Instructions to use llmfan46/Laguna-S-2.1-Uncensored-Heretic-Vision-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use llmfan46/Laguna-S-2.1-Uncensored-Heretic-Vision-GGUF with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="llmfan46/Laguna-S-2.1-Uncensored-Heretic-Vision-GGUF")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("llmfan46/Laguna-S-2.1-Uncensored-Heretic-Vision-GGUF", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use llmfan46/Laguna-S-2.1-Uncensored-Heretic-Vision-GGUF with llama.cpp:

Install (macOS, Linux)

curl -LsSf https://llama.app/install.sh | sh
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf llmfan46/Laguna-S-2.1-Uncensored-Heretic-Vision-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf llmfan46/Laguna-S-2.1-Uncensored-Heretic-Vision-GGUF:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf llmfan46/Laguna-S-2.1-Uncensored-Heretic-Vision-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf llmfan46/Laguna-S-2.1-Uncensored-Heretic-Vision-GGUF:Q4_K_M

Use pre-built binary

# Download pre-built binary from:
# https://github.com/ggerganov/llama.cpp/releases
# Start a local OpenAI-compatible server with a web UI:
./llama-server -hf llmfan46/Laguna-S-2.1-Uncensored-Heretic-Vision-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf llmfan46/Laguna-S-2.1-Uncensored-Heretic-Vision-GGUF:Q4_K_M

Build from source code

git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-server llama-cli
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama-server -hf llmfan46/Laguna-S-2.1-Uncensored-Heretic-Vision-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf llmfan46/Laguna-S-2.1-Uncensored-Heretic-Vision-GGUF:Q4_K_M

Use Docker

docker model run hf.co/llmfan46/Laguna-S-2.1-Uncensored-Heretic-Vision-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use llmfan46/Laguna-S-2.1-Uncensored-Heretic-Vision-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "llmfan46/Laguna-S-2.1-Uncensored-Heretic-Vision-GGUF"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "llmfan46/Laguna-S-2.1-Uncensored-Heretic-Vision-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/llmfan46/Laguna-S-2.1-Uncensored-Heretic-Vision-GGUF:Q4_K_M

SGLang

How to use llmfan46/Laguna-S-2.1-Uncensored-Heretic-Vision-GGUF with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "llmfan46/Laguna-S-2.1-Uncensored-Heretic-Vision-GGUF" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "llmfan46/Laguna-S-2.1-Uncensored-Heretic-Vision-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "llmfan46/Laguna-S-2.1-Uncensored-Heretic-Vision-GGUF" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "llmfan46/Laguna-S-2.1-Uncensored-Heretic-Vision-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Ollama
How to use llmfan46/Laguna-S-2.1-Uncensored-Heretic-Vision-GGUF with Ollama:
```
ollama run hf.co/llmfan46/Laguna-S-2.1-Uncensored-Heretic-Vision-GGUF:Q4_K_M
```
Unsloth Desktop

How to use llmfan46/Laguna-S-2.1-Uncensored-Heretic-Vision-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf llmfan46/Laguna-S-2.1-Uncensored-Heretic-Vision-GGUF:Q4_K_M

Configure the model in Pi

# Install Pi:
npm install -g @earendil-works/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "llmfan46/Laguna-S-2.1-Uncensored-Heretic-Vision-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use llmfan46/Laguna-S-2.1-Uncensored-Heretic-Vision-GGUF with Docker Model Runner:
```
docker model run hf.co/llmfan46/Laguna-S-2.1-Uncensored-Heretic-Vision-GGUF:Q4_K_M
```

Lemonade

How to use llmfan46/Laguna-S-2.1-Uncensored-Heretic-Vision-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull llmfan46/Laguna-S-2.1-Uncensored-Heretic-Vision-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.Laguna-S-2.1-Uncensored-Heretic-Vision-GGUF-Q4_K_M

List all available models

lemonade list

Hermes Agent

How to use llmfan46/Laguna-S-2.1-Uncensored-Heretic-Vision-GGUF with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf llmfan46/Laguna-S-2.1-Uncensored-Heretic-Vision-GGUF:Q4_K_M

Configure Hermes

# Install Hermes:
curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash
hermes setup
# Point Hermes at the local server:
hermes config set model.provider custom
hermes config set model.base_url http://127.0.0.1:8080/v1
hermes config set model.default llmfan46/Laguna-S-2.1-Uncensored-Heretic-Vision-GGUF:Q4_K_M

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use llmfan46/Laguna-S-2.1-Uncensored-Heretic-Vision-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf llmfan46/Laguna-S-2.1-Uncensored-Heretic-Vision-GGUF:Q4_K_M

Configure OpenClaw

# Install OpenClaw:
npm install -g openclaw@latest
# Register the local server and set it as the default model:
openclaw onboard --non-interactive --mode local \
  --auth-choice custom-api-key \
  --custom-base-url http://127.0.0.1:8080/v1 \
  --custom-model-id "llmfan46/Laguna-S-2.1-Uncensored-Heretic-Vision-GGUF:Q4_K_M" \
  --custom-provider-id llama-cpp \
  --custom-compatibility openai \
  --custom-text-input \
  --accept-risk \
  --skip-health

Run OpenClaw

openclaw agent --local --agent main --message "Hello from Hugging Face"

llmfan46 commited on 17 days ago

Commit

24194e8

verified ·

1 Parent(s): f943153

Upload folder using huggingface_hub

Browse files

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+---
+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](https://huggingface.co/llmfan46/Omega-Darker-Gaslight_The-Final-Forgotten-Fever-Dream-24B-ultra-uncensored-heretic-v1/resolve/main/waifu001.webp)
+| Platform | Link | What you get |
+|----------|------|--------------|
+| ☕ Ko-fi | [Coffee Tips](https://ko-fi.com/llmfan46) | 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](https://huggingface.co/llmfan46/Laguna-S-2.1-Uncensored-Heretic)
+# This is a decensored version of [poolside/Laguna-S-2.1](https://huggingface.co/poolside/Laguna-S-2.1), made using [Heretic](https://heretic-project.org/)
+## Performance
+| Metric | This model | Original model ([Qwen3-Coder-Next](https://huggingface.co/Qwen/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](https://huggingface.co/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](https://huggingface.co/poolside/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](https://huggingface.co/poolside/Laguna-S-2.1-DFlash) is available
+  for lower-latency serving
+- **Quantized variants**:
+  [FP8](https://huggingface.co/poolside/Laguna-S-2.1-FP8),
+  [NVFP4](https://huggingface.co/poolside/Laguna-S-2.1-NVFP4),
+  [INT4](https://huggingface.co/poolside/Laguna-S-2.1-INT4) and
+  [GGUF](https://huggingface.co/poolside/Laguna-S-2.1-GGUF)
+- **OpenMDW-1.1 license**: Use and modify the model and associated materials freely
+  for commercial and non-commercial purposes
+  ([learn more about OpenMDW](https://openmdw.ai/))
+## 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](https://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
+```shell
+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](https://huggingface.co/poolside/Laguna-S-2.1-DFlash)
+> by adding
+> `--speculative-config '{"model":"poolside/Laguna-S-2.1-DFlash","num_speculative_tokens":7,"method":"dflash"}'`.
+### SGLang
+```shell
+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
+```shell
+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](https://huggingface.co/poolside/Laguna-S-2.1-GGUF).
+Serve with poolside's llama.cpp fork, branch
+[`laguna`](https://github.com/poolsideai/llama.cpp/tree/laguna), which carries
+full Laguna support including DFlash speculative decoding. (Base Laguna support
+is also in upstream review:
+[ggml-org/llama.cpp#25165](https://github.com/ggml-org/llama.cpp/pull/25165).)
+```shell
+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](https://ollama.com/library/laguna-s-2.1):
+```shell
+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:
+```python
+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](https://huggingface.co/poolside/Laguna-S-2.1/blob/main/LICENSE.md).
+## 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](https://huggingface.co/poolside/Laguna-S-2.1/blob/main/LICENSE.md), and should be used consistently with Poolside's [Acceptable Use Policy](https://poolside.ai/legal/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](mailto:security@poolside.ai).