Welcome to the ultimate guide for using and building on the ReplitLM model family. In this user-friendly article, we’ll walk you through the essentials, including how to use the models, training, and troubleshooting common issues. Let’s dive in!
Table of Contents
Models
| Model | Checkpoint | Vocabulary | Code |
|---|---|---|---|
| replit-code-v1-3b | Download Link | Download | Repo |
| replit-code-v1_5-3b | (Coming Soon) | (Coming Soon) | Coming Soon |
Releases
As of May 2, 2023, you can find the replit-code-v1-3b model released for use.
Usage
Hosted Demo
Experience the power of the replit-code-v1-3b model with our GPU-powered demo available here.
Using with Hugging Face Transformers
All released Replit models are available on Hugging Face under the Replit organization page, allowing you to seamlessly integrate the models into your projects. When using the Hugging Face Transformers library, make sure to set the clean_up_tokenization_spaces=False when decoding with the tokenizer. Additionally, refer to the model README for post-processing instructions.
Training and Fine-tuning
Training with LLM Foundry
Preparing to train using LLM Foundry can be likened to setting up your kitchen for a big cooking adventure. You need all the right ingredients (data), utensils (training tools), and recipes (configuration files) to get started.
- Step 1: Install LLM Foundry
Follow the LLM Foundry README for installation, ensuring to set up prerequisites.
- Step 2: Convert and Save Your Dataset
Your dataset needs to be formatted into the Mosaic StreamingDataset. This is essential for efficient training.
- Step 3: Define a YAML Configuration
Define your training setup with a `.yaml` file detailing the model, datasets, and other parameters. Think of this step as writing down your recipe.
- Step 4: Execute Training
Finally, you’ll run the training script using the command
composer train train.py configuration_yaml_path extra_args.
Instruction Tuning
Instruction tuning allows you to adapt the ReplitLM models to your specific needs. It’s like training a dog to perform tricks based on your commands—you’re molding the base model to understand and respond to custom inputs.
Alpaca-style Instruct Tuning with Hugging Face Transformers
For instruction tuning in an Alpaca-style, you can use existing datasets like the Stanford Alpaca and modify the models according to your requirements.
Instruct Tuning with LLM Foundry
To perform instruction tuning with LLM Foundry, follow parallel steps as before, adapting your dataset and formatting. Ensure to install any necessary dependencies along the way.
FAQs
- What dataset was this trained on? – Stack Dedup.
- How many GPUs do I need to train a LLM? – Consult the LLM Foundry Documentation.
- What languages was the model trained on? – The training mixture includes over 20 different languages.
Troubleshooting Ideas and Common Issues
If you encounter common pitfalls while using the ReplitLM, consider these suggestions:
- Ensure that all dependencies are correctly installed as per the requirements.
- Double-check the formatting of your dataset, ensuring it aligns with the specified structure.
- Review your YAML configuration for any syntax errors or misconfigurations.
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