Instructions to use SC117/Laguna-S-2.1-Uncensored-APEX-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use SC117/Laguna-S-2.1-Uncensored-APEX-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="SC117/Laguna-S-2.1-Uncensored-APEX-GGUF",
	filename="Laguna-S-2.1-Uncensored-APEX-I-Balanced.gguf",
)

llm.create_chat_completion(
	messages = [
		{
			"role": "user",
			"content": "What is the capital of France?"
		}
	]
)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use SC117/Laguna-S-2.1-Uncensored-APEX-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 SC117/Laguna-S-2.1-Uncensored-APEX-GGUF:BF16
# Run inference directly in the terminal:
llama cli -hf SC117/Laguna-S-2.1-Uncensored-APEX-GGUF:BF16

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf SC117/Laguna-S-2.1-Uncensored-APEX-GGUF:BF16
# Run inference directly in the terminal:
llama cli -hf SC117/Laguna-S-2.1-Uncensored-APEX-GGUF:BF16

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 SC117/Laguna-S-2.1-Uncensored-APEX-GGUF:BF16
# Run inference directly in the terminal:
./llama-cli -hf SC117/Laguna-S-2.1-Uncensored-APEX-GGUF:BF16

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 SC117/Laguna-S-2.1-Uncensored-APEX-GGUF:BF16
# Run inference directly in the terminal:
./build/bin/llama-cli -hf SC117/Laguna-S-2.1-Uncensored-APEX-GGUF:BF16

Use Docker

docker model run hf.co/SC117/Laguna-S-2.1-Uncensored-APEX-GGUF:BF16

LM Studio
Jan

vLLM

How to use SC117/Laguna-S-2.1-Uncensored-APEX-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "SC117/Laguna-S-2.1-Uncensored-APEX-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": "SC117/Laguna-S-2.1-Uncensored-APEX-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/SC117/Laguna-S-2.1-Uncensored-APEX-GGUF:BF16

Ollama
How to use SC117/Laguna-S-2.1-Uncensored-APEX-GGUF with Ollama:
```
ollama run hf.co/SC117/Laguna-S-2.1-Uncensored-APEX-GGUF:BF16
```

Unsloth Studio

How to use SC117/Laguna-S-2.1-Uncensored-APEX-GGUF with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for SC117/Laguna-S-2.1-Uncensored-APEX-GGUF to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for SC117/Laguna-S-2.1-Uncensored-APEX-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for SC117/Laguna-S-2.1-Uncensored-APEX-GGUF to start chatting

How to use SC117/Laguna-S-2.1-Uncensored-APEX-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf SC117/Laguna-S-2.1-Uncensored-APEX-GGUF:BF16

Configure the model in Pi

# Install Pi:
npm install -g @mariozechner/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "SC117/Laguna-S-2.1-Uncensored-APEX-GGUF:BF16"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use SC117/Laguna-S-2.1-Uncensored-APEX-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 SC117/Laguna-S-2.1-Uncensored-APEX-GGUF:BF16

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 SC117/Laguna-S-2.1-Uncensored-APEX-GGUF:BF16

Run Hermes

hermes

Atomic Chat new

OpenClaw new

How to use SC117/Laguna-S-2.1-Uncensored-APEX-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf SC117/Laguna-S-2.1-Uncensored-APEX-GGUF:BF16

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 "SC117/Laguna-S-2.1-Uncensored-APEX-GGUF:BF16" \
  --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"

Docker Model Runner
How to use SC117/Laguna-S-2.1-Uncensored-APEX-GGUF with Docker Model Runner:
```
docker model run hf.co/SC117/Laguna-S-2.1-Uncensored-APEX-GGUF:BF16
```

Lemonade

How to use SC117/Laguna-S-2.1-Uncensored-APEX-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull SC117/Laguna-S-2.1-Uncensored-APEX-GGUF:BF16

Run and chat with the model

lemonade run user.Laguna-S-2.1-Uncensored-APEX-GGUF-BF16

List all available models

lemonade list

ABLITERIX TRIAL 16 APEX OpenMDW-1.1

Laguna-S-2.1-Uncensored-APEX-GGUF

English | 📖 中文文档

Uncensored 118B-A8B MoE · abliterix Trial 16 · APEX I-tier GGUFs + imatrix

🌊 About this release

Laguna S 2.1 is a poolside ~118B total / ~8B active per token Mixture-of-Experts model for agentic coding and long-horizon work. Architecture highlights: token-choice routing with softplus gates, 256 routed experts + 1 shared expert (top-10), GQA, 1:3 global/sliding-window attention (48 layers: 12 global + 36 local, window 512), and up to about 1M context, with optional native thinking interleaved with tool use.

This release builds on the official weights in two steps:

Uncensored behavior edit via abliterix (ROCm + bitsandbytes 4-bit search path), selecting Trial 16 LoRA, stream-merged back to BF16, plus MoE safety-expert router bake-in.
APEX mixed-precision GGUF from a poolside llama.cpp (laguna) BF16 GGUF, using APEX-style tensor-type configs + imatrix (token embedding / output kept at BF16).

License: OpenMDW-1.1 (same family as the base model). See openmdw.ai and poolside terms.

⚠️ Uncensored notice

After merging abliterix Trial 16, this model shows a much lower refusal rate and can differ substantially from official Laguna-S-2.1. Evaluate compliance and safety for your use case; control access and audit as needed.

Refusals (harmful eval)	8 / 100 (baseline ~97 / 100)
KL divergence	~0.0042
Length deviation	~0.07 σ
Generation health	PASSED
Selected trial	abliterix Trial 16

Implementation sketch: LoRA merge W += (B @ A) * (alpha / r) (this trial alpha = r = 1); per-layer top ~20 safety experts with router row scale ~0.74 (see merge metadata).

📜 Disclaimer (identity & text quality)

The following can also appear with official Laguna-S-2.1 (including high-precision online deployments). They are not introduced by this Uncensored edit and should not be blamed on abliterix / the refusal merge:

Model identity quirks or self-descriptions that diverge from the official persona;
Odd tokens, typos, stiffness, or occasional awkward phrasing in Chinese and other languages.

This Uncensored pipeline mainly changes refusal / safety-related behavior. More aggressive quant tiers (e.g. Compact) may amplify existing text noise, but the underlying issues already exist in the original model and/or the local quant+inference stack. For a fair check, compare official vs this release under the same serving settings.

🧠 Model details

Architecture	Laguna MoE (poolside `laguna`)
Parameters	~118B total, ~8B active / token
Layers	48 (L0 dense FFN, L1–L47 MoE)
Experts	256 routed + 1 shared, top-10
Attention	GQA, 8 KV heads, head dim 128
Context	Up to ~1,048,576 tokens (practical limit depends on VRAM / `-c`)
Vocab	100,352 (Laguna family tokenizer)
Modality	Text → text (no mmproj in this package)
This repo	APEX I-Quality / I-Balanced / I-Compact GGUF

Coding / tool ability is largely retained; refusal and alignment behavior are changed. Full official bench tables were not re-run for this derivative.

💡 What is APEX?

These files use APEX-style MoE-aware mixed precision: precision follows tensor role + layer position (higher on edges, more aggressive in the middle), with imatrix for the I- tiers.

Common settings for this package: source Laguna-S-2.1-Uncensored BF16 GGUF; calibration laguna-s-2.1.imatrix; token embedding / output = BF16; routers and norms largely left at high precision defaults. Configs target Laguna 48-layer naming (ffn_*_exps / ffn_*_shexp / attn_*, L0 dense).

📦 APEX quantization tiers

File	Size	Mid experts	Best for
`*-I-Quality.gguf`	~70 GB	edge Q6_K / near Q5_K / mid iq4_xs; shared Q8_0; attn Q6_K	Smaller high-quality try (IQ mid-layers)
`*-I-Balanced.gguf`	~80 GB	edge Q6_K / near & mid Q5_K; shared Q8_0; attn Q6_K	Recommended default for Chinese users — steadier than Compact
`*-I-Compact.gguf`	~52 GB	edge Q4_K / mid Q3_K; shared Q6_K; attn Q4_K	Tighter memory; more quant noise OK

How to choose:

Chinese-heavy use: prefer I-Balanced. Mid experts stay Q5_K (not iq4_xs), which usually feels more stable. Drop to I-Compact only if memory is tight.
English / coding: tier differences are usually small; pick by VRAM/speed (Balanced vs Compact). I-Quality is optional when you want IQ mid-layers and a smaller footprint than Balanced.

🚀 Usage (llama.cpp)

Use a build that understands the laguna architecture (poolside laguna branch or equivalent).

Example (I-Balanced recommended)

./llama-server \
  -m ./Laguna-S-2.1-Uncensored-APEX-I-Balanced.gguf \
  --port 8080 \
  -sm none \
  --device rocm0 \
  --ctx-size 131072 \
  --flash-attn on \
  --no-mmap \
  --fit on \
  --jinja \
  --host 0.0.0.0

Must recognize general.architecture = laguna.
Thinking / tools: configure per client/server docs (e.g. enable_thinking, reasoning options).
Warnings like special_eos_id is not in special_eog_ids are tokenizer metadata hints; the model can still load. If stop/truncation is odd, check stop / max_tokens / template.
Text-only GGUF; no mmproj.

🎛️ Recommended sampling

General / coding	temperature 0.6–1.0, top_p 0.95, top_k 20
More stable	Slightly lower temperature

🔧 Build pipeline (summary)

Base: poolside/Laguna-S-2.1
abliterix search → Trial 16 LoRA + MoE router adjustments
BF16 stream-merge → Laguna-S-2.1-Uncensored
poolside llama.cpp → BF16 GGUF + imatrix
APEX tensor-type-file + imatrix → I-Quality / I-Balanced / I-Compact

Disclaimer

Community derivative (behavior edit + quantization). Not an official poolside release. Use at your own risk; follow local law and upstream licenses.

Downloads last month: 2,041

GGUF

Model size

118B params

Architecture

laguna

Hardware compatibility

16-bit

View +3 variants

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