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. Thinking was restored: the abliteration-time force-false override was removed from the chat template (see below).
Full-precision release: this repo ships the uncensored weights as BF16 safetensors (46 shards, ~219 GB). Quantized APEX I-tier GGUFs + imatrix built from this exact merge live in the sibling repo Laguna-S-2.1-Uncensored-APEX-GGUF.

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
Thinking	Restored — `chat_template.jinja` uses `enable_thinking \| default(true)` (abliteration-time force-`false` override removed)

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_INFO.json).

📜 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. This repo ships full-precision BF16 weights, so no quantization noise is introduced here; if you use the APEX GGUFs, more aggressive tiers 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`, `LagunaForCausalLM`)
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
Reasoning	Native thinking, interleaved with tool use (`enable_thinking`)
This repo	BF16 safetensors, 46 shards (~219 GB); GGUF → APEX-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's in this repo

model-00001-of-00046.safetensors … model-00046-of-00046.safetensors — BF16 full weights (46 shards, ~219 GB), stream-merged from poolside/Laguna-S-2.1 + Trial 16 LoRA + router bake-in.
config.json / configuration_laguna.py / generation_config.json — laguna architecture; requires trust_remote_code=True. generation_config.json presets enable_thinking=true, poolside_v1 tool/reasoning parsers, and a DFlash speculative-decoding config.
chat_template.jinja — thinking-restored template (enable_thinking | default(true)).
MERGE_INFO.json — machine-readable provenance (trial, KL, refusals, router rows scaled, LoRA modules merged, audit count).
UNCENSORED_README.md — condensed uncensored notes.
LICENSE.md — OpenMDW-1.1.

The APEX-GGUF sibling repo is the quantization output of this exact merge: laguna-s-2.1.imatrix was calibrated on it, and token embedding / output stay BF16 there as well.

🚀 Usage

Transformers (trust remote code)

from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained( "SC117/Laguna-S-2.1-Uncensored", trust_remote_code=True, torch_dtype="bfloat16", device_map="auto", ) tokenizer = AutoTokenizer.from_pretrained( "SC117/Laguna-S-2.1-Uncensored", trust_remote_code=True )

messages = [{"role": "user", "content": "Implement a small HTTP server in Python."}] inputs = tokenizer.apply_chat_template( messages, add_generation_prompt=True, return_tensors="pt" ).to(model.device) out = model.generate(**inputs, max_new_tokens=1024) print(tokenizer.decode(out[0], skip_special_tokens=True))

vLLM

vllm serve SC117/Laguna-S-2.1-Uncensored \ --tensor-parallel-size 4 \ --trust-remote-code \ --tool-call-parser poolside_v1 \ --reasoning-parser poolside_v1 \ --enable-auto-tool-choice \ --served-model-name laguna-uncensored \ --default-chat-template-kwargs '{"enable_thinking": true}'

SGLang

python -m sglang.launch_server \ --model-path SC117/Laguna-S-2.1-Uncensored \ --tp-size 4 \ --trust-remote-code \ --reasoning-parser poolside_v1 \ --tool-call-parser poolside_v1

llama.cpp / GGUF users: use the APEX-GGUF repo with poolside's llama.cpp fork, branch laguna (must recognize general.architecture = laguna).
Speculative decoding: pair with the DFlash draft model (dflash method, 15 draft tokens — already preset in generation_config.json).
Thinking is controlled per request via the chat template: chat_template_kwargs={"enable_thinking": False} to disable.

🎛️ 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 → this repo (Laguna-S-2.1-Uncensored, 46 shards)
GGUF branch: poolside llama.cpp → BF16 GGUF + imatrix → APEX tensor-type-file + imatrix → I-Quality / I-Balanced / I-Compact (see APEX-GGUF)

Disclaimer

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

SC117/Laguna-S-2.1-Uncensored

Laguna-S-2.1-Uncensored

Links

Disclaimer

SC117/
Laguna-S-2.1-Uncensored