Instructions to use plunderstruck/FastContext-1.0-4B-SFT-ROCmFP4-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use plunderstruck/FastContext-1.0-4B-SFT-ROCmFP4-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="plunderstruck/FastContext-1.0-4B-SFT-ROCmFP4-GGUF",
	filename="FastContext-1.0-4B-SFT-Q6_0_ROCMFPX_AGENT-bm25imatrix.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/FastContext-1.0-4B-SFT-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/FastContext-1.0-4B-SFT-ROCmFP4-GGUF:BF16
# Run inference directly in the terminal:
llama cli -hf plunderstruck/FastContext-1.0-4B-SFT-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/FastContext-1.0-4B-SFT-ROCmFP4-GGUF:BF16
# Run inference directly in the terminal:
llama cli -hf plunderstruck/FastContext-1.0-4B-SFT-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/FastContext-1.0-4B-SFT-ROCmFP4-GGUF:BF16
# Run inference directly in the terminal:
./llama-cli -hf plunderstruck/FastContext-1.0-4B-SFT-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/FastContext-1.0-4B-SFT-ROCmFP4-GGUF:BF16
# Run inference directly in the terminal:
./build/bin/llama-cli -hf plunderstruck/FastContext-1.0-4B-SFT-ROCmFP4-GGUF:BF16

Use Docker

docker model run hf.co/plunderstruck/FastContext-1.0-4B-SFT-ROCmFP4-GGUF:BF16

LM Studio
Jan
Ollama
How to use plunderstruck/FastContext-1.0-4B-SFT-ROCmFP4-GGUF with Ollama:
```
ollama run hf.co/plunderstruck/FastContext-1.0-4B-SFT-ROCmFP4-GGUF:BF16
```

Unsloth Studio

How to use plunderstruck/FastContext-1.0-4B-SFT-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/FastContext-1.0-4B-SFT-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/FastContext-1.0-4B-SFT-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/FastContext-1.0-4B-SFT-ROCmFP4-GGUF to start chatting

How to use plunderstruck/FastContext-1.0-4B-SFT-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/FastContext-1.0-4B-SFT-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/FastContext-1.0-4B-SFT-ROCmFP4-GGUF:BF16"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use plunderstruck/FastContext-1.0-4B-SFT-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/FastContext-1.0-4B-SFT-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/FastContext-1.0-4B-SFT-ROCmFP4-GGUF:BF16

Run Hermes

hermes

Atomic Chat new

OpenClaw new

How to use plunderstruck/FastContext-1.0-4B-SFT-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/FastContext-1.0-4B-SFT-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/FastContext-1.0-4B-SFT-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/FastContext-1.0-4B-SFT-ROCmFP4-GGUF with Docker Model Runner:
```
docker model run hf.co/plunderstruck/FastContext-1.0-4B-SFT-ROCmFP4-GGUF:BF16
```

Lemonade

How to use plunderstruck/FastContext-1.0-4B-SFT-ROCmFP4-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull plunderstruck/FastContext-1.0-4B-SFT-ROCmFP4-GGUF:BF16

Run and chat with the model

lemonade run user.FastContext-1.0-4B-SFT-ROCmFP4-GGUF-BF16

List all available models

lemonade list

FastContext-1.0-4B-SFT-ROCmFP4-GGUF / README.md

plunderstruck

Repoint to charlie12345/ROCmFPX; add Q6 ROCmFPX-agent fidelity build

ae0d119 verified 22 days ago

preview code

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	---
	base_model: microsoft/FastContext-1.0-4B-SFT
	license: mit
	library_name: gguf
	tags:
	- gguf
	- rocmfp4
	- qwen3
	- fastcontext
	- subagent
	- repository-exploration
	- coder
	- agentic
	- 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;">
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	</pre>
	<div style="flex:0 0 auto; font-size:30px; font-weight:300; opacity:0.55; padding:0 2px;">+</div>
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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;">FASTCONTEXT-1.0-4B</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;">QWEN3 DENSE 4B</span> · <span style="white-space:nowrap;">REPO-EXPLORATION SUBAGENT</span> · <span style="white-space:nowrap;">CODE-WEIGHTED IMATRIX</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;">ARCH</div><div style="font-weight:700;">QWEN3 DENSE</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;">256 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;">PARAMS</div><div style="font-weight:700;">4B DENSE</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;">DRAFT</div><div style="font-weight:700;">NO MTP</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;">MIT</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"/16-bit badge — its parser can't read ROCmFP4 and mislabels the file. These are <b>~4.5 bpw 4-bit</b> ROCmFP4 files; pick by filename in <i>Files and versions</i>.
	</div>

	Experimental AMD Strix Halo (gfx1151) quant of [microsoft/FastContext-1.0-4B-SFT](https://huggingface.co/microsoft/FastContext-1.0-4B-SFT) — Microsoft's repository-exploration subagent for coding agents. Instead of one model both exploring the repo and solving the task, FastContext is invoked on demand by a main agent, fires parallel read-only tool calls (READ / GLOB / GREP), and returns compact file paths + line ranges as focused context. Architecturally it's a plain Qwen3 dense 4B (`Qwen3ForCausalLM`, 36 layers, hidden 2560, 256K context, MIT-licensed), here in the custom ROCmFP4 4-bit format, imatrix-quantized.

	<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;">Body</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;">Pick if</th>
	</tr></thead>
	<tbody>
	<tr><td style="border:1px solid currentColor; padding:7px 10px;"><code>…-STRIX-embF16-imatrix.gguf</code> ★</td><td style="border:1px solid currentColor; padding:7px 10px;">fast</td><td style="border:1px solid currentColor; padding:7px 10px;">2.7 GB</td><td style="border:1px solid currentColor; padding:7px 10px;"><b>the speed build</b> — best speed/quality balance: f16 tied embeddings/head on the fast single-scale body</td></tr>
	<tr><td style="border:1px solid currentColor; padding:7px 10px;"><code>…-Q6_0_ROCMFPX_AGENT-bm25imatrix.gguf</code></td><td style="border:1px solid currentColor; padding:7px 10px;">Q6 · agent</td><td style="border:1px solid currentColor; padding:7px 10px;">3.8 GB</td><td style="border:1px solid currentColor; padding:7px 10px;"><b>the fidelity build</b> — 6-bit ROCmFPX body on the <b>agent</b> profile (structured-output tensors protected: Q6/Q5_K attention, more FFN-down) + bm25 imatrix; closest to BF16 for tool-call/code work</td></tr>
	</tbody>
	</table>
	</div>

	Two builds. The ★ speed build keeps the quality lever that's actually felt — genuine f16 embeddings (from BF16), which also serve as the output head since the model ties them — on the fast single-scale `q4_0_rocmfp4_fast` body + a code-weighted imatrix (see §04): the best speed/quality balance for Strix Halo. The Q6 · agent fidelity build uses the 6-bit `q6_0_rocmfpx` body on the ROCmFPX agent profile (which protects the structured-output pathways — attention K/V at Q6_K/Q5_K, more FFN-down boosted) + a bm25-weighted imatrix: a bit larger/slower, but the closest to BF16 for precise tool-call/code output. Both have the Qwen (ChatML) chat template baked in — just pass `--jinja`.

	<div style="border:1px solid currentColor; padding:8px 13px; font-family:ui-monospace,'SF Mono',Consolas,monospace; font-size:12px; margin:12px 0; opacity:0.85;">
	<b>NOTE // TIED EMBEDDINGS.</b> FastContext has <code>tie_word_embeddings=True</code>, so there's <b>no separate output head</b> — the token-embedding tensor doubles as the lm-head. Setting <code>--token-embedding-type f16</code> therefore gives an <b>f16 embedding <i>and</i> f16 output head</b> in one (no <code>headQ6</code> variant needed — f16 already beats Q6 there).
	</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;">02</span> · QUICK START</div>

	Run from the folder holding the `.gguf` (the Qwen ChatML template is baked in — just pass `--jinja`):

	```bash
	env HSA_OVERRIDE_GFX_VERSION=11.5.1 GGML_HIP_ENABLE_UNIFIED_MEMORY=1 \
	llama-server \
	-m FastContext-1.0-4B-SFT-ROCmFP4-STRIX-embF16-imatrix.gguf \
	--alias fastcontext-4b \
	--host 0.0.0.0 \
	--port 8080 \
	-c 262144 \
	-ctk f16 \
	-ctv f16 \
	--temp 0.7 \
	--top-p 0.8 \
	--top-k 20 \
	-dev Vulkan0 \
	-ngl 999 \
	-fa on \
	-b 2048 \
	-ub 256 \
	-t 16 \
	-tb 16 \
	-cpent 256 \
	-ctxcp 32 \
	--cache-reuse 256 \
	--cache-ram 65536 \
	--jinja \
	--parallel 1 \
	--metrics \
	--no-mmap
	```

	<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 262144</code></td><td style="border:1px solid currentColor; padding:6px 10px;">context length (256K)</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 (cheap on a 4B); drop to <code>q8_0</code>/<code>q4_0</code> to use less memory at deep context</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 0.7 --top-p 0.8 --top-k 20</code></td><td style="border:1px solid currentColor; padding:6px 10px;">Qwen3 recommended sampling (instruct/non-thinking)</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="border:1px solid currentColor; padding:8px 13px; font-family:ui-monospace,'SF Mono',Consolas,monospace; font-size:12px; margin:12px 0; opacity:0.85;">
	<b>NOTE //</b> No <code>--spec-*</code> / <code>--spec-type draft-mtp</code> flags — this arch has <b>no MTP head</b> (see §04). It's already fast on its own.
	</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> · USING IT AS A SUBAGENT</div>

	FastContext isn't a general chat model — it's a repository-exploration subagent meant to be called by your main coding agent, not driven directly. The intended loop: the main agent delegates "find the relevant context for X" → FastContext issues parallel read-only tool calls (`READ`, `GLOB`, `GREP`) → returns compact file paths + line ranges, which the main agent folds into its own context to do the actual work. The point is to keep repo-exploration tokens out of the main agent's window.

	- Chat template: Qwen (ChatML) is baked into the GGUF — just pass `--jinja`.
	- Tool calling: it emits structured `READ`/`GLOB`/`GREP` calls — wire those tools into your harness and use a Qwen/Hermes-style tool-call parser so they're parsed rather than printed. See the [upstream model card](https://huggingface.co/microsoft/FastContext-1.0-4B-SFT) for the exact subagent protocol + tool schema (it expects a specific invocation format).
	- Sampling: temp `0.7`, top-p `0.8`, top-k `20` (Qwen3 instruct defaults) — already set in §02.

	<div style="border:1px solid currentColor; padding:8px 13px; font-family:ui-monospace,'SF Mono',Consolas,monospace; font-size:12px; margin:12px 0; opacity:0.85;">
	<b>NOTE //</b> It's small (4B) and fast (~68 t/s, §04) by design — a cheap, disposable explorer you can fan out in parallel next to a larger main model on the same box. The cross-turn reuse cache (<code>--cache-reuse</code> / <code>--cache-ram</code>) keeps repeated exploration over the same repo cheap.
	</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>

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	<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;">~68 t/s (Vulkan / Ryzen AI Max+ 395)</td></tr>
	<tr><td style="border:1px solid currentColor; padding:8px 11px;">SPECULATIVE DECODE</td><td style="border:1px solid currentColor; padding:8px 11px; font-weight:700;">none (no MTP head)</td></tr>
	<tr><td style="border:1px solid currentColor; padding:8px 11px;">CONTEXT</td><td style="border:1px solid currentColor; padding:8px 11px;">256K native (dense attention)</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 tied emb/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 one quality lever that's actually felt — genuine f16 embeddings, which on this model double as the output head (`tie_word_embeddings=True`), so a single f16 tensor sharpens both the input and output side at near-zero decode cost (it's a lookup, not a matmul) — on top of the fast single-scale `q4_0_rocmfp4_fast` body + a code-weighted imatrix. A leaner Q5-embedding build would shave a couple tok/s but degrades that lever; we keep full f16.

	We didn't re-run the entire rocmfp4 lever sweep on this 4B. We ran it exhaustively on the larger [Qwen3.6-27B](https://huggingface.co/plunderstruck/Qwen3.6-27B-MTP-ROCmFP4-GGUF) — KL divergence vs the BF16 reference plus `llama-bench` decode across an all-dual-scale body, selective higher-precision tensors, and full f16 embeddings. The finding there: an all-dual-scale body and selective higher-precision tensors both cost decode speed for a KL improvement that sat inside the measurement noise, so the fast single-scale body + f16 embeddings is the balance point. That conclusion carries to FastContext — same format, same kernels — so we ship the one build that lands on it rather than a slower variant that wins KL only inside the noise.

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	<b>WANT MAXIMUM FIDELITY INSTEAD OF SPEED?</b> Grab the <b><code>…-Q6_0_ROCMFPX_AGENT-bm25imatrix.gguf</code></b> in this repo — a 6-bit ROCmFPX body on the agent profile (structured-output tensors protected), the closest to BF16 here. If you want even higher, a <b>Q6_K / Q8 GGUF of the base</b> from <a href="https://huggingface.co/microsoft/FastContext-1.0-4B-SFT"><b>microsoft/FastContext-1.0-4B-SFT</b></a> also runs on this same fork.
	</div>

	Fast on its own. ~68 t/s short-context decode on a Ryzen AI Max+ 395 (Vulkan0, measured `llama-bench tg128`). It's a 4B dense Qwen3 with no MTP head, so there's no speculative decoding — it doesn't need it, and at 4B it's a cheap explorer you can run several of in parallel.

	<div style="border:1px solid currentColor; padding:8px 13px; font-family:ui-monospace,'SF Mono',Consolas,monospace; font-size:12px; margin:12px 0; opacity:0.85;">
	<b>NOTE // imatrix.</b> This build is quantized <b>with</b> an importance matrix (Kalomaze <code>groups_merged</code> + froggeric <code>code</code>/<code>technical</code>, via <a href="https://huggingface.co/datasets/froggeric/imatrix">froggeric/imatrix</a>), computed on this model's BF16. We did <b>not</b> run a separate imatrix-vs-no-imatrix ablation on this 4B; at 4+ bpw imatrix is a free polish, not a transformation. Scope note: any fidelity-vs-BF16 figures are a held-out measurement, <b>not</b> an absolute coding benchmark.
	</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;">05</span> · BUILD (REPRODUCIBLE)</div>

	```bash
	# 0) convert the safetensors -> BF16 GGUF (plain qwen3 dense; no MTP, tied embeddings)
	python convert_hf_to_gguf.py FastContext-1.0-4B-SFT/ --outtype bf16 --outfile FastContext-1.0-4B-SFT-BF16.gguf

	# 1) imatrix on the BF16 (general+code: Kalomaze groups_merged + froggeric code/technical)
	llama-imatrix -m FastContext-1.0-4B-SFT-BF16.gguf -f general+code-calib.txt -o fastcontext-4b.imatrix -c 512 -ngl 999

	# 2) THE ONE BUILD: fast single-scale STRIX body + f16 tied emb/head + imatrix (the ★ file) — the balance point (§04).
	# tie_word_embeddings=True -> --token-embedding-type f16 also gives an f16 output head; no --output-tensor-type.
	llama-quantize --token-embedding-type f16 --imatrix fastcontext-4b.imatrix \
	FastContext-1.0-4B-SFT-BF16.gguf FastContext-1.0-4B-SFT-ROCmFP4-STRIX-embF16-imatrix.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>

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	<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/microsoft/FastContext-1.0-4B-SFT">microsoft/FastContext-1.0-4B-SFT</a> (MIT, Microsoft) · repository-exploration subagent · Qwen3 dense 4B (<code>Qwen3ForCausalLM</code>)</td></tr>
	<tr><td style="border:1px solid currentColor; padding:8px 11px;">CALIBRATION</td><td style="border:1px solid currentColor; padding:8px 11px;">Kalomaze <code>groups_merged</code> + froggeric <code>code</code>/<code>technical</code> via <a href="https://huggingface.co/datasets/froggeric/imatrix">froggeric/imatrix</a></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.