Welcome to the world of WizardCoder, an innovative model designed to enhance the capabilities of code generation through advanced language modeling. In this article, we’ll explore how to get started with WizardCoder, including fine-tuning and inference processes, while ensuring you have a user-friendly experience. Let’s dive right in!

Getting Started with WizardCoder

WizardCoder is built on the Evol-Instruct method adapted for coding tasks. Here’s how you can utilize it:

Online Demo

We invite you to try our latest models. If you find any links not functioning, don’t hesitate to switch to another. Feel free to challenge our models with real-world coding problems!

Fine-tuning WizardCoder

Fine-tuning is an essential step to make the model more effective. WizardCoder utilizes modified training scripts for optimal performance. Here’s how to do it:

Steps to Fine-tune:

• Clone the repository from Llama-X.
• Install the required environment, ensuring compatibility with `deepspeed==0.9.2` and `transformers==4.29.2`.
• Replace the training script with train_wizardcoder.py from our repo.
• Log in to Hugging Face using the command: huggingface-cli login.
• Run the training command:

bash deepspeed train_wizardcoder.py \
    --model_name_or_path bigcodestarcoder \
    --data_path yourpathtocode_instruction_data.json \
    --output_dir yourpathtockpt \
    --num_train_epochs 3 \
    --model_max_length 2048 \
    --per_device_train_batch_size 16 \
    --per_device_eval_batch_size 1 \
    --gradient_accumulation_steps 4 \
    --evaluation_strategy no \
    --save_strategy steps \
    --save_steps 50 \
    --save_total_limit 2 \
    --learning_rate 2e-5 \
    --warmup_steps 30 \
    --logging_steps 2 \
    --lr_scheduler_type cosine \
    --report_to tensorboard \
    --gradient_checkpointing True \
    --deepspeed configsdeepspeed_config.json \
    --fp16 True

Inference with WizardCoder

Once the model is fine-tuned, it’s time to generate responses. The decoding script will read an input file and provide relevant outputs:

• Prepare your input JSONL file with the format:

idx: 11, Instruction: Write a Python code to count 1 to 10.
idx: 12, Instruction: Write a Java code to sum 1 to 10.

• Run the decoding command:

python src/inference_wizardcoder.py \
    --base_model yourpathtockpt \
    --input_data_path yourpathtoinputdata.jsonl \
    --output_data_path yourpathtooutputresult.jsonl

Troubleshooting WizardCoder

While using WizardCoder, you might run into some hiccups. Here are some troubleshooting tips:

• Ensure all dependencies are correctly installed. Verify versions for deepspeed and transformers.
• If the model isn’t generating outputs, check your input file for formatting errors.
• For issues relating to model performance, consider modifying hyperparameters such as learning rate and batch size.
• If you still face difficulty, feel free to reach out on our issue discussion page on GitHub.

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, get started with WizardCoder and experience the power of generating code like never before!