Instructions to use DreamFoundries/Laguna-S-2.1-6bit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use DreamFoundries/Laguna-S-2.1-6bit 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("DreamFoundries/Laguna-S-2.1-6bit")

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 DreamFoundries/Laguna-S-2.1-6bit with Pi:

Start the MLX server

# Install MLX LM:
uv tool install mlx-lm
# Start a local OpenAI-compatible server:
mlx_lm.server --model "DreamFoundries/Laguna-S-2.1-6bit"

Configure the model in Pi

# Install Pi:
npm install -g @earendil-works/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "mlx-lm": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "DreamFoundries/Laguna-S-2.1-6bit"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

MLX LM

How to use DreamFoundries/Laguna-S-2.1-6bit with MLX LM:

Generate or start a chat session

# Install MLX LM
uv tool install mlx-lm
# Interactive chat REPL
mlx_lm.chat --model "DreamFoundries/Laguna-S-2.1-6bit"

Run an OpenAI-compatible server

# Install MLX LM
uv tool install mlx-lm
# Start the server
mlx_lm.server --model "DreamFoundries/Laguna-S-2.1-6bit"
# Calling the OpenAI-compatible server with curl
curl -X POST "http://localhost:8000/v1/chat/completions" \
   -H "Content-Type: application/json" \
   --data '{
     "model": "DreamFoundries/Laguna-S-2.1-6bit",
     "messages": [
       {"role": "user", "content": "Hello"}
     ]
   }'

Hermes Agent

How to use DreamFoundries/Laguna-S-2.1-6bit 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 "DreamFoundries/Laguna-S-2.1-6bit"

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 DreamFoundries/Laguna-S-2.1-6bit

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use DreamFoundries/Laguna-S-2.1-6bit with OpenClaw:

Start the MLX server

# Install MLX LM:
uv tool install mlx-lm
# Start a local OpenAI-compatible server:
mlx_lm.server --model "DreamFoundries/Laguna-S-2.1-6bit"

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 "DreamFoundries/Laguna-S-2.1-6bit" \
  --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"

Laguna-S-2.1-6bit

File size: 1,452 Bytes

---
library_name: mlx
base_model: poolside/Laguna-S-2.1
license: openmdw-1.1
pipeline_tag: text-generation
tags:
- mlx
- mlx-lm
- laguna
- quantized
---

[![Open in MLXHub](https://mlxhub.app/assets/badge-open-in-mlxhub.svg)](https://mlxhub.app/open-model?repo=DreamFoundries/Laguna-S-2.1-6bit)

# Laguna-S-2.1-6bit

This repository contains an MLX conversion of [poolside/Laguna-S-2.1](https://huggingface.co/poolside/Laguna-S-2.1).

## Conversion Details

- Original model: `poolside/Laguna-S-2.1`
- Model family: Laguna S 2.1
- Model size: 118B total parameters, approximately 8B activated parameters per token
- Conversion: MLX-LM conversion using the Laguna architecture implementation
- Quantization: 6-bit affine quantization
- Quantization policy: Uniform 6-bit affine quantization
- Group size: 64
- Published MLX package size: 89.01 GiB

## Benchmarks

No comparative benchmarks are available yet. This repository does not currently provide quality, speed, memory, or benchmark comparisons against the original weights or other quantizations.

## License

This is a converted and quantized derivative of the original checkpoint. It retains the upstream [OpenMDW-1.1 license](https://huggingface.co/poolside/Laguna-S-2.1/blob/main/LICENSE.md) and applicable notices of origin.

---

[![Download MLXHub](https://mlxhub.app/assets/badge-download-mlxhub.svg)](https://apps.apple.com/app/apple-store/id6766485144?pt=121945436&ct=HuggingFace&mt=8)