In the realm of natural language processing, fine-tuning pre-trained models is akin to refining a diamond—it brings out more clarity and brilliance. In this guide, we’ll explore how to fine-tune the CapyLake-7B-v2-laser model using the WestLake-7B-v2-Laser and the argilladistilabel-capybara-dpo-7k-binarized datasets.

Process Overview

The fine-tuning process is straightforward. Here’s how you can do it step by step:

• Realign the chat template to ChatML format.
• Complete one epoch of training.
• Set a learning rate to 0.00005.
• The training time is approximately 2 hours using one H100 GPU.
• The total cost for this training is about $8.

Code Example

Below is a Python code snippet illustrating how to implement the fine-tuning process:

from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "macadeliccc/CapyLake-7B-v2-laser"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id)

text = "Create an idea for a TV show and write a short pilot script"
inputs = tokenizer(text, return_tensors="pt")

# Adding hyperparameters to the generation call
outputs = model.generate(
    **inputs,
    max_new_tokens=4096,  # Controls the maximum length of the new tokens created
    temperature=0.7,  # Adjust for creativity (lower is less random)
    top_k=50,  # Keeps the top k tokens for sampling
    top_p=0.95,  # Uses nucleus sampling with this cumulative probability
    num_return_sequences=1,  # Number of sequences to generate
    no_repeat_ngram_size=2,  # Prevents repeating n-grams to ensure diversity
    early_stopping=True  # Stops generation when all sequences reach the EOS token
)

print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Breaking Down the Code: An Analogy

Think of the fine-tuning process like preparing a special recipe in a culinary competition:

• AutoTokenizer is like your sous-chef, getting all the ingredients (tokens) ready from the pantry (model repository).
• AutoModelForCausalLM is the master chef, ready to mix and cook (generate) the perfect dish (text).
• inputs are your raw ingredients—once they are prepped, they can be cooked.
• The generate function is where you blend everything together, tweaking the recipe’s key factors like spices (temperature) and cooking time (max_new_tokens) to create a dish (text) that impresses the judges (users).

Evaluation Metrics

After fine-tuning, it’s essential to evaluate your model against various benchmarks:

• AGIEval
• GPT4All
• TruthfulQA
• Bigbench

The scores from these evaluations will guide you on how well your model performs on different tasks.

Troubleshooting Tips

If you encounter any issues during model training or evaluation, consider the following troubleshooting steps:

• Check your dataset for inconsistencies or missing values.
• Ensure your GPU resources are functioning properly and are allocated correctly.
• Verify that all dependencies and libraries are updated to their compatible versions.
• Utilize logging to identify at which point the model encounters issues.

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

Final Thoughts

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.