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"

YTan2000 commited on 25 days ago

Commit

6da464b

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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)