Welcome to your easy guide on setting up the Function Calling Fine-tuned Llama 2 Chat model! This blog will walk you through the essential steps, prompt formats, and troubleshooting tips to make your experience smooth and user-friendly.

What is Llama 2?

Llama 2 is a powerful generative text model developed by Meta, designed to excel in dialogue-heavy situations. With variations ranging from 7 billion to 70 billion parameters, it demonstrates remarkable efficiency in task-oriented communication.

Quick Server Setup

Setting up the server is a breeze with Runpod! Here are the templates you can use:

• Runpod one click TGI template
• Runpod one click vLLM template
• Runpod Affiliate Link (helps support the Trelis channel)

Understanding the Inference Scripts

The inference scripts are like the stage directions in a play, guiding the actors (functions) through their roles (tasks). These scripts let you format prompts, automate function calls, and manage responses effortlessly.

Here is a glimpse of how you can format a prompt using the provided metadata:

prompt = fB_INSTB_FUNCfunctionList.strip()E_FUNCuser_prompt.strip()E_INSTnn

Prompt Format and Setup

The prompt format is crucial for ensuring Llama 2 understands what you need. Below is the structure:

[INST] You have access to the following functions. Use them if required:[            type: function,        function:             name: get_current_weather,            description: This function gets the current weather in a given city,            parameters:                 type: object,                properties:                     city:                         type: string,                        description: The city, e.g., San Francisco                    ,                    format:                         type: string,                        enum: [celsius, fahrenheit],                        description: The temperature unit to use.                                    ,                required: [city]                        ][INST] Get the current weather in London [INST]

As you can see, this format allows you to engage with Llama 2 efficiently and effectively, akin to giving a well-structured instruction manual to a new employee.

Using the Tokenizer

You can set up the tokenizer and apply a chat template to make everything even simpler:

tokenizer = AutoTokenizer.from_pretrained('TrelisLlama-2-7b-chat-hf-function-calling-v3', trust_remote_code=True)prompt = tokenizer.apply_chat_template(prompt, tokenize=False)

Troubleshooting

If you encounter any issues during setup or function execution, here are some troubleshooting tips to consider:

• Ensure you have the correct API keys if you’re using gated models.
• Check that your input format strictly adheres to the prompt specifications outlined above.
• Verify that all required parameters are included in your function calls.
• If unexpected output occurs, fine-tune the input prompts until clarity is achieved.

For more insights, updates, or to collaborate on AI development projects, stay connected with fxis.ai.

Conclusion

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.

Now that you have the knowledge to set up and troubleshoot your Llama 2 Chat, dive in and unleash the power of function calling! Happy coding!