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cnuland/legunas21-custom
legunas21-custom is a machine learning model from cnuland. Use it for the machine learning task on the model card, and read the license before you ship it in a product. It is set up for gguf. The card lists the license as openmdw-1.1.
Working configuration, tuning notes, and benchmark data for running poolside/Laguna-S-2.1 (118B MoE, ~8B active) on an AMD Strix Halo iGPU (Radeon 8060S, RDNA3.5, unified memory) under OpenShift via the llama.cpp Vulk…
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.gguf652 MB · 100%
From the Hugging Face model README
Working configuration, tuning notes, and benchmark data for running poolside/Laguna-S-2.1 (118B MoE, ~8B active) on an AMD Strix Halo iGPU (Radeon 8060S, RDNA3.5, unified memory) under OpenShift via the llama.cpp Vulkan backend (RADV).
Final serving stack: Myric APEX i-compact
quant (54.4 GB, MoE-aware mixed precision) + metadata-fixed DFlash Q4_K_M draft,
on poolside's llama.cpp fork (branch laguna).
Hardware: 16 GiB VRAM carve-out + ~55 GiB GTT on a 114 GiB node.
| File | What it is |
|---|---|
laguna-s-2.1-DFlash-Q4_K_M-fixed.gguf | The draft to use. Q4_K_M requant of poolside's corrected BF16 DFlash — includes the dflash.attention.sliding_window=512 metadata. |
deployment.yaml | OpenShift Deployment (llama-server, Vulkan, node-pinned) |
bench_laguna.py | Shallow benchmark script (server-side timings) |
speculative-cmath.patch | One-line fix to build the poolside fork with GCC 15 |
Removed: earlier revisions of this repo mirrored community Q4/Q8 DFlash conversions made from poolside's pre-fix upload. Those files are missing the sliding-window metadata and silently collapse to <1% draft acceptance beyond a few K tokens of context (8 t/s at 45K on our hardware). Check git history if you need them; you don't.
Shallow = 512-token code gen. Deep = 45K-token prompt, 256-token continuation.
| Config | Shallow decode | Deep (45K) decode | Draft acceptance |
|---|---|---|---|
| unsloth UD-IQ4_XS (57.5 GB), no spec | 32.4 t/s | ~15 t/s | — |
| APEX i-compact + broken Q4 draft | 41.5 t/s | 8.4 t/s | 44% shallow → 0.8% deep |
| APEX i-compact + fixed Q4 draft, n=4 | 42.7 t/s | 34.6 t/s | 42% shallow → 60% deep |
Deep-context decode improved 2.3× over the original deployment. The DFlash speedup grows with depth: the target slows (KV reads over 45K tokens) while the SWA-corrected draft's cost stays constant (512-token window), so drafting gets relatively cheaper exactly when you need it.
Looping smoke test on APEX i-compact: 3 tool-less diagnostic prompts + 1
adversarial physics puzzle — all terminated with finish_reason=stop, no unclosed
thinking blocks. The model still overthinks hard problems (~11K reasoning tokens
on the physics puzzle) — that is a model trait at any precision; give it
max_tokens ≥ 8k and consider a "think briefly, then act" system prompt.
Poolside's day-one DFlash GGUF lacked sliding-window attention metadata; they fixed
it in discussion #12
("Correct DFlash config", 2026-07-22). Community Q4/Q8 conversions made from the
pre-fix file inherit the bug: fine at shallow depth, useless past ~2-4K tokens.
Diagnose by checking your draft GGUF for dflash.attention.sliding_window = 512;
fix by requantizing the current official BF16:
llama-quantize laguna-s-2.1-DFlash-BF16.gguf laguna-s-2.1-DFlash-Q4_K_M-fixed.gguf Q4_K_M 8
(Quantization preserves metadata — or just use the fixed file from this repo.)
laguna)llama-server \
-m Laguna-S-2.1-APEX-i-compact.gguf \
-md laguna-s-2.1-DFlash-Q4_K_M-fixed.gguf \
--spec-type draft-dflash --spec-draft-n-max 4 \
-ctkd f16 -ctvd f16 -ngld 99 \
--temp 0.7 --top-p 0.95 \
-c 65536 -ngl 99 -fa on \
--cache-type-k q8_0 --cache-type-v q8_0 \
--jinja --parallel 1
laguna). Upstream (b10087–b10103 tested) fails on any DFlash draft with
expected 76, got 69 — the draft borrows token_embd/output.weight from the
target. Apply speculative-cmath.patch for GCC 15 / Ubuntu 26.04.-ctkd f16 -ctvd f16) — quantized draft KV also collapses acceptance.--spec-draft-n-max 4 measured best on bandwidth-limited hardware (poolside's
15, and even their updated 7, were net losses here with a Q4 draft).laguna.rope.scaling.yarn_attn_factor = 1.4852, double-applied by llama.cpp.
Fix: --override-kv laguna.rope.scaling.yarn_attn_factor=float:1.0. The APEX
quants don't need this.--temp 0.7 --top-p 0.95 (the shipped
generation_config's 1.0/1.0 measurably increases fabrications). No min-p.AMD_VULKAN_ICD=RADV,
VK_ICD_FILENAMES=/usr/share/vulkan/icd.d/radeon_icd.json, HIP/ROCR disabled.OpenMDW-1.1, inherited from Laguna S 2.1. The fixed DFlash draft is a direct Q4_K_M requantization of poolside's official DFlash BF16 GGUF (current, post-fix revision); poolside does not endorse this redistribution.