BTL-4 — ROCmFP4 STRIX GGUF — AMD Ryzen AI Max+ 395 / Strix Halo / gfx1151

First ROCmFP4 quantization of badtheorylabs/BTL-4 that exists anywhere (verified against the Hub before publish). Built for AMD Ryzen AI Max+ / Strix Halo (gfx1151).

BTL-4 is a Qwen3.5 MoE vision model: architecture Qwen3_5MoeForConditionalGeneration / model_type: qwen3_5_moe, 40 layers, 256 experts / 8 active, hidden 2048, shared-expert 512, vocab 248320. Upstream text_config.mtp_num_hidden_layers: 0 — there is no MTP head. Do not enable speculative / MTP drafting against this file; a spec flag with no tensors is a silent garbage drafter.

Same architecture family as KAT-Coder-V2.5-Dev (8-of-256 active-param shape), which is where ROCmFP4 STRIX already beat Q4_K_M on Strix Halo. This build reproduces that pattern on BTL-4.

Files

File	Notes
`BTL-4-Q4_0_ROCMFP4_STRIX.gguf`	Text MoE trunk, recipe 105 (`Q4_0_ROCMFP4_STRIX`)
`mmproj-BTL-4-F16.gguf`	Vision projector (F16), load with `-mm` / `--mmproj`

Single-shard: 17.39 GiB text + 0.84 GiB mmproj. Under the HF 50 GB file cap — no split.

Measured A/B (gfx1151, 128 GB unified, ROCm)

Equal conditions for both quants:

Binary: charlie12345/ROCmFPX Laguna Strix export 6255cc8 (export: Laguna Strix ROCmFP4 recipe on top of charlie12345/ROCmFPX@3edc3d3)
Runtime: -dio, HSA_OVERRIDE_GFX_VERSION=11.5.1, GGML_HIP_ENABLE_UNIFIED_MEMORY=1, -ngl 999, --no-warmup, --ignore-eos
256-token generations, nonce-prefixed prompts (prefix cache defeated; cache_n == 0 asserted every run)
3-run medians; Q4_K_M baseline run twice (noise control)
Quality: greedy (temp 0, top_k 1), thinking disabled via chat template kwargs, 10 prompts

Size

Artifact	Bytes	BPW (real)
Upstream BF16 (HF)	70,242,700,904	bf16
F16 GGUF intermediate	69,376,637,024	16.01
This ROCmFP4 STRIX	18,664,879,904	4.31 (dry-run + build; advertised ~4.49)
Same-model Q4_K_M control	21,166,757,664	4.88

STRIX is −11.8% smaller than the Q4_K_M control.

Decode throughput (tok/s, median of 3)

Context	Q4_K_M A	Q4_K_M B	ROCmFP4 STRIX	vs doubled baseline
~8K prompt	54.28	54.02	61.06	+12.8%
~32K prompt	46.59	46.72	51.83	+11.1%

Prompt-eval medians (tok/s): STRIX 1146.8 @8K / 857.4 @32K; Q4_K_M ~1105 / ~835.

Quality (10-prompt greedy battery)

	Score
ROCmFP4 STRIX	10 / 10
Q4_K_M	10 / 10

Equal quality, clear speed win, smaller file → ship.

Recipe notes

Prefer Q4_0_ROCMFP4_STRIX (105) over _STRIX_LEAN (106): same speed class, better quality headroom on this fork’s prior Strix A/Bs.
Real dry-run BPW was 4.31, not the type’s advertised ~4.49. Always read dry-run.
Converted from the official BF16 with the fork’s convert_hf_to_gguf.py (Qwen3_5MoeForConditionalGeneration + --mmproj). No --mtp.

Launch (Strix Halo / gfx1151)

env HSA_OVERRIDE_GFX_VERSION=11.5.1 \
    GGML_HIP_ENABLE_UNIFIED_MEMORY=1 \
  llama-server \
    -m BTL-4-Q4_0_ROCMFP4_STRIX.gguf \
    --mmproj mmproj-BTL-4-F16.gguf \
    -ngl 999 -dio --no-warmup --jinja \
    -c 32768 --parallel 1 \
    --temp 0.0 --top-k 1

Do not pass MTP / speculative draft flags. Upstream has zero MTP layers.

Requires a ROCmFP4-capable llama.cpp build (ROCmFPX / Laguna Strix recipe), not stock llama.cpp alone, for the ROCmFP4 tensor types.

License

Inherited from badtheorylabs/BTL-4 (Apache-2.0 on the base card at publish time). All credit to the base authors; this repo is a quantization only (base_model_relation: quantized).

Other public builds of this model

Compiled from Hugging Face repository metadata — file sizes, shipped files, quant variant as named by each repo. No third-party build was run or benchmarked here, so this table makes no speed or quality claim about any of them. It is here so you can see the size and format options at a glance and pick what fits your hardware.

Repository	Largest model file	Variant	Ships	Downloads	Likes
`kingjones777/BTL-4-ROCmFP4-STRIX_LEAN-GGUF`	17.32 GiB	STRIX_LEAN	vision	102	0
`kingjones777/BTL-4-ROCmFP4-STRIX-GGUF` (this repo)	17.38 GiB	STRIX	vision	77	0

Base model: badtheorylabs/BTL-4. Generated from Hub metadata; download counts move over time.

Acknowledgements

This build would not exist without the work below. Please star and follow these projects — the quantisation format used here is their engineering, not mine.

ROCmFPX — maintained by charlie12345 / caf The ROCmFP4 / ROCmFPX tensor formats (ggml types 100–106) exist only in this fork. Every ROCmFP4 file in this repository was produced with its llama-quantize, and runs on its runtime. The fork also credits collaborators ciru-ai, Tom Turney, PlunderStruck and Aydan S., and acknowledges AMD for hardware support. Licensed MIT, based on upstream llama.cpp.

llama.cpp — ggml-org and contributors The inference engine, GGUF format and conversion tooling everything here is built on.

AMD ROCm The compute platform these builds target — ROCm 7.2.4 on gfx1151 / Radeon 8060S.

Base model authors — see base_model in the metadata above; all model weights, licences and capabilities are theirs. This repository contributes quantisation and measurement only.

If you use these files, please credit ROCmFPX alongside this repository.

kingjones777/BTL-4-ROCmFP4-STRIX-GGUF