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Laguna-S-2.1-GGUF
poolside/Laguna-S-2.1-GGUF
Laguna-S-2.1-GGUF is a machine learning model from poolside. Use it for the machine learning task on the model card, and read the license before you ship it in a product.
<p align="center" <img alt="poolside-banner" src="https://poolside.ai/assets/laguna/laguna-s-2-1-banner.svg" width="800px" </p
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.gguf462 GB · 100%
At a glance
- Access
- Public
- Created
- Jul 13, 2026
- Updated
- Aug 6, 2026
- SHA
- 19036076
Base models
Model card
From the Hugging Face model README
<p align="center">
<img alt="poolside-banner" src="https://poolside.ai/assets/laguna/laguna-s-2-1-banner.svg" width="800px">
</p>
<p align="center">
<a href="https://openrouter.ai/poolside/laguna-s-2.1"><strong>Use on OpenRouter</strong></a> ·
<a href="https://vercel.com/ai-gateway/models/laguna-s-2.1"><strong>Use on Vercel AI Gateway</strong></a> ·
<a href="https://poolside.ai/blog/introducing-laguna-s-2-1"><strong>Release blog post</strong></a>
</p>
<br>
Laguna S 2.1 GGUF
GGUF conversions of Laguna S 2.1 for llama.cpp, plus the DFlash speculative-decoding draft model. See the base model card for architecture details, license, and usage guidance.
Files
| File | Size | Notes |
|---|---|---|
laguna-s-2.1-F16.gguf | 235 GB | full precision |
laguna-s-2.1-Q8_0.gguf | 129 GB | routed experts Q8_0, signal path (attention, shared experts, embeddings) kept BF16 |
laguna-s-2.1-Q4_K_M.gguf | 68 GB | routed experts Q4_K (imatrix), signal path kept Q8_0 |
laguna-s-2.1-DFlash-BF16.gguf | 2.2 GB | DFlash drafter for speculative decoding |
laguna-s-2.1.imatrix | 0.4 GB | importance matrix used for the K-quants |
Serving
Serve with Poolside's llama.cpp fork, branch
laguna, which carries full
Laguna support including DFlash speculative decoding. (Base Laguna support is also
in upstream review: ggml-org/llama.cpp#25165.)
git clone --branch laguna https://github.com/poolsideai/llama.cpp
cd llama.cpp && cmake -B build && cmake --build build -j
./build/bin/llama-server -m laguna-s-2.1-Q4_K_M.gguf --jinja --port 8000
# with DFlash speculative decoding:
./build/bin/llama-server -m laguna-s-2.1-Q4_K_M.gguf \
-md laguna-s-2.1-DFlash-BF16.gguf \
--spec-type draft-dflash --spec-draft-n-max 15 \
--spec-draft-override-tensor '.*=CUDA0' \
-fa on --jinja --port 8000
Context length
These GGUFs ship configured for a 262,144-token (256K) context window. This is the configuration we recommend for best output quality.
The weights are native 1M checkpoints: training included a long-context extension stage up to 1,048,576 tokens. To use more than 256K of context with llama.cpp, override the rope configuration at load time:
--ctx-size 1048576 --rope-scaling yarn --rope-scale 128 --yarn-orig-ctx 8192
You may experience quality degradation with the 1M configuration. If you use it, we recommend sampling with --temp 0.7 --top-p 0.95.
This release also corrects the embedded yarn_attn_factor metadata (now 1.0; llama.cpp derives the YaRN attention scaling internally).
- Created
- Jul 13, 2026
- Updated
- Aug 6, 2026