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
| {#- Iteration on laguna_glm_thinking_v8/chat_template.jinja -#} | |
| {#- No formatting instructions -#} | |
| {{- "〈|EOS|〉" -}} | |
| {%- set enable_thinking = enable_thinking | default(true) -%} | |
| {%- set add_generation_prompt = add_generation_prompt | default(false) -%} | |
| {%- set preserve_thinking = preserve_thinking | default(false) -%} | |
| {%- set preserve_thinking = preserve_thinking | default(false) -%} | |
| {#- ───── header (system message) ───── -#} | |
| {#- A caller-supplied system message with empty content opts out of the default below, producing no <system> block — used to train without a system message. -#} | |
| {%- set system_message = "You are a helpful, conversationally-fluent assistant made by Poolside. You are here to be helpful to users through natural language conversations." -%} | |
| {%- if messages and messages[0].role == "system" -%} | |
| {%- set system_message = messages[0].content -%} | |
| {%- set messages = messages[1:] -%} | |
| {%- endif -%} | |
| {%- set has_sys = system_message and system_message.strip() -%} | |
| {%- if has_sys or tools or enable_thinking -%} | |
| {{- "<system>" -}} | |
| {%- if has_sys -%} | |
| {{- system_message.rstrip() -}} | |
| {%- if tools -%}{{- "\n\n" -}}{%- endif -%} | |
| {%- endif -%} | |
| {%- if tools -%} | |
| {{- "### Tools\n\n" -}} | |
| {{- "You may call functions to assist with the user query.\n" -}} | |
| {{- "All available function signatures are listed below:\n" -}} | |
| {{- "<available_tools>\n" -}} | |
| {%- for tool in tools -%} | |
| {{- (tool | tojson) ~ "\n" -}} | |
| {%- endfor -%} | |
| {{- "</available_tools>" -}} | |
| {%- endif -%} | |
| {{- "</system>\n" -}} | |
| {%- endif -%} | |
| {#- ───── main loop ───── -#} | |
| {%- for message in messages -%} | |
| {%- set content = message.content if message.content is string else "" -%} | |
| {%- if message.role == "user" -%} | |
| {{- "<user>" + content + "</user>\n" -}} | |
| {%- elif message.role == "assistant" -%} | |
| {%- generation -%} | |
| {{- "<assistant>" -}} | |
| {#- Extract reasoning content from message.reasoning (vLLM field name) or message.reasoning_content -#} | |
| {%- set reasoning_content = '' -%} | |
| {%- if message.reasoning is string -%} | |
| {%- set reasoning_content = message.reasoning -%} | |
| {%- elif message.reasoning_content is string -%} | |
| {%- set reasoning_content = message.reasoning_content -%} | |
| {%- endif -%} | |
| {#- Display reasoning content for all messages if enable_thinking -#} | |
| {%- if enable_thinking or preserve_thinking -%} | |
| {{- '<think>' + reasoning_content + '</think>' -}} | |
| {%- else -%} | |
| {{- '</think>' -}} | |
| {%- endif -%} | |
| {#- Display main content (trailing newline only when no tool_calls follow) -#} | |
| {%- if content -%} | |
| {{- content -}} | |
| {%- endif -%} | |
| {%- if message.tool_calls -%} | |
| {%- for tool_call in message.tool_calls -%} | |
| {%- set function_data = tool_call.function -%} | |
| {{- '<tool_call>' + function_data.name -}} | |
| {%- set _args = function_data.arguments -%} | |
| {%- for k, v in _args.items() -%} | |
| {{- "<arg_key>" ~ k ~ "</arg_key>" -}} | |
| {{- "<arg_value>" -}}{{- v | tojson(ensure_ascii=False) if v is not string else v -}}{{- "</arg_value>" -}} | |
| {%- endfor -%} | |
| {{- "</tool_call>" -}} | |
| {%- endfor -%} | |
| {%- endif -%} | |
| {{- "</assistant>\n" -}} | |
| {%- endgeneration -%} | |
| {%- elif message.role == "tool" -%} | |
| {{- "<tool_response>" + content + "</tool_response>\n" -}} | |
| {%- elif message.role == "system" -%} | |
| {#- Render additional system messages (the first one, if any, is handled separately in the header and was sliced off above) -#} | |
| {{- "<system>" + content + "</system>\n" -}} | |
| {%- endif -%} | |
| {%- endfor -%} | |
| {#- ───── generation prompt ───── -#} | |
| {%- if add_generation_prompt -%} | |
| {{- "<assistant>" -}} | |
| {#- ───── Include reasoning mode directive ───── -#} | |
| {%- if enable_thinking -%} | |
| {{- '<think>' -}} | |
| {%- else -%} | |
| {{- '</think>' -}} | |
| {%- endif -%} | |
| {%- endif -%} |