Poolside releases Laguna S 2.1, the open-weight coding model pitched as the West’s answer to DeepSeek and Qwen

Introduction

In this tutorial, you'll learn how to run and experiment with open-weight coding models like Poolside's Laguna S 2.1. These models are becoming increasingly popular because they offer powerful AI capabilities while remaining accessible to developers and researchers. We'll walk you through setting up a local environment to test such models, focusing on the practical steps needed to get started with agentic coding models.

Prerequisites

• A computer with an NVIDIA GPU (such as RTX 3090 or higher)
• Basic understanding of Python and command-line tools
• Installed NVIDIA drivers and CUDA toolkit
• Access to a Python virtual environment

Step-by-step instructions

1. Setting Up Your Environment

1.1 Install Required Dependencies

First, you'll need to install Python dependencies to support running large language models. We'll use transformers and torch libraries, which are essential for loading and running open-weight models.

pip install torch transformers accelerate

Why: These libraries provide the necessary tools to load, run, and interact with large language models on your local hardware.

1.2 Create a Virtual Environment

To avoid conflicts with other Python projects, create a dedicated virtual environment:

python -m venv laguna_env
source laguna_env/bin/activate  # On Windows: laguna_env\Scripts\activate

Why: A virtual environment isolates your project's dependencies, ensuring that you don't interfere with other Python packages on your system.

2. Loading the Model

2.1 Download the Model

Although Laguna S 2.1 is an open-weight model, it's not directly available on Hugging Face. However, we can simulate the process using a similar open-source model like meta-llama/Llama-3.2-1B for demonstration purposes.

from transformers import AutoTokenizer, AutoModelForCausalLM

model_name = "meta-llama/Llama-3.2-1B"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

Why: This step initializes the model and tokenizer, which are needed to process text inputs and generate outputs.

2.2 Prepare the Input Prompt

Next, prepare a coding prompt to test the model's agentic capabilities:

prompt = "Write a Python function that calculates the factorial of a number."
inputs = tokenizer.encode(prompt, return_tensors="pt")

Why: The input prompt tells the model what kind of code you want it to generate. Tokenization converts the text into a format the model can understand.

3. Running the Model

3.1 Generate Output

Now, let's generate the model's output:

with torch.no_grad():
    outputs = model.generate(inputs, max_length=150)
    response = tokenizer.decode(outputs[0], skip_special_tokens=True)

print(response)

Why: This step runs the model on your input prompt and decodes the output into readable text. The max_length parameter limits how long the output can be.

3.2 Optimize for Performance

To improve performance, especially on smaller GPUs, you can enable model quantization:

from transformers import AutoModelForCausalLM

model = AutoModelForCausalLM.from_pretrained(model_name, torch_dtype=torch.float16)
model = model.to("cuda")  # Move model to GPU

Why: Quantization reduces memory usage and speeds up inference by using half-precision floating-point numbers.

4. Testing Agentic Coding Capabilities

4.1 Create a Multi-Step Prompt

Try a more complex prompt that simulates agentic coding:

prompt = "\n".join([
    "You are an expert Python developer. Your task is to build a web scraper that fetches data from a given URL.",
    "1. Use requests to fetch the HTML content.",
    "2. Parse the HTML using BeautifulSoup.",
    "3. Extract all links from the page.",
    "Return the code in a single Python function."
])

inputs = tokenizer.encode(prompt, return_tensors="pt")
with torch.no_grad():
    outputs = model.generate(inputs, max_length=300)
    response = tokenizer.decode(outputs[0], skip_special_tokens=True)

print(response)

Why: This prompt tests the model's ability to understand and execute multi-step coding tasks, mimicking the agentic coding capabilities mentioned in the article.

4.2 Analyze the Output

After running the model, carefully review the generated code. Look for:

• Correct syntax
• Logical flow
• Use of appropriate libraries

Why: Evaluating the output helps you understand how well the model performs in real-world coding scenarios.

Summary

In this tutorial, you've learned how to set up a local environment for running open-weight coding models like Laguna S 2.1. You've installed the necessary tools, loaded a model, and generated code based on prompts. While this tutorial used a similar model for demonstration, the steps are directly applicable to running models like Laguna S 2.1 on compatible hardware. As these models become more accessible, they open up new possibilities for developers to integrate AI into their coding workflows.