Are you intrigued by the capabilities of the ActionGemma model, inspired by Salesforce’s xLAM? This unique model combines multi-lingual functionalities with the power of function calling, making it an indispensable tool for developers. In this guide, we’ll break down how to effectively use this model while troubleshooting any challenges you might encounter along the way.

Model Overview

The ActionGemma model is based on the Gemma2-9B-it architecture and fine-tuned on the xLAM dataset. This model offers the capability to handle multiple languages and supports various tool functions, paving the way for enhanced programming and data interaction.

Getting Started

To get started with the ActionGemma model, you’ll need to implement a few code snippets. Here is a breakdown that illustrates how you can set up your model:

tokenizer = AutoTokenizer.from_pretrained("KishoreK/ActionGemma-9B")
tokenizer.chat_template = """{{ bos_token }}{% for message in messages %}    {% if message['role'] == 'tools' %}        {{ '\n' + message['content'] + '\n'}}    {% else %}      {{ '' + message['role'] + '\n' + message['content'] | trim + '\n' }}  {% endif %}{% endfor %}{% if add_generation_prompt %}  {{ 'assistant\n' }}{% endif %}"""

Understanding the Code Structure

Think of the code you write for the ActionGemma model like constructing a house. The tokenizer is your foundation, which ensures that teams (messages) work together correctly. Here’s how the analogy fits:

• The tokenizer is like the architect; it organizes how communications (messages) are structured.
• The chat template is the blueprint, determining how the architecture will manage conversations between the user and the tools available.
• Each message corresponds to a room in the house where unique conversations happen — the living room for user queries and a tool room for function calls.

Using the Model

After setting up your tokenizer, you can begin to utilize the model with a well-structured user query. For example, querying who the President of the United States is can invoke relevant functions. The user query can be represented like this:

user_query = "अमेरिका के राष्ट्रपति कौन है?"
tools = openai_format_tools
messages = [{
    "role": "system",
    "content": task_instruction},
    {
    "role": "user",
    "content": user_query},
    {
    "role": "tools",
    "content": json.dumps(convert_to_xlam_tool(tools))}]
print(tokenizer.decode(tokenizer.apply_chat_template(messages, add_generation_prompt=True)))

Troubleshooting

While using the ActionGemma model, you might encounter some common issues. Here are some troubleshooting tips:

• Issue: Model doesn’t return expected results.
  • Solution: Check if your user query is correctly formatted and contains all necessary parameters needed by the specified functions.
• Issue: Error messages when calling functions.
  • Solution: Ensure your tool definitions comply with the expected inputs and outputs as outlined in the model documentation.

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Next Steps

Once acquainted with the basics, delve deeper into optimizing your model for specific tasks. Remember to keep an eye on the resources available to ensure utmost effectiveness.

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.