GGUF
English
rocmfp4
qwen3
fastcontext
subagent
repository-exploration
coder
agentic
imatrix
strix-halo
amd
rocm
vulkan
conversational
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.
- 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 Desktop
- Pi
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 @earendil-works/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
- 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
- Hermes Agent
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
- OpenClaw
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"
File size: 24,851 Bytes
2fd4bb5 5689754 910905e 5689754 910905e 2fd4bb5 ae0d119 2fd4bb5 ae0d119 2fd4bb5 ae0d119 2fd4bb5 c75438f 2fd4bb5 c75438f 2fd4bb5 c75438f 2fd4bb5 c75438f 2fd4bb5 c75438f ae0d119 c75438f 2fd4bb5 c75438f 2fd4bb5 c75438f 2fd4bb5 ae0d119 2fd4bb5 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 | ---
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>
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<div style="flex:0 0 auto; font-size:30px; font-weight:300; opacity:0.55; padding:0 2px;">+</div>
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<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.
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<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.
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<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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<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>
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**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.
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**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.
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<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.
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```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.
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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>
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*Derivative quantization — verify the base model's license before redistribution / use.*
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