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
Tool-Calling Error Report
Model info: Q4_K_M
Settings: llama-server -m laguna-s-2.1-Q4_K_M.gguf --jinja --chat-template-file chat_template.jinja --port 8000 -fa on
One-line summary
The model invoked edit without the required newText argument and mixed tool-markup residue into the payload, so validation failed and no code change was applied.
Problem
• Coding-agent evaluation via local llama-server (OpenAI-compatible API)
• Agent attempted a file edit via the edit tool, but tool arguments failed schema validation
• No source change was applied (empty diff)
• Not an eval-server / network / tool-runtime outage — model tool-call generation quality issue
───
Observed outputs
- Invalid tool call from the model
edits[0] contained only oldText; newText was missing. Corrupted markup leaked into the argument string:
path: /testbed/django/db/migrations/serializer.py
edits: [
{
"oldText": "class EnumSerializer(BaseSerializer): def serialize(self): ..."
// newText missing
// pollution at end of oldText:
// ...importsnewText ]...
}
]
Tool runtime response: Validation failed for tool "edit":
• edits.0.newText: must have required properties newTextExpected schema (as produced on successful PASS runs)
{
"path": "...",
"edits": [
{
"oldText": "...",
"newText": "..."
}
]
}
- Additional observations
• Same environment: other tasks produced valid edit(oldText, newText) and applied successfully (PASS)
• Around the failing turn: stopReason: length observed → possible truncation mid tool-call generation
• On some tasks, almost no edit tool calls; long reasoning only, then timeout
───
Suspected root cause (model-side)
- Broken tool-call argument generation
• Missing required field newText
• Tool/XML leftovers (, , etc.) leaked into arguments - Unstable structured output
• Multi-field tool args not completed as valid schema-compliant JSON - Possible max-token / length stop
• Generation cut off during tool call → missing fields + corrupted payload
Ruled out: eval server failure, tool executor bug, API endpoint outage
(Valid tool calls succeed in the same session with the same tool definitions.)