Instructions to use raulvidis/Laguna-S-2.1-ROCmFP4-COHERENT-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use raulvidis/Laguna-S-2.1-ROCmFP4-COHERENT-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 raulvidis/Laguna-S-2.1-ROCmFP4-COHERENT-GGUF:Q4_0_ROCMFP
# Run inference directly in the terminal:
llama cli -hf raulvidis/Laguna-S-2.1-ROCmFP4-COHERENT-GGUF:Q4_0_ROCMFP

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf raulvidis/Laguna-S-2.1-ROCmFP4-COHERENT-GGUF:Q4_0_ROCMFP
# Run inference directly in the terminal:
llama cli -hf raulvidis/Laguna-S-2.1-ROCmFP4-COHERENT-GGUF:Q4_0_ROCMFP

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 raulvidis/Laguna-S-2.1-ROCmFP4-COHERENT-GGUF:Q4_0_ROCMFP
# Run inference directly in the terminal:
./llama-cli -hf raulvidis/Laguna-S-2.1-ROCmFP4-COHERENT-GGUF:Q4_0_ROCMFP

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 raulvidis/Laguna-S-2.1-ROCmFP4-COHERENT-GGUF:Q4_0_ROCMFP
# Run inference directly in the terminal:
./build/bin/llama-cli -hf raulvidis/Laguna-S-2.1-ROCmFP4-COHERENT-GGUF:Q4_0_ROCMFP

Use Docker

docker model run hf.co/raulvidis/Laguna-S-2.1-ROCmFP4-COHERENT-GGUF:Q4_0_ROCMFP

LM Studio
Jan
Ollama
How to use raulvidis/Laguna-S-2.1-ROCmFP4-COHERENT-GGUF with Ollama:
```
ollama run hf.co/raulvidis/Laguna-S-2.1-ROCmFP4-COHERENT-GGUF:Q4_0_ROCMFP
```
Unsloth Desktop

How to use raulvidis/Laguna-S-2.1-ROCmFP4-COHERENT-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf raulvidis/Laguna-S-2.1-ROCmFP4-COHERENT-GGUF:Q4_0_ROCMFP

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": "raulvidis/Laguna-S-2.1-ROCmFP4-COHERENT-GGUF:Q4_0_ROCMFP"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use raulvidis/Laguna-S-2.1-ROCmFP4-COHERENT-GGUF with Docker Model Runner:
```
docker model run hf.co/raulvidis/Laguna-S-2.1-ROCmFP4-COHERENT-GGUF:Q4_0_ROCMFP
```

Lemonade

How to use raulvidis/Laguna-S-2.1-ROCmFP4-COHERENT-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull raulvidis/Laguna-S-2.1-ROCmFP4-COHERENT-GGUF:Q4_0_ROCMFP

Run and chat with the model

lemonade run user.Laguna-S-2.1-ROCmFP4-COHERENT-GGUF-Q4_0_ROCMFP

List all available models

lemonade list

Hermes Agent

How to use raulvidis/Laguna-S-2.1-ROCmFP4-COHERENT-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 raulvidis/Laguna-S-2.1-ROCmFP4-COHERENT-GGUF:Q4_0_ROCMFP

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 raulvidis/Laguna-S-2.1-ROCmFP4-COHERENT-GGUF:Q4_0_ROCMFP

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use raulvidis/Laguna-S-2.1-ROCmFP4-COHERENT-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf raulvidis/Laguna-S-2.1-ROCmFP4-COHERENT-GGUF:Q4_0_ROCMFP

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 "raulvidis/Laguna-S-2.1-ROCmFP4-COHERENT-GGUF:Q4_0_ROCMFP" \
  --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-S-2.1 — ROCmFP4 COHERENT (Strix Halo optimized)

AMD-optimized 4-bit quant of poolside/Laguna-S-2.1 (118B MoE, 8B active, 1M ctx) using the Q4_0_ROCMFP4_COHERENT tensor-protected format from charlie12345/ROCmFPX.

File: Laguna-S-2.1-Q4_0_ROCMFP4_COHERENT.gguf — 58.3 GiB, ~4.3 bpw effective.

Why this quant

COHERENT recipe protects agent-critical tensors (attention, embeddings, structured-output paths) at higher precision — in our testing it had the best long-context integrity of any Laguna quant we measured (correct refusals instead of confabulation on absent-information probes).
Corrected metadata: laguna.rope.scaling.yarn_attn_factor is baked to 1.0 per poolside's upstream fix ("llama.cpp derives mscale") — upstream-converted GGUFs from before 2026-07-24 carry 1.4852, which double-applies YaRN attention scaling. No --override-kv needed with this file.
Quantized directly from poolside's BF16 with poolside's official imatrix (no intermediate quant).

Requirements

⚠️ Does not load in stock llama.cpp. Requires the ROCmFPX runtime (built/tested at commit c190e435, 2026-07-23):

git clone https://github.com/charlie12345/ROCmFPX
env JOBS=16 scripts/build-strix-rocmfp4-mtp.sh   # gfx1151 / Strix Halo

Measured on AMD Ryzen AI MAX+ 395 (Radeon 8060S, Strix Halo, 128GB)

Metric	Value
Decode (tg128)	~32–35 t/s (HIP), ~37 t/s (Vulkan)
Prefill (pp512)	~405 t/s (HIP)
256k context serving	validated (HIP device; ~194k-token cold prompt in ~12 min)
Warm-turn TTFT (49k-token cached prefix)	~0.3 s prefill (`--cache-reuse 256`)
KV q8_0 decode cost	negligible (<1%)

Recommended serve command:

HSA_OVERRIDE_GFX_VERSION=11.5.1 GGML_HIP_ENABLE_UNIFIED_MEMORY=1 \
llama-server -m Laguna-S-2.1-Q4_0_ROCMFP4_COHERENT.gguf \
  -ngl 999 -fa on -dev ROCm0 -c 262144 --jinja \
  -b 2048 -ub 1024 --cache-reuse 256 \
  --temp 0.7 --top-p 0.95 --top-k 20 --min-p 0

Notes: thinking is on by default (disable per request via chat_template_kwargs: {"enable_thinking": false}); give thinking ≥32k max_tokens or it can exhaust the budget mid-reasoning. Vulkan devices failed very large (>190k-token) single fills in our testing across all runtimes — use the HIP device for extreme contexts.

Provenance & credits

Base model & imatrix & DFlash draft: poolside (Laguna S 2.1, OpenMDW 1.1 + model terms — review before use)
ROCmFP4 codebook & ROCmFPX runtime: charlie12345/caf
Quantization & benchmarking: raulvidis, 2026-07-24

License follows the base model (OpenMDW 1.1 + poolside model terms).

Downloads last month: 32

GGUF

Model size

118B params

Architecture

laguna

Hardware compatibility

4-bit

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Model tree for raulvidis/Laguna-S-2.1-ROCmFP4-COHERENT-GGUF

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