What is plunderstruck/FastContext-1.0-4B-SFT-ROCmFP4-GGUF?

--- 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 --- PLUNDERSTRUCK // ROCmFP4 QUANTIZED MODEL // STRIX HALO · gfx1151 <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; lett…

What license applies to plunderstruck/FastContext-1.0-4B-SFT-ROCmFP4-GGUF?

License: mit. Verify terms on Hugging Face before commercial use.

Model Intelligence Sheet

plunderstruck/FastContext-1.0-4B-SFT-ROCmFP4-GGUF overview

Q: What license applies to plunderstruck/FastContext-1.0-4B-SFT-ROCmFP4-GGUF?

License: mit. Verify terms on Hugging Face before commercial use.

Q: How do I run plunderstruck/FastContext-1.0-4B-SFT-ROCmFP4-GGUF locally?

Download a GGUF file from this page and load it in guIDE or llama.cpp. Pipeline task: text-generation.

Q: How much VRAM or disk space does plunderstruck/FastContext-1.0-4B-SFT-ROCmFP4-GGUF need?

Runs locally from ~2.53 GB disk (4 GB VRAM class GPUs with llama.cpp / guIDE).

<div style="border:2px solid currentColor; font family:ui monospace,'SF Mono','Cascadia Mono',Consolas,'Liberation Mono',monospace;" <div style="border bottom:…

ggufrocmfp4qwen3fastcontextsubagentrepository-explorationcoderagenticimatrixstrix-haloamdrocmvulkanenbase_model:microsoft/FastContext-1.0-4B-SFTbase_model:quantized:microsoft/FastContext-1.0-4B-SFTlicense:mitendpoints_compatibleregion:usconversational

Runs locally from ~2.53 GB disk (4 GB VRAM class GPUs with llama.cpp / guIDE).

Downloads

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Pipeline

—

Author

plunderstruck

Repository Files & Downloads

2 GGUF files detected

Direct downloads for local inference

File	Type	Quantization	Size	Link
FastContext-1.0-4B-SFT-Q6_0_ROCMFPX_AGENT-bm25imatrix.gguf	GGUF	Q6_0_ROCMFPX_AGENT	3.51 GB	Download
FastContext-1.0-4B-SFT-ROCmFP4-STRIX-embF16-imatrix.gguf	GGUF	GGUF	2.53 GB	Download

Model Details

Model ID	plunderstruck/FastContext-1.0-4B-SFT-ROCmFP4-GGUF
Author	plunderstruck
Pipeline	—
License	mit
Base model	microsoft/FastContext-1.0-4B-SFT
Last modified	2026-06-21T04:57:37.000Z

Model README

---

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:

base_model_relation: quantized

---

<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="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>

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<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>

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<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>

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<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>.

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<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>.

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Experimental AMD Strix Halo (gfx1151) quant of 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>

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<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>

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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.

<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).

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

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

<th style="border:1px solid currentColor; padding:6px 10px; text-align:left; text-transform:uppercase; font-size:10px; letter-spacing:1px;">Function</th>

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<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>

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</table>

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<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.

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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 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.

<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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<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 — 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.

<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.

<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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# 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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</table>

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

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