Meditron is a medical Large Language Models (LLMs) project, housing models Meditron-7B and Meditron-70B. The models, though superior in medical reasoning tasks, are cautioned against use in medical applications without extensive testing.
The project, written in Python, is moderately active with 53 commits, 1 branch, 28 forks, 526 stars, and 12 watchers. The models, based on a causal decoder-only transformer language model, are trained on an offline dataset using the Megatron-LLM distributed training library.
The repository includes an advisory notice on the limitations of Meditron and provides detailed instructions on model usage, fine-tuning, and data preprocessing. Future plans include releasing enhanced versions of the tuned models.
There are no open issues or pull requests. The only recently closed issue (#6) was promptly addressed by the team, indicating their responsiveness. Closed pull requests include documentation updates (#5, #3, #1), new feature implementation (#4), and data preprocessing for fine-tuning (#2). Notably, PR #4 introduces a streaming data fetching option and a seed option for reproducibility but notes the streaming option's slow speed.
The project seems to prioritize documentation, and most changes were merged quickly, indicating active maintenance. No significant problems or anomalies are observed based on the provided information.
The software project currently has no open issues, hence there are no recent issues to analyze or highlight.
The only recently closed issue was #6, which was related to a problem with loading guidelines using huggingface datasets. The error was a "DatasetGenerationError" indicating an issue occurred while generating the dataset. This issue was promptly addressed by the team, with a fix involving the manual addition of feature types for each column. The user who reported the issue confirmed that the fix worked, leading to the closure of the issue. This suggests that the team is responsive and effective in addressing issues as they arise.
There are no open pull requests at the moment, which indicates that the project is not actively being worked on or all changes have been merged recently.
The closed pull requests show a variety of updates, including documentation updates (#5, #3, #1), implementation of new features (#4), and data preprocessing for fine-tuning (#2).
Notably, PR #4 introduces a streaming data fetching option and a seed option for reproducibility, which could significantly impact how users interact with the software. However, the author notes that the streaming option is slow. This might be a point of concern for future development.
PR #2 adds data preprocessing for fine-tuning, which indicates that the project might be moving towards a machine learning or data analysis direction.
The project seems to emphasize documentation, as seen in PR #5, #3, and #1. This is a good practice as it ensures that users and contributors understand how to use and contribute to the project.
Most of the changes were merged quickly, indicating that the project is actively maintained. However, without any open pull requests, it's hard to determine any ongoing issues or discussions.
Overall, there are no significant problems, major uncertainties, or worrying anomalies based on the provided pull requests.
Meditron is an open-source suite of medical Large Language Models (LLMs) developed by the EPFL LLM Team. The project includes Meditron-7B and Meditron-70B, models adapted from Llama-2 and pretrained on a curated medical corpus. The models are designed to encode medical knowledge and have shown superior performance in medical reasoning tasks. However, the authors advise against using Meditron in medical applications without extensive testing and alignment with use-cases. The models can be loaded directly from the HuggingFace model hub.
The repository is moderately active with 53 total commits, 1 branch, and 28 forks. It has gained popularity with 526 stars and 12 watchers. The project is written in Python and licensed under the Apache License 2.0. The models are based on a causal decoder-only transformer language model and are trained on an offline dataset. The training process is optimized using the Megatron-LLM distributed training library. The models are primarily English-based and generate text-only output.
The repository includes an advisory notice highlighting the limitations of Meditron. The models, while designed to encode medical knowledge, are not yet adapted to deliver this knowledge appropriately or safely within professional actionable constraints. The authors emphasize the need for extensive use-case alignment and additional testing before using Meditron in medical applications. The repository also provides detailed instructions on how to use and fine-tune the models, as well as how to download and preprocess the training data. The project's future plans include releasing enhanced versions of the tuned models.
Summary
Detailed Reports