Inference code and configs for ReplitLM model family
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This repository provides inference code and configurations for Replit's family of code-focused large language models, ReplitLM. It targets developers and researchers looking to leverage or fine-tune code generation models, offering integration with Hugging Face Transformers and MosaicML's LLM Foundry for advanced training.
How It Works
ReplitLM models are designed for code understanding and generation. The repository facilitates their use via Hugging Face Transformers, allowing direct loading and inference. For fine-tuning and further training, it strongly recommends MosaicML's LLM Foundry and Composer, which provide optimized training pipelines, state-of-the-art techniques, and PyTorch-based components for efficient model adaptation on custom datasets.
Quick Start & Requirements
replit/replit-code-v1-3b
). Use with Hugging Face Transformers library.requirements.txt
.Highlighted Details
replit-code-v1-3b
model available, with v1_5
coming soon..py
files when using Composer.Maintenance & Community
The project is actively updated by Replit. Further community interaction details (e.g., Discord/Slack) are not explicitly mentioned in the README.
Licensing & Compatibility
Limitations & Caveats
The replit-code-v1_5-3b
model is listed as "Coming Soon." A workaround is required for saving checkpoints with certain tokenizers when using LLM Foundry/Composer, indicating potential integration friction. The CC BY-SA 4.0 license for models may restrict commercial applications.
1 year ago
1 week