Text Generation
GGUF
English
llama.cpp
laguna-s-2.1
poolside
agentic-coding
tool-use
rocm
rocmfp4
rocmfpx
chadrock
amd
ryzen-ai-max-395
strix-halo
long-context
imatrix
conversational
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.
- 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
- Pi
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
- 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
- Atomic Chat
Add model card
Browse files
README.md
CHANGED
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@@ -42,8 +42,8 @@ official Q4_K_M GGUF and reached **35.62 tokens/second** during the complete
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> [!IMPORTANT]
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> This GGUF uses ROCmFP4 tensor types and Laguna architecture support. It is
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> built for the Laguna-enabled
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> [Ciru ROCmFPX Runtime V3](https://github.com/ciru-ai/ROCmFPX/tree/
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> at commit `
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> stock upstream llama.cpp.
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>
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> Runtime V3 fixes Laguna tool-call grammar/parser agreement and removes a
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- aligns PEG JSON-array comma whitespace handling with the generated grammar;
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- adds a Laguna/Pi regression for an `edit` call containing an
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`array<object>` argument and source-code strings;
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- retains the V2 RADV DeviceLost safeguards and validated 128K defaults.
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Focused V3 validation on Ryzen AI Max+ 395 / Radeon 8060S with Mesa RADV
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the slot after cancellation at 82% of a 15,000-token request. A follow-up
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request returned `Paris.` with no `VK_ERROR_DEVICE_LOST`.
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### V2 Vulkan stability baseline
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The first runtime release could lose the Vulkan device during a very deep
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git clone --branch agent/laguna-s21-runtime-v3 --depth 1 \
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https://github.com/ciru-ai/ROCmFPX.git
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cd ROCmFPX
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git checkout --detach
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test "$(git rev-parse HEAD)" = \
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"
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```
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The distro-aware helper prints the native package command before making any
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```
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The
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[complete Linux and V3 guide](https://github.com/ciru-ai/ROCmFPX/blob/
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contains the Fedora, Arch, and NixOS commands.
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### Start Laguna with the validated 128K V3 profile
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The launcher applies the measured single-slot Strix Halo configuration:
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Vulkan0, full offload, row split, Flash Attention, 131,072 context, F16/F16
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KV, batch 2048, ubatch 512, node cap 10, FA dispatch width 4, 16 threads,
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thinking off, and this sampler:
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```json
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{
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scripts/run-laguna-s21-rocmfp4-v4.sh /path/to/model.gguf
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```
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The model's 256K capacity remains available only as an explicit experimental
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lane:
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full-depth prefill, multi-turn, and cache-replay gates.
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The complete production recipe is preserved in the
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[ROCmFPX Laguna Runtime V3 guide](https://github.com/ciru-ai/ROCmFPX/blob/
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## Example request
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> [!IMPORTANT]
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> This GGUF uses ROCmFP4 tensor types and Laguna architecture support. It is
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> built for the Laguna-enabled
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+
> [Ciru ROCmFPX Runtime V3](https://github.com/ciru-ai/ROCmFPX/tree/54f5fe06c74350fb8b6aec21d8749071bc195bdb)
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> at commit `54f5fe06c74350fb8b6aec21d8749071bc195bdb`. It does not load in
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> stock upstream llama.cpp.
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>
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> Runtime V3 fixes Laguna tool-call grammar/parser agreement and removes a
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- aligns PEG JSON-array comma whitespace handling with the generated grammar;
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- adds a Laguna/Pi regression for an `edit` call containing an
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`array<object>` argument and source-code strings;
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- keeps server checkpoints host-backed, preventing the on-device checkpoint
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destruction fault seen when a long hybrid/SWA context is invalidated;
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- explicitly disables context checkpoints in the validated Laguna launcher
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while leaving the normal KV and prompt caches available;
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- retains the V2 RADV DeviceLost safeguards and validated 128K defaults.
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Focused V3 validation on Ryzen AI Max+ 395 / Radeon 8060S with Mesa RADV
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the slot after cancellation at 82% of a 15,000-token request. A follow-up
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request returned `Paris.` with no `VK_ERROR_DEVICE_LOST`.
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The checkpoint repair was exercised separately with checkpoints explicitly
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re-enabled and prompt-cache RAM left at 8192 MiB. A 120,045-token request
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created 16 hybrid/SWA checkpoints; an unrelated follow-up forced `pos_next=0`
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and erased all 16, then returned `OK.`. The server remained healthy and shut
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down cleanly without a core dump. Checkpoints nevertheless remain disabled in
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the public profile until this gate is repeated across multi-turn workloads.
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### V2 Vulkan stability baseline
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The first runtime release could lose the Vulkan device during a very deep
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git clone --branch agent/laguna-s21-runtime-v3 --depth 1 \
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https://github.com/ciru-ai/ROCmFPX.git
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cd ROCmFPX
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git checkout --detach 54f5fe06c74350fb8b6aec21d8749071bc195bdb
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test "$(git rev-parse HEAD)" = \
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"54f5fe06c74350fb8b6aec21d8749071bc195bdb"
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```
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The distro-aware helper prints the native package command before making any
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```
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The
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[complete Linux and V3 guide](https://github.com/ciru-ai/ROCmFPX/blob/54f5fe06c74350fb8b6aec21d8749071bc195bdb/docs/recipes/laguna-s21-chadrock-rocmfp4-strixkvspine-v4.md)
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contains the Fedora, Arch, and NixOS commands.
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### Start Laguna with the validated 128K V3 profile
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The launcher applies the measured single-slot Strix Halo configuration:
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Vulkan0, full offload, row split, Flash Attention, 131,072 context, F16/F16
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KV, batch 2048, ubatch 512, node cap 10, FA dispatch width 4, 16 threads,
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thinking off, context checkpoints disabled, and this sampler:
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```json
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{
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scripts/run-laguna-s21-rocmfp4-v4.sh /path/to/model.gguf
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```
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Context checkpoints are separate from the normal KV cache. The launcher now
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passes `--ctx-checkpoints 0` because the hybrid/SWA checkpoint path has not yet
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completed the full repeated 128K multi-turn and cache-replay qualification.
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The runtime also moves checkpoint payloads back to host memory, matching the
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safer upstream design. Advanced diagnostic runs can opt in with
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`CTX_CHECKPOINTS=N`; this is not part of the validated public profile yet.
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The model's 256K capacity remains available only as an explicit experimental
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lane:
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full-depth prefill, multi-turn, and cache-replay gates.
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The complete production recipe is preserved in the
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+
[ROCmFPX Laguna Runtime V3 guide](https://github.com/ciru-ai/ROCmFPX/blob/54f5fe06c74350fb8b6aec21d8749071bc195bdb/docs/recipes/laguna-s21-chadrock-rocmfp4-strixkvspine-v4.md).
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## Example request
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