cac-v0.1

CAC-v0.1

CAC is a large language model with 6.7B parameters specifically finetuned for code completions. CAC, although trained for code autocompletion, can also be used for other code related tasks such as:

• Generation
• Summarization
• Translation
• Question Answering
• Optimization
• Debugging
• Code Review and more.

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This is the very first version of CAC (0.1) and is still under development. For this version, we chose to go ahead with DeepSeek-Coder-6.7B as the base model.

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Model Details

• Training Data: Exclusively fine-tuned on a proprietary dataset of 1.8 billion tokens of high-quality programming problems and solutions.

• The dataset was generated manually and is internal to CodeMate.

• Training Techniques: The model was fine-tuned using Flash Attention 2.

• A sequence length of 8096 tokens was used during training.

• Multilingual Support: CAC-v0.1 is proficient in multiple programming languages, including Python, C/C++, TypeScript, Java, and more.

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Load the model with Transformers:

Make sure to install Transformers from the main git branch:

pip install git+https://github.com/huggingface/transformers.git

How to Prompt the Model:

This model accepts prompts in the Alpaca/Vicuna instruction format. For example:

### System Prompt
You are an intelligent programming assistant.

### User Message
Implement a linked list in C++

### Assistant
...

You can also use the Mistral chat template for conversations:

<s>[INST] .... [/INST] ... </s>

Load the model:

from transformers import AutoTokenizer, AutoModelForCausalLM

# Initialize the model
model_path = "codemateai/CodeMate-v0.1"
model = AutoModelForCausalLM.from_pretrained(model_path, device_map="auto")
tokenizer = AutoTokenizer.from_pretrained(model_path)

# ... generate response ...

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Limitations

This model has undergone very limited testing. CodeMate recommends additional safety testing before any real-world deployments.

For more information and updates, visit the CodeMate website.