Laguna-XS.2-GGUF

Laguna-XS.2 GGUF

GGUF quantizations of poolside/Laguna-XS.2. Converted and tested with a patched fork of llama.cpp that implements LLM_ARCH_LAGUNA from scratch.

Requires the custom fork. Upstream llama.cpp does not support this architecture. Fork: https://github.com/linuxid10t/llama.cpp-add-laguna

The same fork also converts and runs the Laguna-M.1 sibling (~226B, all-full-attention, per-element attention gate) — see its own repo.

⚠️ These files were reconverted 2026-06-20 from the latest published poolside/Laguna-XS.2 (config.json last modified 2026-06-18). The previous GGUFs in this repo (a) lacked the rope.dimension_count key, so the global layers silently ran full rotary instead of half (partial_rotary_factor = 0.5), and (b) were produced before several converter fixes. The current files correct this. Verify the key is present (head_dim 128 → expect 64):

python3 gguf-py/scripts/gguf_dump.py Laguna-XS.2-Q4_K_M.gguf | grep rope.dimension_count

Files

Usage

git clone https://github.com/linuxid10t/llama.cpp-add-laguna
cd llama.cpp-add-laguna && cmake -B build && cmake --build build -j$(nproc)

./build/bin/llama-cli \
  -m Laguna-XS.2-Q4_K_M.gguf \
  --ctx-size 262144 \
  --temp 0 \
  -p "The capital of France is"

Chat / thinking mode

# Thinking on (default) — model prefills <think> and generates a reasoning trace
./build/bin/llama-cli -m Laguna-XS.2-Q4_K_M.gguf -cnv --ctx-size 32768

# Thinking off — direct answer, no reasoning trace
./build/bin/llama-cli -m Laguna-XS.2-Q4_K_M.gguf -cnv --ctx-size 32768 --reasoning off

The chat template, thinking-mode prefix, EOT token (</assistant>, token 24), and streaming parser are all handled automatically — no manual prompt engineering needed.

Architecture

Per config.json (full-attention layers = global, sliding-attention layers = SWA):

Implementation Notes

Attention gate: Every layer has a self_attn.g_proj that projects the hidden state to one scalar per head (num_heads outputs). This is softplus-gated and multiplied, broadcast across head_dim, into the SDPA output before o_proj. (XS.2 is per-head gating. The same graph builder does Laguna-M.1's per-element variant, selected by the attention.gate_per_head GGUF key — detected from the actual g_proj tensor shape by the converter, since config gating is a bool rather than a mode string.)

Partial rotary: Global layers rotate only the first half of the head dimension (matching partial_rotary_factor = 0.5); SWA layers use full rotary. The converter writes this explicitly as rope.dimension_count (global) and rope.dimension_count_swa (SWA), derived from each rope config's partial_rotary_factor, and the model reads the per-layer count via n_rot(il) at graph-build time. (For head_dim 128 this is 64 global / 128 SWA. This replaces an earlier hardcoded n_rot_full /= 2.)

Dual RoPE: Global and SWA layers use entirely different RoPE configs. The fork adds per-layer-type YaRN siblings to llama_hparams / llama_cparams and selects at graph build time via hparams.is_swa(il).

MoE routing: Router logits pass through sigmoid (not softmax), biased by a per-expert e_score_correction_bias that is added only during top-k selection, not during weight computation. Routing weights are L1-normalized before scaling by moe_routed_scaling_factor.

Thinking mode prefill: The chat template injects <think> as the generation prompt prefix when thinking is enabled. The streaming parser is patched to treat this as a delimiter-style boundary (not a generated token), so the <think> tag never appears in streaming output.

Stop token: </assistant> is token 24, a regular vocabulary token (not a special token). The fork adds it to antiprompt so the stop-word erase logic strips it before output.

Tested

• Greedy decode on "The capital of France is" → " Paris.\nThe capital of Germany is" ✓
• 10K-token prompt (exercises SWA layers) — no corruption ✓
• 35K needle-in-haystack (SWORDFISH99) — correctly retrieved (exercises global YaRN layers) ✓

(Re-validated against the reconverted Q4_K_M; the half-rotary global layers now match the reference config.)

Known Limitations

• Numerical validation against HF Transformers not yet done (requires CUDA/ROCm).
• Q4_K_M vs f16 top-1 token agreement not formally checked (f16 GGUF exceeds available RAM for full eval).