Instructions to use mlx-community/Laguna-S-2.1-oQ5e with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use mlx-community/Laguna-S-2.1-oQ5e with MLX:

# Make sure mlx-lm is installed
# pip install --upgrade mlx-lm

# Generate text with mlx-lm
from mlx_lm import load, generate

model, tokenizer = load("mlx-community/Laguna-S-2.1-oQ5e")

prompt = "Write a story about Einstein"
messages = [{"role": "user", "content": prompt}]
prompt = tokenizer.apply_chat_template(
    messages, add_generation_prompt=True
)

text = generate(model, tokenizer, prompt=prompt, verbose=True)

Notebooks
Google Colab
Kaggle
Local Apps Settings
LM Studio

How to use mlx-community/Laguna-S-2.1-oQ5e with Pi:

Start the MLX server

# Install MLX LM:
uv tool install mlx-lm
# Start a local OpenAI-compatible server:
mlx_lm.server --model "mlx-community/Laguna-S-2.1-oQ5e"

Configure the model in Pi

# Install Pi:
npm install -g @mariozechner/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "mlx-lm": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "mlx-community/Laguna-S-2.1-oQ5e"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use mlx-community/Laguna-S-2.1-oQ5e with Hermes Agent:

Start the MLX server

# Install MLX LM:
uv tool install mlx-lm
# Start a local OpenAI-compatible server:
mlx_lm.server --model "mlx-community/Laguna-S-2.1-oQ5e"

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 mlx-community/Laguna-S-2.1-oQ5e

Run Hermes

hermes

OpenClaw new

How to use mlx-community/Laguna-S-2.1-oQ5e with OpenClaw:

Start the MLX server

# Install MLX LM:
uv tool install mlx-lm
# Start a local OpenAI-compatible server:
mlx_lm.server --model "mlx-community/Laguna-S-2.1-oQ5e"

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 "mlx-community/Laguna-S-2.1-oQ5e" \
  --custom-provider-id mlx-lm \
  --custom-compatibility openai \
  --custom-text-input \
  --accept-risk \
  --skip-health

Run OpenClaw

openclaw agent --local --agent main --message "Hello from Hugging Face"

MLX LM

How to use mlx-community/Laguna-S-2.1-oQ5e with MLX LM:

Generate or start a chat session

# Install MLX LM
uv tool install mlx-lm
# Interactive chat REPL
mlx_lm.chat --model "mlx-community/Laguna-S-2.1-oQ5e"

Run an OpenAI-compatible server

# Install MLX LM
uv tool install mlx-lm
# Start the server
mlx_lm.server --model "mlx-community/Laguna-S-2.1-oQ5e"
# Calling the OpenAI-compatible server with curl
curl -X POST "http://localhost:8000/v1/chat/completions" \
   -H "Content-Type: application/json" \
   --data '{
     "model": "mlx-community/Laguna-S-2.1-oQ5e",
     "messages": [
       {"role": "user", "content": "Hello"}
     ]
   }'

gabfssilva commited on 8 days ago

Commit

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@@ -108,6 +108,12 @@ around 2.5 points, so oQ4e through oQ6e aren't separated by this run.
 | [Laguna-S-2.1-oQ6e](https://huggingface.co/mlx-community/Laguna-S-2.1-oQ6e) | 92 GB | 6.27 | 53.0 → 32.9 | 0.763 | 0.873 | 0.777 |
 | [Laguna S 2.1 (API, bf16)](https://openrouter.ai/poolside/laguna-s-2.1) | — | 16 | — | 0.773 | 0.880 | 0.810 |
 ## Usage
 ```bash

 | [Laguna-S-2.1-oQ6e](https://huggingface.co/mlx-community/Laguna-S-2.1-oQ6e) | 92 GB | 6.27 | 53.0 → 32.9 | 0.763 | 0.873 | 0.777 |
 | [Laguna S 2.1 (API, bf16)](https://openrouter.ai/poolside/laguna-s-2.1) | — | 16 | — | 0.773 | 0.880 | 0.810 |
+Treat this as a rough sighting, not a verdict. Three benchmarks at n=300 cover a narrow slice of what
+the model does — no long-context work, no agentic loops, no real code — and at this sample size most
+of the ladder above 3.6 bpw sits inside the error bars. I ran them to size the drop between levels,
+not to rank the variants against each other. Test the one you're considering on your own workload
+before trusting any of it.
 ## Usage
 ```bash