Instructions to use poolside/Laguna-S-2.1-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- llama-cpp-python
How to use poolside/Laguna-S-2.1-GGUF with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="poolside/Laguna-S-2.1-GGUF", filename="laguna-s-2.1-DFlash-BF16.gguf", )
llm.create_chat_completion( messages = "No input example has been defined for this model task." )
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use poolside/Laguna-S-2.1-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 poolside/Laguna-S-2.1-GGUF:Q4_K_M # Run inference directly in the terminal: llama cli -hf poolside/Laguna-S-2.1-GGUF:Q4_K_M
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf poolside/Laguna-S-2.1-GGUF:Q4_K_M # Run inference directly in the terminal: llama cli -hf poolside/Laguna-S-2.1-GGUF:Q4_K_M
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 poolside/Laguna-S-2.1-GGUF:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf poolside/Laguna-S-2.1-GGUF:Q4_K_M
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 poolside/Laguna-S-2.1-GGUF:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf poolside/Laguna-S-2.1-GGUF:Q4_K_M
Use Docker
docker model run hf.co/poolside/Laguna-S-2.1-GGUF:Q4_K_M
- LM Studio
- Jan
- Ollama
How to use poolside/Laguna-S-2.1-GGUF with Ollama:
ollama run hf.co/poolside/Laguna-S-2.1-GGUF:Q4_K_M
- Unsloth Studio
How to use poolside/Laguna-S-2.1-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 poolside/Laguna-S-2.1-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 poolside/Laguna-S-2.1-GGUF to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for poolside/Laguna-S-2.1-GGUF to start chatting
- Pi
How to use poolside/Laguna-S-2.1-GGUF with Pi:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf poolside/Laguna-S-2.1-GGUF:Q4_K_M
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": "poolside/Laguna-S-2.1-GGUF:Q4_K_M" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Hermes Agent new
How to use poolside/Laguna-S-2.1-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 poolside/Laguna-S-2.1-GGUF:Q4_K_M
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 poolside/Laguna-S-2.1-GGUF:Q4_K_M
Run Hermes
hermes
- Atomic Chat new
- OpenClaw new
How to use poolside/Laguna-S-2.1-GGUF with OpenClaw:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf poolside/Laguna-S-2.1-GGUF:Q4_K_M
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 "poolside/Laguna-S-2.1-GGUF:Q4_K_M" \ --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 poolside/Laguna-S-2.1-GGUF with Docker Model Runner:
docker model run hf.co/poolside/Laguna-S-2.1-GGUF:Q4_K_M
- Lemonade
How to use poolside/Laguna-S-2.1-GGUF with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull poolside/Laguna-S-2.1-GGUF:Q4_K_M
Run and chat with the model
lemonade run user.Laguna-S-2.1-GGUF-Q4_K_M
List all available models
lemonade list
Inconsistent reasoning trigger on llama-server
Appreciate the wonderful release first and foremost!
I'm running the Q4_K_M through the shared custom fork's llama-server and I notice the model's reasoning trigger to be inconsistent. It triggers for select prompts like "Prove sqrt(2) is irrational", "What is 17*23? Think it through.", etc.., but completely skips it for the rest of my prompts (complex ones) even when I nudge it to think/reason before answering.
My launch command:
build/bin/llama-server -m ../Models/GGUFs/laguna-s-2.1-Q4_K_M.gguf -c 0 --fit on -fa on --port 1234 --jinja --no-mmap -b 8192 -ub 8192 --temp 0.7 --top-p 0.95 --chat-template-kwargs '{"enable_thinking": true}'
Try omitting your sampling parameters. The ones you have there a recommended for rope scaling at 1M token context. I haven't had any issue with reasoning triggering
--jinja
--flash-attn on
--parallel 1
--no-warmup
-m /models/Laguna-S-2.1-GGUF-poolside/laguna-s-2.1-Q4_K_M.gguf
--model-draft /models/Laguna-S-2.1-GGUF-poolside/laguna-s-2.1-DFlash-BF16.gguf
--spec-type draft-dflash
--spec-draft-n-max 15
--fit off
-c 262144
--n-gpu-layers 999
--main-gpu 0
--chat-template-kwargs '{"enable_thinking":true}'
Hi i have the same issue with llama.cpp vulkan backend. Simple question like "what is agile?" Creates a reasoning trace but a complex prompt with requirements and success criteria does not
@barnesea Removal of sampling params doesn't fix it for me either...The Q8 has the same issue as well. Surprised to hear it's working fine for you. Are you testing it directly via llama-server or via an agentic harness like OpenCode/Pi?
Edit: Facing the same issue with mainline llama.cpp post PR-merge
Hi guys! Thank you for your comments.
I'd like to ask how you're interacting with llama-server: it may be a harness issue. In any case, we've also noticed that our chat template didn't ship with preserved thinking properly enabled, which might be contributing to this. We've uploaded a new chat template to this repo, and we plan to repackage the GGUFs to contain the proper formatting later today. I'll update here once it's done.
I noticed same non-thinking replies in llama.cpp web ui, but in "normal" agent harness works ok, for ~1k messages now (10 user requests + 10 responses + 990 tool call/response), 0 transport-level/content-reasoning_content confusion/full-non-thinking problems. I suspect it may be that llama web ui sends empty system prompt, and template-default is omitted
I upgraded to the server-vulkan-b10088 image and manually forced the new chat-template-file. Still does not trigger. My front end is open webui now for testing. I also tried adding a system message, "you are a deep thinking assistant", did not help.
Hi guys! Thank you for your comments.
I'd like to ask how you're interacting with llama-server: it may be a harness issue. In any case, we've also noticed that our chat template didn't ship with preserved thinking properly enabled, which might be contributing to this. We've uploaded a new chat template to this repo, and we plan to repackage the GGUFs to contain the proper formatting later today. I'll update here once it's done.
Just tested running it via Pi agent and the model's reasoning surprisingly works there consistently. The reported issue is reproducible when interacting directly with llama-server's Web UI.