CodeGemma, a powerful tool for code generation, simplifies the task of creating code snippets or even entire functions from natural language prompts. In this guide, we will dive into how to access and utilize CodeGemma effectively.

Accessing CodeGemma on Hugging Face

To get started with CodeGemma, you need to agree to Google’s usage license. Here’s how you can do it:

• Make sure you’re logged into your Hugging Face account.
• Click the button below to acknowledge the license:
• Acknowledge license

Sample Code Usage

Here’s a simple example of using CodeGemma to generate a Python function that calculates the nth Fibonacci number. Consider this analogy: think of the model as a skilled chef. When you give it a recipe (input text), the chef prepares a dish (code) based on your request!

from transformers import GemmaTokenizer, AutoModelForCausalLM

tokenizer = GemmaTokenizer.from_pretrained("google/codegemma-7b-it")
model = AutoModelForCausalLM.from_pretrained("google/codegemma-7b-it")

input_text = "Write me a Python function to calculate the nth fibonacci number."
input_ids = tokenizer(input_text, return_tensors="pt")
outputs = model.generate(**input_ids)

print(tokenizer.decode(outputs[0]))

Chatting with CodeGemma

If you’re looking to initiate a conversation or query about coding, CodeGemma has got you covered with its chat template. This means it can respond to your questions in a conversational manner, just like having a discussion with a knowledgeable friend!

from transformers import AutoTokenizer, AutoModelForCausalLM
import transformers
import torch

model_id = "google/codegemma-7b-it"
dtype = torch.bfloat16

tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    device_map="cuda",
    torch_dtype=dtype,
)

chat = [
    {"role": "user", "content": "Write a hello world program"},
]

prompt = tokenizer.apply_chat_template(chat, tokenize=False, add_generation_prompt=True)

Understanding Inputs and Outputs

CodeGemma excels at different tasks depending on the model variant you choose:

• Pretrained Models: These are ideal for code completion and generation tasks.
• Instruction-Tuned Model: This variant is focused on generating code from natural language and can also engage in coding conversations.

Troubleshooting Tips

If you encounter issues while using CodeGemma, here are some tips to help you out:

• Ensure that you have a stable internet connection when accessing Hugging Face.
• If the model fails to generate output, double-check your model selection and ensure you are using the correct input format.
• For more insights, updates, or to collaborate on AI development projects, stay connected with fxis.ai.
• Check the Hugging Face community forums for additional troubleshooting advice or common issues.

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.

Conclusion

Using CodeGemma is like conversing with an expert coder—providing it with natural language prompts leads to insightful code generation. With the right setup and permissions, anyone can leverage this tool to make coding tasks more manageable.