prefill batch / micro-batch · CPU threads</td></tr>
	<tr><td style="border:1px solid currentColor; padding:6px 10px;"><code>-ctk f16 · -ctv f16</code></td><td style="border:1px solid currentColor; padding:6px 10px;">f16 KV cache — how we run it; drop to <code>q8_0</code>/<code>q4_0</code> to use less memory</td></tr>
	<tr><td style="border:1px solid currentColor; padding:6px 10px;"><code>-cpent · -ctxcp · --cache-reuse · --cache-ram 65536</code></td><td style="border:1px solid currentColor; padding:6px 10px;">cross-turn KV checkpointing + 64 GB resident reuse cache</td></tr>
	<tr><td style="border:1px solid currentColor; padding:6px 10px;"><code>--temp 0.6 --top-p 0.95 --top-k 20 --min-p 0.0</code></td><td style="border:1px solid currentColor; padding:6px 10px;">base-model recommended sampling</td></tr>
	<tr><td style="border:1px solid currentColor; padding:6px 10px;"><code>--jinja --parallel 1 --metrics --no-mmap</code></td><td style="border:1px solid currentColor; padding:6px 10px;">apply baked ChatML template · single slot · metrics · weights in RAM</td></tr>
	</tbody>
	</table>
	</div>

	<div style="font-family:ui-monospace,'SF Mono',Consolas,monospace; font-weight:800; font-size:14px; letter-spacing:2px; text-transform:uppercase; border-bottom:2px solid currentColor; padding-bottom:5px; margin:26px 0 12px;"><span style="color:#ea580c;">03</span> · AGENTIC CODING / TOOLS</div>

	Nex-N2-mini is an agentic / "thinking" coder — agentic tool-use trained. To get native tool calls, your client must use the `qwen3_coder` tool-call parser. Without it the model tends to narrate code instead of emitting structured tool calls.

	<div style="overflow:hidden; border-radius:0;">
	<table style="width:100%; border-collapse:collapse; border-radius:0; font-family:ui-monospace,'SF Mono',Consolas,monospace; font-size:12.5px;">
	<tbody>
	<tr><td style="border:1px solid currentColor; padding:8px 11px; width:30%;">CHAT TEMPLATE</td><td style="border:1px solid currentColor; padding:8px 11px;">Qwen (ChatML) — baked into the GGUF; pass <code>--jinja</code></td></tr>
	<tr><td style="border:1px solid currentColor; padding:8px 11px;">TOOL-CALL PARSER</td><td style="border:1px solid currentColor; padding:8px 11px;"><code>qwen3_coder</code> — set in your client/runtime</td></tr>
	<tr><td style="border:1px solid currentColor; padding:8px 11px;">SAMPLING</td><td style="border:1px solid currentColor; padding:8px 11px;">temp <code>0.6</code> · top-p <code>0.95</code> · top-k <code>20</code> (base-model recommended)</td></tr>
	</tbody>
	</table>
	</div>

	<div style="font-family:ui-monospace,'SF Mono',Consolas,monospace; font-weight:800; font-size:14px; letter-spacing:2px; text-transform:uppercase; border-bottom:2px solid currentColor; padding-bottom:5px; margin:26px 0 12px;"><span style="color:#ea580c;">04</span> · PERFORMANCE & QUALITY</div>

	<div style="overflow:hidden; border-radius:0;">
	<table style="width:100%; border-collapse:collapse; border-radius:0; font-family:ui-monospace,'SF Mono',Consolas,monospace; font-size:12.5px;">
	<tbody>
	<tr><td style="border:1px solid currentColor; padding:8px 11px; width:42%;">DECODE · short-context</td><td style="border:1px solid currentColor; padding:8px 11px; font-weight:700;">~72 t/s (Vulkan / Ryzen AI Max+ 395)</td></tr>
	<tr><td style="border:1px solid currentColor; padding:8px 11px;">SWE-BENCH VERIFIED · base model</td><td style="border:1px solid currentColor; padding:8px 11px; font-weight:700;">74.4</td></tr>
	<tr><td style="border:1px solid currentColor; padding:8px 11px;">ACTIVE PARAMS</td><td style="border:1px solid currentColor; padding:8px 11px;">3B of 35B (high-sparsity MoE)</td></tr>
	<tr><td style="border:1px solid currentColor; padding:8px 11px;">QUANTIZATION</td><td style="border:1px solid currentColor; padding:8px 11px;">fast single-scale body + f16 embeddings + Q6 head + code-weighted imatrix</td></tr>
	</tbody>
	</table>
	</div>

	This is the best speed/quality balance in ROCmFP4 — by design, not the absolute fastest. It keeps the two quality levers that are actually felt — genuine f16 token embeddings and a Q6_K output head — on the fast single-scale `q4_0_rocmfp4_fast` body. A leaner 4-bit-output-head build is a few tok/s faster but degrades fidelity you'll notice; an all-dual-scale body buys a KL improvement that sits inside the measurement noise while costing decode speed. The fast body + f16 embeddings + Q6 head is the point where those meet best.

	How we landed on this recipe. We ran the full body-kernel / head-precision / dual-scale sweep — KL divergence vs the BF16 reference plus `llama-bench` decode — on the dense Qwen3.6-27B sibling, where the same `q4_0_rocmfp4` levers apply. The frontier there was unambiguous: the all-dual-scale body and selective higher-precision tensors both traded decode speed for a KL gain inside the noise, so the fast body + f16 embeddings + Q6 head won the balance. We carry that conclusion to this MoE rather than re-running the whole sweep per model — see the [27B sweep](https://huggingface.co/plunderstruck/Qwen3.6-27B-MTP-ROCmFP4-GGUF) for the numbers and the format-limit reasoning. (Directional internal measurements — reproduce before citing.)

	<div style="border:1px solid currentColor; padding:8px 13px; font-family:ui-monospace,'SF Mono',Consolas,monospace; font-size:12px; margin:12px 0; opacity:0.9;">
	<b>WANT MAXIMUM FIDELITY INSTEAD OF SPEED?</b> Grab a <b>Q6_K / Q8_0 GGUF of the base</b> from <a href="https://huggingface.co/nex-agi/Nex-N2-mini"><b>nex-agi/Nex-N2-mini</b></a> — those higher-bit GGUFs run on this same fork. We optimize for throughput in ROCmFP4; if you want the last bit of fidelity over speed, a higher-bit quant of the base is the one to grab.
	</div>

	The imatrix — code-weighted, and measured (it helps here). Quantized with an importance matrix from a code-weighted calibration mix (~2.6:1 code:general — [eaddario](https://huggingface.co/datasets/eaddario/imatrix-calibration) code + Kalomaze `groups_merged` via [`froggeric/imatrix`](https://huggingface.co/datasets/froggeric/imatrix)). Measured by KL-divergence + perplexity vs the true BF16 on a held-out code slice (disjoint from calibration):

	<div style="overflow:hidden; border-radius:0;">
	<table style="width:100%; border-collapse:collapse; border-radius:0; font-family:ui-monospace,'SF Mono',Consolas,monospace; font-size:12.5px;">
	<thead><tr>
	<th style="border:1px solid currentColor; padding:7px 10px; text-align:left; text-transform:uppercase; font-size:10px; letter-spacing:1px;">Metric (vs BF16, held-out code)</th>
	<th style="border:1px solid currentColor; padding:7px 10px; text-align:left; text-transform:uppercase; font-size:10px; letter-spacing:1px;">No-imatrix</th>
	<th style="border:1px solid currentColor; padding:7px 10px; text-align:left; text-transform:uppercase; font-size:10px; letter-spacing:1px;">Imatrix</th>
	<th style="border:1px solid currentColor; padding:7px 10px; text-align:left; text-transform:uppercase; font-size:10px; letter-spacing:1px;">Change</th>
	</tr></thead>
	<tbody>
	<tr><td style="border:1px solid currentColor; padding:7px 10px;"><b>Perplexity</b></td><td style="border:1px solid currentColor; padding:7px 10px;">4.076</td><td style="border:1px solid currentColor; padding:7px 10px;"><b>4.013</b></td><td style="border:1px solid currentColor; padding:7px 10px;"><b>−1.5%</b> (recovers >½ the 4-bit loss; ~3.3σ)</td></tr>
	<tr><td style="border:1px solid currentColor; padding:7px 10px;"><b>Median KLD</b></td><td style="border:1px solid currentColor; padding:7px 10px;">0.0184</td><td style="border:1px solid currentColor; padding:7px 10px;"><b>0.0159</b></td><td style="border:1px solid currentColor; padding:7px 10px;"><b>−13%</b></td></tr>
	<tr><td style="border:1px solid currentColor; padding:7px 10px;">RMS Δp</td><td style="border:1px solid currentColor; padding:7px 10px;">8.57%</td><td style="border:1px solid currentColor; padding:7px 10px;">8.00%</td><td style="border:1px solid currentColor; padding:7px 10px;">−7%</td></tr>
	<tr><td style="border:1px solid currentColor; padding:7px 10px;"><b>Same top token as BF16</b></td><td style="border:1px solid currentColor; padding:7px 10px;">88.97%</td><td style="border:1px solid currentColor; padding:7px 10px;"><b>89.44%</b></td><td style="border:1px solid currentColor; padding:7px 10px;">+0.5 pp</td></tr>
	</tbody>
	</table>
	</div>

	For this model the imatrix is a clean win — better on every metric, including perplexity. (It's model-dependent — on the dense [Qwopus-Coder](https://huggingface.co/plunderstruck/Qwopus3.6-27B-Coder-MTP-ROCmFP4-GGUF) the same recipe worsened code-PPL, so we shipped that one without imatrix. Always measure.)

	<div style="font-family:ui-monospace,'SF Mono',Consolas,monospace; font-weight:800; font-size:14px; letter-spacing:2px; text-transform:uppercase; border-bottom:2px solid currentColor; padding-bottom:5px; margin:26px 0 12px;"><span style="color:#ea580c;">05</span> · BUILD (REPRODUCIBLE)</div>

	```bash
	# code-weighted imatrix on the BF16 (single pass)
	llama-imatrix -m Nex-N2-mini-bf16.gguf -f code-weighted-calib.txt -o nexn2.imatrix -c 512 -ngl 999

	# quant -> ROCmFP4 with the imatrix + genuine f16 embeddings
	llama-quantize --token-embedding-type f16 --imatrix nexn2.imatrix \
	Nex-N2-mini-bf16.gguf \
	Nex-N2-mini-ROCmFP4-STRIX-embF16-imatrix.gguf Q4_0_ROCMFP4_STRIX

	# THE ONE BUILD (★): add the Q6_K output head on the fast single-scale body — best speed/quality balance (§04)
	llama-quantize --token-embedding-type f16 --output-tensor-type q6_K --imatrix nexn2.imatrix \
	Nex-N2-mini-bf16.gguf \
	Nex-N2-mini-ROCmFP4-STRIX-embF16-imatrix-headQ6.gguf Q4_0_ROCMFP4_STRIX
	```

	> Experimental research build for AMD Strix Halo — hardware/driver/prompt-sensitive, may not reproduce elsewhere. Not native FP4 tensor-core execution.

	<div style="font-family:ui-monospace,'SF Mono',Consolas,monospace; font-weight:800; font-size:14px; letter-spacing:2px; text-transform:uppercase; border-bottom:2px solid currentColor; padding-bottom:5px; margin:26px 0 12px;"><span style="color:#ea580c;">06</span> · LINEAGE & CREDITS</div>

	<div style="overflow:hidden; border-radius:0;">
	<table style="width:100%; border-collapse:collapse; border-radius:0; font-family:ui-monospace,'SF Mono',Consolas,monospace; font-size:12.5px;">
	<tbody>
	<tr><td style="border:1px solid currentColor; padding:8px 11px; width:26%;">BASE MODEL</td><td style="border:1px solid currentColor; padding:8px 11px;"><a href="https://huggingface.co/nex-agi/Nex-N2-mini">nex-agi/Nex-N2-mini</a> (Apache-2.0) · Qwen3.5-35B-A3B lineage (35B total / 3B active MoE)</td></tr>
	<tr><td style="border:1px solid currentColor; padding:8px 11px;">CALIBRATION</td><td style="border:1px solid currentColor; padding:8px 11px;"><a href="https://huggingface.co/datasets/eaddario/imatrix-calibration">eaddario/imatrix-calibration</a> (code) + Kalomaze <code>groups_merged</code> via <a href="https://huggingface.co/datasets/froggeric/imatrix">froggeric/imatrix</a> (general)</td></tr>
	<tr><td style="border:1px solid currentColor; padding:8px 11px;">FORMAT + RUNTIME</td><td style="border:1px solid currentColor; padding:8px 11px;"><a href="https://github.com/charlie12345/ROCmFPX">charlie12345/ROCmFPX</a> (based on llama.cpp, MIT)</td></tr>
	</tbody>
	</table>
	</div>

	Derivative quantization — verify the base model's license before redistribution / use.