Instructions to use jcbtc/Laguna-S-2.1-Chadrock-ROCmFP4-StrixKVSpine-V4-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use jcbtc/Laguna-S-2.1-Chadrock-ROCmFP4-StrixKVSpine-V4-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="jcbtc/Laguna-S-2.1-Chadrock-ROCmFP4-StrixKVSpine-V4-GGUF",
	filename="laguna-s-2.1-ROCmFP4-StrixKVSpine-v4.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 jcbtc/Laguna-S-2.1-Chadrock-ROCmFP4-StrixKVSpine-V4-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 jcbtc/Laguna-S-2.1-Chadrock-ROCmFP4-StrixKVSpine-V4-GGUF
# Run inference directly in the terminal:
llama cli -hf jcbtc/Laguna-S-2.1-Chadrock-ROCmFP4-StrixKVSpine-V4-GGUF

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf jcbtc/Laguna-S-2.1-Chadrock-ROCmFP4-StrixKVSpine-V4-GGUF
# Run inference directly in the terminal:
llama cli -hf jcbtc/Laguna-S-2.1-Chadrock-ROCmFP4-StrixKVSpine-V4-GGUF

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 jcbtc/Laguna-S-2.1-Chadrock-ROCmFP4-StrixKVSpine-V4-GGUF
# Run inference directly in the terminal:
./llama-cli -hf jcbtc/Laguna-S-2.1-Chadrock-ROCmFP4-StrixKVSpine-V4-GGUF

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 jcbtc/Laguna-S-2.1-Chadrock-ROCmFP4-StrixKVSpine-V4-GGUF
# Run inference directly in the terminal:
./build/bin/llama-cli -hf jcbtc/Laguna-S-2.1-Chadrock-ROCmFP4-StrixKVSpine-V4-GGUF

Use Docker

docker model run hf.co/jcbtc/Laguna-S-2.1-Chadrock-ROCmFP4-StrixKVSpine-V4-GGUF

LM Studio
Jan

vLLM

How to use jcbtc/Laguna-S-2.1-Chadrock-ROCmFP4-StrixKVSpine-V4-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "jcbtc/Laguna-S-2.1-Chadrock-ROCmFP4-StrixKVSpine-V4-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": "jcbtc/Laguna-S-2.1-Chadrock-ROCmFP4-StrixKVSpine-V4-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/jcbtc/Laguna-S-2.1-Chadrock-ROCmFP4-StrixKVSpine-V4-GGUF

Ollama
How to use jcbtc/Laguna-S-2.1-Chadrock-ROCmFP4-StrixKVSpine-V4-GGUF with Ollama:
```
ollama run hf.co/jcbtc/Laguna-S-2.1-Chadrock-ROCmFP4-StrixKVSpine-V4-GGUF
```

Unsloth Studio

How to use jcbtc/Laguna-S-2.1-Chadrock-ROCmFP4-StrixKVSpine-V4-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 jcbtc/Laguna-S-2.1-Chadrock-ROCmFP4-StrixKVSpine-V4-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 jcbtc/Laguna-S-2.1-Chadrock-ROCmFP4-StrixKVSpine-V4-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for jcbtc/Laguna-S-2.1-Chadrock-ROCmFP4-StrixKVSpine-V4-GGUF to start chatting

How to use jcbtc/Laguna-S-2.1-Chadrock-ROCmFP4-StrixKVSpine-V4-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf jcbtc/Laguna-S-2.1-Chadrock-ROCmFP4-StrixKVSpine-V4-GGUF

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": "jcbtc/Laguna-S-2.1-Chadrock-ROCmFP4-StrixKVSpine-V4-GGUF"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use jcbtc/Laguna-S-2.1-Chadrock-ROCmFP4-StrixKVSpine-V4-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 jcbtc/Laguna-S-2.1-Chadrock-ROCmFP4-StrixKVSpine-V4-GGUF

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 jcbtc/Laguna-S-2.1-Chadrock-ROCmFP4-StrixKVSpine-V4-GGUF

Run Hermes

hermes

Atomic Chat new

OpenClaw new

How to use jcbtc/Laguna-S-2.1-Chadrock-ROCmFP4-StrixKVSpine-V4-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf jcbtc/Laguna-S-2.1-Chadrock-ROCmFP4-StrixKVSpine-V4-GGUF

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 "jcbtc/Laguna-S-2.1-Chadrock-ROCmFP4-StrixKVSpine-V4-GGUF" \
  --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 jcbtc/Laguna-S-2.1-Chadrock-ROCmFP4-StrixKVSpine-V4-GGUF with Docker Model Runner:
```
docker model run hf.co/jcbtc/Laguna-S-2.1-Chadrock-ROCmFP4-StrixKVSpine-V4-GGUF
```

Lemonade

How to use jcbtc/Laguna-S-2.1-Chadrock-ROCmFP4-StrixKVSpine-V4-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull jcbtc/Laguna-S-2.1-Chadrock-ROCmFP4-StrixKVSpine-V4-GGUF

Run and chat with the model

lemonade run user.Laguna-S-2.1-Chadrock-ROCmFP4-StrixKVSpine-V4-GGUF-{{QUANT_TAG}}

List all available models

lemonade list

Laguna S 2.1 118B Chadrock ROCmFP4 for AMD Strix Halo

Laguna S 2.1 118B Chadrock ROCmFP4 StrixKVSpine V4 — Runtime V2

An AMD-optimized, quality-protected ROCmFP4 quant of Poolside Laguna S 2.1, built for local agentic coding on Ryzen AI Max+ 395 / Radeon 8060S Strix Halo.

This V4 recipe fits a 118B-total-parameter, approximately 8B-active model into a 60.945 GiB GGUF at 4.453 effective BPW, while retaining a tested 131,072-token V2 safe context. It is 12.95% smaller than Poolside's official Q4_K_M GGUF and reached 35.62 tokens/second during the complete 128K V2 promotion gate.

This GGUF uses ROCmFP4 tensor types and Laguna architecture support. It is built for the Laguna-enabled Ciru ROCmFPX Runtime V2 at commit 090e317b4e2f998a9470faeb076cf841ba72b739. It does not load in stock upstream llama.cpp.

Runtime V2 changes the Vulkan runtime and safe serving defaults. The V4 GGUF weights are unchanged from the first release; existing users do not need to download the 60.945 GiB model again.

At a glance

Property	Value
Base model	Poolside Laguna S 2.1
Architecture	118B total / approximately 8B active MoE
Artifact	`laguna-s-2.1-ROCmFP4-StrixKVSpine-v4.gguf`
File size	65,438,991,968 bytes / 60.945 GiB
Effective quantization density	4.453 BPW
Runtime release	V2
Validated V2 serving context	131,072 tokens
Model context capacity	262,144 tokens; V2 256K lane is experimental
Complete 128K gate	195.70 PP / 35.62 TG tok/s
Tested generation speed	35.62 tok/s during the 128K gate
Tested mixed speed	82.953 tok/s, PG512 + TG256
Primary target	AMD Ryzen AI Max+ 395 / Radeon 8060S
KV cache in tested profile	F16 K / F16 V
Default reasoning mode	Off

Runtime V2 patch notes

The first runtime release could lose the Vulkan device during a very deep Flash Attention prefill on RADV/Strix Halo. Lowering the graph-node submission ceiling was not enough: matched 100-node and 10-node controls both reached an AMD compute-ring timeout after approximately 77–78 minutes.

V2 fixes the operator-level problem by splitting a large Flash Attention X grid into shorter Vulkan dispatch commands while preserving global workgroup IDs and output offsets.

Serving behavior	First release	Runtime V2
Default context	262,144	131,072 validated safe lane
Ubatch	512	512
Graph nodes per submit	100	10
FA workgroups per dispatch	Unbounded	4
Submission sizing	Tensor-byte heuristic	FLOP-aware heuristic
DeviceLost handling	Secondary exceptions possible	Sticky fatal latch and bounded teardown
Diagnostics	Manual	Automatic kernel, Vulkan, service, and devcoredump bundle
Restart behavior	Unbounded/external	Driver preflight and persisted bounded backoff
256K status	Advertised as tested	Experimental pending a full-depth gate

V2 validation on Ryzen AI Max+ 395 / Radeon 8060S with Mesa RADV 26.1.2:

Gate	Prompt processing	Generation	Result
8K, three matched passes	352.38 tok/s	35.64 tok/s	Pass
64K, one complete prefill	267.27 tok/s	35.63 tok/s	Pass
128K, one complete prefill	195.70 tok/s	35.62 tok/s	Pass

The 8K V2 row improved prompt processing by 10.57% over the matched unsplit 10-node control (318.70 tok/s), with effectively unchanged generation speed. Deterministic split and unsplit test generations were byte-identical after removing their timing lines.

Runtime V2 also adds:

GGML_VK_FA_MAX_WORKGROUPS_X_PER_DISPATCH;
GGML_VK_MAX_NODES_PER_SUBMIT;
first-failure graph node/operator context;
no new Vulkan submissions or failed fence waits after DeviceLost;
portable crash collection and a supervised launcher;
an explicit warning when selecting the experimental 256K lane.

Why this release

The goal was not simply to make Laguna smaller. StrixKVSpine V4 protects the tensors that were most sensitive in our Laguna experiments while using the fast ROCmFP4 path where it delivered the best memory and throughput return:

attention K/V, attention gates, dense block 0, shared experts, and a nine-layer expert-down spine remain protected;
attention Q/O and non-spine packed experts use the fast ROCmFP4 path;
the output tensor remains Q6_K;
F16/F16 KV cache is retained for the validated 128K V2 profile.

The resulting model is 9.066 GiB smaller than the official Poolside Q4_K_M while matching or improving that baseline on most of the retained quality checks.

Results against Poolside Q4_K_M

These are direct local comparisons against Poolside's official laguna-s-2.1-Q4_K_M.gguf, using the same benchmark tasks. Scores are reported individually rather than blended into a synthetic aggregate.

Evaluation	Chadrock ROCmFP4 V4	Poolside Q4_K_M	Difference
Tool-Eval disputed-19, 3 passes	80/114 (70.18%)	62/114 (54.39%)	+18 accepted calls / +15.79 pp
HumanEval pass@1	155/164 (94.51%)	155/164 (94.51%)	Tied
HumanEval+ pass@1	149/164 (90.85%)	147/164 (89.63%)	+2 tasks / +1.22 pp
HermesAgent-20	77/100	71/100	+6 points
BigCodeBench Hard, official	37/148 (25.00%)	39/148 (26.35%)	-2 tasks / -1.35 pp

The hero's rounded quality figure is the matched Tool-Eval result: 80 accepted calls versus 62, a 29.0% increase.

BigCodeBench follow-up

The official V4 BigCodeBench run used strict greedy decoding and scored 37/148, with seven length-capped repetition loops. Under the release sampler, all seven completed naturally and two additional tasks passed. The resulting sampler-corrected diagnostic is 39/148, tied with Q4_K_M. The table retains the official 37/148 score.

On the same 148 BigCodeBench prompts, V4 measured:

Per-token metric	Chadrock ROCmFP4 V4	Poolside Q4_K_M	V4 difference
Generation throughput	30.932 tok/s	22.201 tok/s	+39.33%
Incremental prompt throughput	199.421 tok/s	159.951 tok/s	+24.68%

The table reports per-token throughput. The greedy run generated more than twice as many completion tokens because of the seven loops, so end-to-end wall time from that run is not used as the speed headline.

Recommended serving profile

Linux support

The runtime builds natively on Linux x86-64. NixOS is the currently validated production build environment; Ubuntu 24.04 LTS and Debian 12+ are the primary documented user path. The repository also provides native dependency paths for Fedora/Rocky/AlmaLinux and Arch/Manjaro.

Linux family	Package manager	Status
Ubuntu 24.04 LTS / Debian 12+	`apt`	Primary install path
Fedora 42+ / Rocky / AlmaLinux	`dnf`	Supported build path
Arch / Manjaro / EndeavourOS	`pacman`	Supported build path
NixOS	Nix	Production build validated

Clone and pin Runtime V2 exactly:

git clone --branch agent/laguna-radv-device-lost-20260724 --depth 1 \
  https://github.com/ciru-ai/ROCmFPX.git
cd ROCmFPX
git checkout --detach 090e317b4e2f998a9470faeb076cf841ba72b739
test "$(git rev-parse HEAD)" = \
  "090e317b4e2f998a9470faeb076cf841ba72b739"

The distro-aware helper prints the native package command before making any change:

scripts/install-laguna-vulkan-deps.sh
scripts/install-laguna-vulkan-deps.sh --install

Ubuntu and Debian users can install directly:

sudo apt-get update
sudo apt-get install -y \
  git cmake ninja-build build-essential glslc \
  libvulkan-dev vulkan-tools spirv-headers mesa-vulkan-drivers

Fedora, Rocky Linux, and AlmaLinux

sudo dnf install -y \
  git cmake ninja-build gcc gcc-c++ glslc \
  vulkan-loader-devel vulkan-headers spirv-headers \
  vulkan-tools mesa-vulkan-drivers

Arch, Manjaro, and EndeavourOS

sudo pacman -S --needed \
  git cmake ninja base-devel shaderc \
  vulkan-icd-loader vulkan-headers spirv-headers \
  vulkan-tools vulkan-radeon

NixOS

nix --extra-experimental-features 'nix-command flakes' profile add \
  nixpkgs#git nixpkgs#cmake nixpkgs#ninja nixpkgs#gcc \
  nixpkgs#shaderc nixpkgs#vulkan-headers nixpkgs#vulkan-loader \
  nixpkgs#spirv-headers

Then verify Vulkan and build the pinned Release runtime:

vulkaninfo --summary
JOBS=8 BUILD_TYPE=Release scripts/build-laguna-strix-vulkan.sh

This produces a static Vulkan build with llama-server, llama-cli, llama-bench, and llama-quantize.

Run the release checks from the repository root:

build-laguna-strix-vulkan/bin/test-chat-auto-parser
build-laguna-strix-vulkan/bin/test-llama-archs

The complete Linux and V2 guide contains the Fedora, Arch, and NixOS commands.

Start Laguna with the validated 128K V2 profile

The supervised launcher is recommended on RADV. It runs the driver preflight, uses the safe V2 settings, and preserves DeviceLost evidence:

scripts/run-laguna-vulkan-supervised.sh \
  /path/to/laguna-s-2.1-ROCmFP4-StrixKVSpine-v4.gguf

The launcher applies the measured single-slot Strix Halo configuration: Vulkan0, full offload, row split, Flash Attention, 131,072 context, F16/F16 KV, batch 2048, ubatch 512, node cap 10, FA dispatch width 4, 16 threads, thinking off, and this sampler:

{
  "temperature": 1.0,
  "top_p": 1.0,
  "top_k": 20,
  "min_p": 0.0,
  "repeat_penalty": 1.0,
  "seed": 42
}

The server listens on 127.0.0.1:8080. Check it with:

curl http://127.0.0.1:8080/health
curl http://127.0.0.1:8080/v1/models

The direct runner uses the same V2 safe defaults without supervision:

scripts/run-laguna-s21-rocmfp4-v4.sh /path/to/model.gguf

The model's 256K capacity remains available only as an explicit experimental lane:

STABILITY_MODE=performance \
  scripts/run-laguna-s21-rocmfp4-v4.sh /path/to/model.gguf

That command prints a warning because 256K has not yet passed the V2 full-depth prefill, multi-turn, and cache-replay gates.

The complete production recipe is preserved in the ROCmFPX Laguna Runtime V2 guide.

Example request

After starting the compatible server:

curl http://127.0.0.1:8080/v1/chat/completions \
  -H 'Content-Type: application/json' \
  -d '{
    "model": "laguna-s21-rocmfp4-strixkvspine-v4",
    "messages": [
      {
        "role": "user",
        "content": "Refactor this Python API client to add bounded retries, typed errors, and tests."
      }
    ],
    "temperature": 1.0,
    "top_p": 1.0,
    "top_k": 20,
    "min_p": 0.0,
    "seed": 42
  }'

Artifact integrity

File	Size	SHA-256
`laguna-s-2.1-ROCmFP4-StrixKVSpine-v4.gguf`	65,438,991,968 bytes	`ea1d854a72c47ec8e72c16ea91b8ff3cd5e1620b834df175f683c86f27dc26d6`

Credits

Charlie / `charlie12345` / `caf`

Enormous thanks to Charlie (charlie12345) for the amazing ROCmFP4 codebook and the experimental ROCmFPX work that made this release possible. We could not have built this release without him.

Please support and credit his work when building on ROCmFP4 or ROCmFPX.

Poolside

Thank you to Poolside for creating and releasing the remarkable Laguna S 2.1 model and its official GGUF collection. Laguna is the foundation of everything here; this release is a quantized, hardware-targeted derivative, not a new base model.

Ciru / Chadrock

Ciru developed the Laguna-specific StrixKVSpine tensor-protection recipe, performed the calibration and quantization, built the Strix Halo Runtime V2 safe profile, and ran the retained quality, performance, and deep-context validation.

License and use

This derivative follows the base model's OpenMDW 1.1 license and Poolside's published model terms. Review the base model card, license, and acceptable-use requirements before deployment.

Benchmark results describe this exact file, runtime, hardware, and sampler configuration. Performance and memory behavior will vary across drivers, backends, hardware, context lengths, and workload shapes.

Downloads last month: 713

GGUF

Model size

118B params

Architecture

laguna

Hardware compatibility

We're not able to determine the quantization variants.

View all variants

Model tree for jcbtc/Laguna-S-2.1-Chadrock-ROCmFP4-StrixKVSpine-V4-GGUF

Base model

poolside/Laguna-S-2.1

Quantized

(70)

this model

Laguna S 2.1 118B Chadrock ROCmFP4 StrixKVSpine V4 — Runtime V2

At a glance

Runtime V2 patch notes

Why this release

Results against Poolside Q4_K_M

BigCodeBench follow-up

Recommended serving profile

Linux support

Start Laguna with the validated 128K V2 profile

Example request

Artifact integrity

Credits

Charlie / charlie12345 / caf

Poolside

Ciru / Chadrock

License and use

Model tree for jcbtc/Laguna-S-2.1-Chadrock-ROCmFP4-StrixKVSpine-V4-GGUF

Charlie / `charlie12345` / `caf`