New Chameleon AI Model: Chameleon-Code_Explation_Gemma29b-v2

I am excited to introduce my new Chameleon AI model – Chameleon-Code_Explation_Gemma29b-v2!

This model has been specifically developed to understand and explain the classes of the Chameleon CMS system and is optimized for efficient inference using 4-bit quantization. This makes the use of the model even more resource-efficient and faster.

What is the Chameleon-Code_Explation_Gemma29b-v2 Model?

Chameleon-Code_Explation_Gemma29b-v2 is a fine-tuned version of the Unsloth Gemma model and is based on a transformer-based language model. It has been trained to explain the structure and components of the Chameleon CMS system. The Chameleon CMS is a combination of shop software and content management system (CMS) that allows for flexible management of web content.

The model is specifically designed to provide non-technical explanations of the CMS components, enabling developers and users to understand the system faster and more easily.

Key Facts:

Base Model: Google Gemma 2 9B
Library: PEFT (Parameter-Efficient Fine-Tuning)
Language: English
License: Apache 2.0
Developer: kzorluoglu

Using the Model

The Chameleon-Code_Explation_Gemma29b-v2 is specifically trained to answer queries like this:Explain the XYZ class. I don’t want to see code, I want only the explanation.

With this type of input, the model can provide detailed yet easy-to-understand explanations of the classes in the Chameleon CMS without diving into the source code.

Here is an example of how to use the model:

from transformers import AutoModel, AutoTokenizer

model = AutoModel.from_pretrained("kzorluoglu/Chameleon-Code_Explation_Gemma29b-v2")
tokenizer = AutoTokenizer.from_pretrained("kzorluoglu/Chameleon-Code_Explation_Gemma29b-v2")

print("Chatbot is ready!")
print("Type 'exit' to end the chat.")
print("Ask like this for a good answer:")
print("Explain the XYZ class. I don't want to see code, I want only the explanation.")

while True:
    question = input("You: ")
    if question.lower() == 'exit':
        print("Ending the chat. Goodbye!")
        break

    instruction = f"{question}"
    inputs = tokenizer([instruction], return_tensors="pt").to("cuda")
    outputs = model.generate(**inputs, max_new_tokens=256, use_cache=True)
    generated_text = tokenizer.batch_decode(outputs, skip_special_tokens=True)
    print(f"AI: {generated_text[0]}")

Available Classes

The model is trained to explain a wide range of classes in the Chameleon CMS system. Some of the available classes are:

MTFeedbackErrors
AmazonDataConverter
AmazonPaymentConfigFactory
AmazonReferenceIdManager
WebServerExample
OffAmazonPaymentsService_Environments
ShopArticleCatalogConfDefaultOrder
… and many more!

A complete list of the available classes can be found in the model card on Hugging Face.

Training and Optimization

I trained the model on data from the following sources:

A mix of fp16 precision and 4-bit quantization was used to minimize memory usage and enhance computational efficiency.

Visit the model page on Hugging Face for more information or to use the model yourself.

For questions or comments, feel free to contact me.