---

library_name: transformers

inference: false

extra_gated_description: >-

To learn more about how we process your personal data, please read our <a

href="https://poolside.ai/legal/privacy">Privacy Policy</a>.

tags:

• heretic
• uncensored
• decensored
• abliterated
• laguna-s-2.1
• vllm

base_model:

• llmfan46/Laguna-S-2.1-Uncensored-Heretic

license: openmdw-1.1

pipeline_tag: text-generation

---

<div style="background-color: #ff4444; color: white; padding: 20px; border-radius: 10px; text-align: center; margin: 20px 0;">

<h2 style="color: white; margin: 0 0 10px 0;">🚨⚠️ I HAVE REACHED HUGGING FACE'S FREE STORAGE LIMIT ⚠️🚨</h2>

<p style="font-size: 18px; margin: 0 0 15px 0;">I can no longer upload new models unless I can cover the cost of additional storage.<br>I host <b>70+ free models</b> as an independent contributor and this work is unpaid.<br><b>Without your support, no more new models can be uploaded.</b></p>

<p style="font-size: 20px; margin: 0;">

<a href="https://ko-fi.com/llmfan46" style="color: white; text-decoration: underline;">☕ Ko-fi</a>

</p>

<p style="font-size: 16px; margin: 10px 0 0 0;">Every contribution goes directly toward Hugging Face storage fees to keep models free for everyone.</p>

</div>

---

94% fewer refusals (6/100 Uncensored vs 97/100 Original) while preserving model quality (0.0300 KL divergence).

❤️ Support My Work

Creating these models takes significant time, work and compute. If you find them useful consider supporting me:

!image/png

| Platform | Link | What you get |

|----------|------|--------------|

| ☕ Ko-fi | Coffee Tips | My eternal gratitude |

Your help will motivate me and would go into further improving my workflow and coverings fees for storage, compute and may even help uncensoring bigger model with rental Cloud GPUs.

-----

GGUF quantizations of llmfan46/Laguna-S-2.1-Uncensored-Heretic

This is a decensored version of poolside/Laguna-S-2.1, made using Heretic

Performance

| Metric | This model | Original model (Qwen3-Coder-Next) |

| :----- | :--------: | :---------------------------: |

| KL divergence | <span style="color:darkgoldenrod">0.0300</span> | 0 (by definition) |

| Refusals | ✅ <span style="color:darkgreen">6/100</span> | ❌ <span style="color:blue">97/100</span> |

Lower refusals indicate fewer content restrictions, while lower KL divergence indicates more closeness to the original model's baseline. Higher refusals cause more rejections, objections, pushbacks, lecturing, censorship, softening and deflections.

-----

Quantizations

| Filename | Quant | Description |

|----------|-------|-------------|

| Laguna-S-2.1-Uncensored-Heretic-BF16.gguf | BF16 | Full precision |

| Laguna-S-2.1-Uncensored-Heretic-Q8_0.gguf | Q8_0 | Near-lossless, recommended |

| Laguna-S-2.1-Uncensored-Heretic-Q6_K.gguf | Q6_K | Excellent quality |

| Laguna-S-2.1-Uncensored-Heretic-Q5_K_M.gguf | Q5_K_M | Good balance |

| Laguna-S-2.1-Uncensored-Heretic-Q5_K_S.gguf | Q5_K_S | Smaller Q5 |

| Laguna-S-2.1-Uncensored-Heretic-Q4_K_M.gguf | Q4_K_M | Good for limited VRAM |

| Laguna-S-2.1-Uncensored-Heretic-Q4_K_S.gguf | Q4_K_S | Smaller Q4 |

| Laguna-S-2.1-Uncensored-Heretic-Q3_K_L.gguf | Q3_K_L | Low VRAM, decent quality

| Laguna-S-2.1-Uncensored-Heretic-Q3_K_M.gguf | Q3_K_M | Low VRAM, smaller |

| Laguna-S-2.1-Uncensored-Heretic-Q3_K_S.gguf | Q3_K_S | Very Low VRAM |

| Laguna-S-2.1-Uncensored-Heretic-Q2_K.gguf | Q2_K | Very Very Low VRAM, only use if you have no other options |

Vision Projector

| Filename | Quant | Description |

|----------|-------|-------------|

| Laguna-S-2.1-Uncensored-Heretic-mmproj-BF16.gguf | BF16 | Native precision |

| Laguna-S-2.1-Uncensored-Heretic-mmproj-F16.gguf | F16 | F16 precision |

A Vision Projector File is Required for vision/multimodal capabilities. Use alongside any quantization above.

Usage

Works with llama.cpp, LM Studio, Ollama, and other GGUF-compatible tools.

Vision support (experimental)

This repo includes two multimodal projector files built from

numinousmuses/laguna-s-2.1-vision

(frozen Qwen3-VL vision tower + a 35.4M-parameter trained projector, MIT — all credit

to its author). Either works with any text quant in this repo; use F16 if your

backend has trouble with BF16 (e.g. Vulkan or older builds):

llama-server -m Laguna-S-2.1-Uncensored-Heretic-Q6_K.gguf \

--mmproj Laguna-S-2.1-Uncensored-Heretic-mmproj-BF16.gguf -ngl 99 --jinja

No extra flags or template overrides are needed — these GGUFs embed a chat template

tuned for llama.cpp/LM Studio multimodal use (images are rendered before the question

text, matching the projector's training order).

**Vision works, but this is a grafted projector, not a natively-trained VLM — set

your expectations:**

• Reliable: image attached in the first message of a conversation, short

factual questions ("What does the sign say?", "What animal is this?"), low

temperature (≤ 0.3) for vision turns.

• Best-effort: images added mid-conversation. The model may answer tersely

("Answer: X"), misidentify the subject, or occasionally ignore the image —

regenerate, or start a fresh chat with the image first for anything that matters.

• Weak by design (per the upstream projector's training: 2,070 steps of

short-form VQA, single-turn, no chat template): long detailed descriptions,

trick/false-premise questions (hallucination-prone), and fine print — add

--image-min-tokens 1024 for document images.

• The projector was trained against stock Laguna-S-2.1; this repo pairs it with an

abliterated backbone, so vision quality may sit slightly below the upstream

author's published benchmarks.

-----

<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

Laguna S 2.1 is a 118B total parameter Mixture-of-Experts model with 8B activated

parameters per token, designed for agentic coding and long-horizon work. It sits

between Laguna XS 2.1 (33B-A3B) and

Laguna M.1 (225B-A23B) in the Laguna series and shares the family recipe: a

token-choice router with softplus gating over 256 routed experts plus one shared

expert, grouped-query attention, and interleaved full/sliding-window attention.

Highlights

• Mixed SWA and global attention layout: 48 layers in a 1:3 global-to-SWA ratio

(12 global attention layers, 36 sliding-window layers, window 512), with softplus

attention gating and per-layer-type rotary scales

• 1M context: 1,048,576-token context window
• Native reasoning support: interleaved thinking between tool calls, with

per-request control via enable_thinking

• Speculative decoding: a trained

DFlash draft model is available

for lower-latency serving

• Quantized variants:

FP8,

NVFP4,

INT4 and

GGUF

• OpenMDW-1.1 license: Use and modify the model and associated materials freely

for commercial and non-commercial purposes

(learn more about OpenMDW)

Model overview

• Number of parameters: 118B total, ~8B activated per token
• Layers: 48 (12 global attention, 36 sliding-window attention)
• Experts: 256 routed (top-10) plus 1 shared expert
• Attention: grouped-query, 8 KV heads, head dim 128; per-head softplus output gating
• Sliding window: 512 tokens
• Context window: 1,048,576 tokens
• Vocabulary: 100,352 tokens (Laguna family tokenizer)
• Modality: text-to-text
• Reasoning: interleaved thinking with preserved thinking

Benchmark results

<p align="center">

<img alt="benchmarks" src="https://poolside.ai/assets/laguna/laguna-s-2-1-chart.svg" width="800px">

</p>

| Model | Size | Terminal-Bench 2.1 | SWE-bench Multilingual | SWE-Bench Pro (Public Dataset) | DeepSWE | SWE Atlas (Codebase QnA) | Toolathlon Verified |

|---|---|---|---|---|---|---|---|

| Laguna S 2.1 | 118B-A8B | 70.2% | 78.5% | 59.4% | 40.4% | 46.2% | 49.7% |

| Tencent Hy3 | 295B-A21B | 71.7% | 75.8% | 57.9% | - | - | - |

| Inkling | 975B-A41B | 63.8% | - | 54.3% | - | - | 45.5%* |

| Nemotron 3 Ultra | 550B-A55B | 56.4% | 67.7% | - | - | - | 34.3%* |

| DeepSeek-V4-Pro Max | 1.6T-A49B | 64.0% | 76.2% | 55.4% | 9.0% | 27.2% | 55.9% |

| Kimi K3 | 2800B-A50B | 88.3% | - | - | 69% | - | - |

| Qwen 3.7 Max | - | 74.5%* | 78.3% | 60.6% | - | - | - |

| Muse Spark 1.1 | - | 80% | - | 61.5% | 53.3% | 42.2%* | 75.6% |

| Claude Fable 5 | - | 88% | - | 80.3% | 70% | - | - |

Benchmarks as of 21 July 2026. Laguna S 2.1 in bold; a dash (-) marks a benchmark a model was not evaluated on. Scores marked * are as reported by third parties: Terminal-Bench 2.1 and DeepSWE via Artificial Analysis, SWE Atlas via Scale AI's official leaderboard, and Toolathlon Verified via its official leaderboard. Full evaluation trajectories: trajectories.poolside.ai.

Usage

Laguna S 2.1 uses the same laguna architecture as Laguna XS 2.1, so the same

engine integrations apply (vLLM, SGLang, Transformers, TRT-LLM, llama.cpp). At 118B

parameters the BF16 checkpoint needs multiple GPUs (roughly 236GB of weights);

quantized variants reduce this substantially.

vLLM

vllm serve \
    --model poolside/Laguna-S-2.1 \
    --tensor-parallel-size 4 \
    --tool-call-parser poolside_v1 \
    --reasoning-parser poolside_v1 \
    --enable-auto-tool-choice \
    --served-model-name laguna \
    --default-chat-template-kwargs '{"enable_thinking": true}'

> [!NOTE]

> Optional: speculative decoding with DFlash. Pair with the

> Laguna S 2.1 DFlash draft model

> by adding

> --speculative-config '{"model":"poolside/Laguna-S-2.1-DFlash","num_speculative_tokens":7,"method":"dflash"}'.

SGLang

python -m sglang.launch_server \
  --model-path poolside/Laguna-S-2.1 \
  --tp-size 4 \
  --reasoning-parser poolside_v1 \
  --tool-call-parser poolside_v1 \
  --trust-remote-code

TRT-LLM

trtllm-serve poolside/Laguna-S-2.1 --trust-remote-code \
    --tool_parser poolside_v1 --reasoning_parser laguna

Note the flag names differ from vLLM's (--tool_parser, and the reasoning parser

is laguna, not poolside_v1).

llama.cpp

GGUF conversions are available at

poolside/Laguna-S-2.1-GGUF.

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 7 -fa on --jinja --port 8000

Ollama

Run directly from the Ollama library:

ollama run laguna-s-2.1

Quantization variants are available as tags (q4_K_M, q8_0, f16, mxfp8,

nvfp4, mlx-bf16), for example ollama run laguna-s-2.1:q8_0. The Laguna chat

template is baked into the model, so tool-calling and interleaved reasoning work

automatically.

Controlling reasoning

Laguna S 2.1 has native reasoning support and works best with preserved thinking:

keep reasoning_content from prior assistant messages in the message history.

The model will generally reason before calling tools and between tool calls, and

may stop reasoning in follow-up steps if prior thinking blocks are dropped.

Thinking is controlled per request via the chat template:

extra_body={"chat_template_kwargs": {"enable_thinking": False}}

or at the server level with

--default-chat-template-kwargs '{"enable_thinking": true}'. For agentic coding

use cases we recommend enabling thinking and preserving reasoning in the message

history.

License

This model is licensed under the OpenMDW-1.1 License.

Intended and Responsible Use

Laguna S 2.1 is designed for software engineering and agentic coding use cases, and you are responsible for confirming that it is appropriate for your intended application. Laguna S 2.1 is subject to the OpenMDW-1.1 License, and should be used consistently with Poolside's Acceptable Use Policy. We advise against circumventing Laguna S 2.1 safety guardrails without implementing substantially equivalent mitigations appropriate for your use case.

Please report security vulnerabilities or safety concerns to security@poolside.ai.