raulvidis/Laguna-S-2.1-ROCmFP4-COHERENT-GGUF overview
Laguna S 2.1 — ROCmFP4 COHERENT Strix Halo optimized AMD optimized 4 bit quant of poolside/Laguna S 2.1 https://huggingface.co/poolside/Laguna S 2.1 118B MoE, …
Runs locally from ~61.78 GB disk (32 GB+ VRAM class GPUs with llama.cpp / guIDE).
Repository Files & Downloads
| File | Type | Quantization | Size | Link |
|---|---|---|---|---|
| Laguna-S-2.1-Q4_0_ROCMFP4_COHERENT.gguf | GGUF | Q4_0_ROCMFP4_COHERENT | 61.78 GB | Download |
Model Details
Model README
---
license: other
license_name: openmdw-1.1
license_link: https://huggingface.co/poolside/Laguna-S-2.1
base_model: poolside/Laguna-S-2.1
tags:
- gguf
- rocm
- rocmfp4
- amd
- strix-halo
- laguna
---
Laguna-S-2.1 — ROCmFP4 COHERENT (Strix Halo optimized)
AMD-optimized 4-bit quant of poolside/Laguna-S-2.1 (118B MoE, 8B active, 1M ctx) using the Q4_0_ROCMFP4_COHERENT tensor-protected format from charlie12345/ROCmFPX.
File: Laguna-S-2.1-Q4_0_ROCMFP4_COHERENT.gguf — 58.3 GiB, ~4.3 bpw effective.
Why this quant
- COHERENT recipe protects agent-critical tensors (attention, embeddings, structured-output paths) at higher precision — in our testing it had the best long-context integrity of any Laguna quant we measured (correct refusals instead of confabulation on absent-information probes).
- Corrected metadata:
laguna.rope.scaling.yarn_attn_factoris baked to1.0per poolside's upstream fix ("llama.cpp derives mscale") — upstream-converted GGUFs from before 2026-07-24 carry1.4852, which double-applies YaRN attention scaling. No--override-kvneeded with this file. - Quantized directly from poolside's BF16 with poolside's official imatrix (no intermediate quant).
Requirements
⚠️ Does not load in stock llama.cpp. Requires the ROCmFPX runtime (built/tested at commit c190e435, 2026-07-23):
git clone https://github.com/charlie12345/ROCmFPX
env JOBS=16 scripts/build-strix-rocmfp4-mtp.sh # gfx1151 / Strix Halo
Measured on AMD Ryzen AI MAX+ 395 (Radeon 8060S, Strix Halo, 128GB)
| Metric | Value |
|---|---|
| Decode (tg128) | ~32–35 t/s (HIP), ~37 t/s (Vulkan) |
| Prefill (pp512) | ~405 t/s (HIP) |
| 256k context serving | validated (HIP device; ~194k-token cold prompt in ~12 min) |
| Warm-turn TTFT (49k-token cached prefix) | ~0.3 s prefill (--cache-reuse 256) |
| KV q8_0 decode cost | negligible (<1%) |
Recommended serve command:
HSA_OVERRIDE_GFX_VERSION=11.5.1 GGML_HIP_ENABLE_UNIFIED_MEMORY=1 \
llama-server -m Laguna-S-2.1-Q4_0_ROCMFP4_COHERENT.gguf \
-ngl 999 -fa on -dev ROCm0 -c 262144 --jinja \
-b 2048 -ub 1024 --cache-reuse 256 \
--temp 0.7 --top-p 0.95 --top-k 20 --min-p 0
Notes: thinking is on by default (disable per request via chat_template_kwargs: {"enable_thinking": false}); give thinking ≥32k max_tokens or it can exhaust the budget mid-reasoning. Vulkan devices failed very large (>190k-token) single fills in our testing across all runtimes — use the HIP device for extreme contexts.
Provenance & credits
- Base model & imatrix & DFlash draft: poolside (Laguna S 2.1, OpenMDW 1.1 + model terms — review before use)
- ROCmFP4 codebook & ROCmFPX runtime: charlie12345/caf
- Quantization & benchmarking: raulvidis, 2026-07-24
License follows the base model (OpenMDW 1.1 + poolside model terms).
Run raulvidis/Laguna-S-2.1-ROCmFP4-COHERENT-GGUF with guIDE
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Source: Hugging Face · Compare models