Instructions to use YTan2000/Laguna-XS-2.1-TQ3_4S with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use YTan2000/Laguna-XS-2.1-TQ3_4S 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 YTan2000/Laguna-XS-2.1-TQ3_4S
# Run inference directly in the terminal:
llama cli -hf YTan2000/Laguna-XS-2.1-TQ3_4S

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf YTan2000/Laguna-XS-2.1-TQ3_4S
# Run inference directly in the terminal:
llama cli -hf YTan2000/Laguna-XS-2.1-TQ3_4S

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 YTan2000/Laguna-XS-2.1-TQ3_4S
# Run inference directly in the terminal:
./llama-cli -hf YTan2000/Laguna-XS-2.1-TQ3_4S

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 YTan2000/Laguna-XS-2.1-TQ3_4S
# Run inference directly in the terminal:
./build/bin/llama-cli -hf YTan2000/Laguna-XS-2.1-TQ3_4S

Use Docker

docker model run hf.co/YTan2000/Laguna-XS-2.1-TQ3_4S

LM Studio
Jan

vLLM

How to use YTan2000/Laguna-XS-2.1-TQ3_4S with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "YTan2000/Laguna-XS-2.1-TQ3_4S"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "YTan2000/Laguna-XS-2.1-TQ3_4S",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/YTan2000/Laguna-XS-2.1-TQ3_4S

Ollama
How to use YTan2000/Laguna-XS-2.1-TQ3_4S with Ollama:
```
ollama run hf.co/YTan2000/Laguna-XS-2.1-TQ3_4S
```

Unsloth Studio

How to use YTan2000/Laguna-XS-2.1-TQ3_4S 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 YTan2000/Laguna-XS-2.1-TQ3_4S 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 YTan2000/Laguna-XS-2.1-TQ3_4S to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for YTan2000/Laguna-XS-2.1-TQ3_4S to start chatting

How to use YTan2000/Laguna-XS-2.1-TQ3_4S with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf YTan2000/Laguna-XS-2.1-TQ3_4S

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": "YTan2000/Laguna-XS-2.1-TQ3_4S"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use YTan2000/Laguna-XS-2.1-TQ3_4S with Docker Model Runner:
```
docker model run hf.co/YTan2000/Laguna-XS-2.1-TQ3_4S
```

Lemonade

How to use YTan2000/Laguna-XS-2.1-TQ3_4S with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull YTan2000/Laguna-XS-2.1-TQ3_4S

Run and chat with the model

lemonade run user.Laguna-XS-2.1-TQ3_4S-{{QUANT_TAG}}

List all available models

lemonade list

Hermes Agent

How to use YTan2000/Laguna-XS-2.1-TQ3_4S with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf YTan2000/Laguna-XS-2.1-TQ3_4S

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 YTan2000/Laguna-XS-2.1-TQ3_4S

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use YTan2000/Laguna-XS-2.1-TQ3_4S with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf YTan2000/Laguna-XS-2.1-TQ3_4S

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 "YTan2000/Laguna-XS-2.1-TQ3_4S" \
  --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"

Laguna-XS-2.1-TQ3_4S / README.md

YTan2000

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	---
	license: openmdw-1.1
	language:
	- en
	library_name: gguf
	pipeline_tag: text-generation
	model_name: TurboLaguna-XS
	tags:
	- gguf
	- llama.cpp
	- laguna
	- poolside
	- turboquant
	- tq3_4s
	- code
	base_model:
	- poolside/Laguna-XS-2.1-GGUF
	model-index:
	- name: TurboLaguna-XS
	results: []
	---

	# TurboLaguna-XS

	Canonical artifact: `Laguna-XS-2.1-TQ3_4S`

	`TurboLaguna-XS` is the TurboQuant GGUF build of Poolside's Laguna XS 2.1 — a sigmoid-routed mixture-of-experts coding model with 256 experts per layer, a shared expert, QK-norm, and hybrid YaRN/sliding-window RoPE.

	The exact file and runtime artifact name is:

	- `Laguna-XS-2.1-TQ3_4S.gguf`

	## Required Runtime

	> This model uses the custom `TQ3_4S` tensor type. Stock `llama.cpp` builds
	> cannot load it. You must use the TurboQuant runtime fork:
	>
	> [turbo-tan/llama.cpp-tq3](https://github.com/turbo-tan/llama.cpp-tq3)
	>
	> This is a standard (non-MTP) model — no draft-MTP flags are needed.

	## Parent Model

	- Upstream parent: [poolside/Laguna-XS-2.1-GGUF](https://huggingface.co/poolside/Laguna-XS-2.1-GGUF)
	- Source quant: `Laguna-XS-2.1-BF16.gguf` (63.8 GB, 16.01 bpw)
	- Format conversion and TurboQuant packaging: [turbo-tan/llama.cpp-tq3](https://github.com/turbo-tan/llama.cpp-tq3)

	## Files

	\| File \| Size \| Notes \|
	\|---\|---\|---\|
	\| `Laguna-XS-2.1-TQ3_4S.gguf` \| 16 GB (4.05 bpw) \| Main model — 678 tensors, 40 layers × 256 routed experts \|
	\| `thumbnail.png` \| — \| Model card image \|
	\| `benchmark.png` \| — \| Benchmark summary \|

	## Quantization Recipe

	Quantized from the official BF16 GGUF using the standard TQ3_4S recipe:

	```bash
	./build/bin/llama-quantize --allow-requantize \
	--output-tensor-type q6_K \
	--token-embedding-type q6_K \
	Laguna-XS-2.1-BF16.gguf \
	Laguna-XS-2.1-TQ3_4S.gguf \
	TQ3_4S
	```

	Tensor policy:
	- Routed experts, attention projections, shared experts → `tq3_4s` (4.0 bpw)
	- Token embeddings, output head → `q6_K`
	- Norms, gates, biases → `f32` (untouched)

	Result: 63.8 GB → 16 GB (3.98× compression), 42% smaller than Poolside's own Q4_K_M (20 GB).

	## Recommended Runtime

	```bash
	./build/bin/llama-server \
	-m Laguna-XS-2.1-TQ3_4S.gguf \
	--host 127.0.0.1 --port 8080 \
	-c 8192 -np 1 -ngl 99 -fa on \
	--reasoning off --jinja
	```

	Build note:
	- `-fa on` is the runtime flash-attention flag, not the CMake `GGML_CUDA_FA_ALL_QUANTS` build flag.

	## GPU Memory Profiles

	\| GPU memory \| Suggested context \| KV cache \| Notes \|
	\|---:\|---:\|---\|---\|
	\| 16 GiB \| `4096` \| `-ctk q4_0 -ctv tq3_0` \| Tight fit — keep context small \|
	\| 24 GiB \| `8192` to `32768` \| `-ctk q8_0 -ctv tq3_0` \| Validated desktop profile \|
	\| 128 GiB GB10 \| `65536+` \| `-ctk q4_0 -ctv tq3_0` \| Full headroom for long context \|

	## Tested Hardware

	- NVIDIA RTX 3090 24 GB — primary validation platform
	- llama.cpp-tq3 fork, branch `feat/laguna-arch` (Laguna arch from upstream [ggml-org/llama.cpp#25165](https://github.com/ggml-org/llama.cpp/pull/25165))

	## Benchmarks

	All scores: greedy decoding, reasoning off, `-ngl 99 -fa on`, RTX 3090.

	\| Benchmark \| Score \| tok/s \|
	\|---\|---\|---\|
	\| HumanEval (base) \| 0.805 \| 196 \|
	\| HumanEval+ (extra tests) \| 0.762 \| 196 \|
	\| MBPP (base) \| 0.833 \| 199 \|
	\| MBPP+ (extra tests) \| 0.720 \| 199 \|
	\| Hard86 (20 tasks / 86 assertions) \| 64.0% (55/86) \| 202 \|
	\| BenchLoop coding \| 100.0 (12/12) \| — \|
	\| BenchLoop overall \| 73.7 \| — \|
	\| BenchLoop speed \| 96.4 (9/9) \| — \|

	### Comparison (all TQ3_4S, same RTX 3090)

	\| Model \| HE+ \| MBPP+ \| Hard86 \| Coding \| tok/s \| Size \|
	\|---\|---\|---\|---\|---\|---\|---\|
	\| Laguna XS 2.1 \| 0.762 \| 0.720 \| 64.0% \| 100.0 \| 196 \| 16 GB \|
	\| Qwen3.5 9B \| 0.671 \| 0.563 \| 44.2% \| 79.2 \| 134 \| 4.5 GB \|
	\| Qwen3.6 27B MTP \| 0.927 \| 0.878 \| — \| 100.0 \| 42–54 \| 12.9 GB \|

	Laguna XS is a coding specialist: perfect BenchLoop coding (same as the 27B), +19.8pp Hard86 over the 9B, at 3.6–4.6× the 27B's decode speed.

	## Validation

	```
	llama-simple-chat coherence smoke: PASS
	llama-server --reasoning off strict smoke: PASS (content = "ok")
	llama-bench pp2048: 745 tok/s
	llama-bench tg128: 196 tok/s
	evalplus HE/HE+/MBPP/MBPP+: scored (see above)
	hard86: 55/86
	benchloop v0.2.3: overall 73.7
	```

	## License

	- Parent model: [OpenMDW-1.1](https://huggingface.co/poolside/Laguna-XS-2.1) (Poolside)
	- Runtime: [turbo-tan/llama.cpp-tq3](https://github.com/turbo-tan/llama.cpp-tq3) (MIT)