Stable LM 2 12B is a powerful language model designed to generate coherent text across multilingual datasets. In this guide, we will walk you through using this model effectively, while ensuring it’s user-friendly and easy to follow.

Getting Started with Stable LM 2 12B

To kick things off, you’ll need to ensure you have the required software setup. The model requires a specific version of the transformers package, so let’s get that installed first:

pip install transformers==4.40.0

Basic Usage

Now that your software is ready, it’s time to generate some text! Here’s a simple code snippet to get started:

from transformers import AutoModelForCausalLM, AutoTokenizer

tokenizer = AutoTokenizer.from_pretrained("stabilityai/stablelm-2-12b")
model = AutoModelForCausalLM.from_pretrained("stabilityai/stablelm-2-12b", torch_dtype='auto')
model.cuda()

inputs = tokenizer("The weather is always wonderful", return_tensors='pt').to(model.device)
tokens = model.generate(
    inputs,
    max_new_tokens=64,
    temperature=0.70,
    top_p=0.95,
    do_sample=True,
)

print(tokenizer.decode(tokens[0], skip_special_tokens=True))

Understanding the Code: An Analogy

Imagine your code as a recipe in a cooking book. Each line tells you what ingredients to gather and what steps to follow to create a delicious dish. Here’s how our “recipe” breaks down:

• Gather Ingredients: The line from transformers import AutoModelForCausalLM, AutoTokenizer imports the necessary tools from the “transformers” kitchen to bake our model cake.
• Preparing the Model: tokenizer = AutoTokenizer.from_pretrained("stabilityai/stablelm-2-12b") is like measuring your flour; it helps the model understand input data.
• Baking: The model.generate() function popcorns our cake in the oven, leading to delectable outputs based on the original recipe (input text).

Running the Model with Flash Attention 2

If you want to enhance your model’s performance, consider utilizing Flash Attention 2. Use the following modified code:

from transformers import AutoModelForCausalLM, AutoTokenizer

tokenizer = AutoTokenizer.from_pretrained("stabilityai/stablelm-2-12b")
model = AutoModelForCausalLM.from_pretrained(
    "stabilityai/stablelm-2-12b",
    torch_dtype='auto',
    attn_implementation='flash_attention_2',
)
model.cuda()

inputs = tokenizer("The weather is always wonderful", return_tensors='pt').to(model.device)
tokens = model.generate(
    inputs,
    max_new_tokens=64,
    temperature=0.70,
    top_p=0.95,
    do_sample=True,
)

print(tokenizer.decode(tokens[0], skip_special_tokens=True))

Troubleshooting Tips

Even with the best recipes, sometimes things don’t turn out as expected. Here are a few troubleshooting ideas to help you navigate common issues:

• Error in Installation: Make sure your environment is set up properly. Verify that you are using transformers==4.40.0 by running pip list in your command line.
• CUDA Issues: If you encounter CUDA errors, ensure your GPU drivers and CUDA toolkit are up to date. Also, verify that the model is on the correct device using model.to('cuda').

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Final Thoughts

At fxis.ai, we believe that such advancements are crucial for the future of AI, as they enable more comprehensive and effective solutions. Our team is continually exploring new methodologies to push the envelope in artificial intelligence, ensuring that our clients benefit from the latest technological innovations.