The LLaMA Factory is a software project that provides a unified framework for the efficient fine-tuning of over 100 different large language models (LLMs). It is hosted on GitHub under the repository hiyouga/LLaMA-Factory and is licensed under the Apache License 2.0. The project was created on May 28, 2023, and has seen active development since then. As of the last update, the repository has amassed a significant following with 17,803 stars and 2,149 forks.
The LLaMA Factory offers a wide range of features for model training and fine-tuning, including support for various models such as LLaMA, Mistral, Mixtral-MoE, Qwen, Yi, Gemma, Baichuan, ChatGLM, Phi, and others. It integrates methods like pre-training, supervised fine-tuning, reward modeling, PPO, DPO, and ORPO. The project also provides scalable resources for different precision levels during training and advanced algorithms like GaLore and BAdam.
The project's README file provides comprehensive documentation on getting started with the software, including links to Colab notebooks for easy access to GPU resources for training. It also includes benchmarks showcasing the performance improvements offered by the LLaMA Factory's tuning methods compared to other approaches.
hoshi-hiyouga:
mlinmg:
Ledzy:
codemayq:
marko1616:
liuzc:
The development team is actively maintaining the project with frequent commits addressing new features, bug fixes, performance improvements, and user-reported issues. The contributions are mainly from a core group of developers with occasional contributions from the community.
The team seems responsive to community feedback as evidenced by their interaction with issues and pull requests. There is a focus on ensuring compatibility across different environments (e.g., Docker support) and improving user experience (e.g., updating examples and documentation).
Given the high level of activity and responsiveness of the development team, it can be concluded that the LLaMA Factory project is in a healthy state with ongoing efforts to enhance its capabilities and usability.
Note: The detailed commit messages have been omitted for brevity but can be provided upon request.
README_ru.md
) has been added.Closed without being merged due to being a duplicate of another pull request (#2445). This indicates good housekeeping by avoiding redundant updates.
Closed after several updates by the contributor. It appears that this was more of a development branch rather than a specific feature or bug fix.
Merged successfully after review. This indicates that new features are being added to the project after thorough review.
--max_samples
error in streaming modeClosed after addressing an error related to --max_samples
in streaming mode. This indicates responsiveness to fixing bugs that affect functionality.
Merged successfully after review. This indicates enhancements to dataset handling within the project.
Closed without being merged due to unresolved issues with MoD (Mixture of Depths) implementation. This indicates ongoing challenges with integrating new algorithms.
Merged successfully after review and necessary changes. This indicates that performance-enhancing algorithms are being considered for inclusion in the project.
Merged successfully after review. This indicates attention to detail in ensuring model configurations are correct.
Closed without being merged due to direct loading issues with the dataset. This indicates attentiveness to dataset usability within the project.
Closed without being merged after contributor acknowledgment. It appears there was no need for these changes at this time.
Closed without being merged due to unresolved issues with training using MoD. This suggests that complex features may require additional development time before they are ready for integration.
Merged successfully after review. This suggests improvements in prediction capabilities for single-card setups.
Closed without being merged as it appears there was no significant change required or it was addressed elsewhere.
Merged successfully after review and necessary changes. This suggests that templates supporting specific models or use cases are being added to enhance usability.
The analysis shows active development within the hiyouga/LLaMA-Fac
Developer | Avatar | Branches | PRs | Commits | Files | Changes |
---|---|---|---|---|---|---|
hoshi-hiyouga | 1 | 0/0/0 | 64 | 69 | 2365 | |
Ledzy | 1 | 1/1/0 | 3 | 10 | 227 | |
Marco | 1 | 3/1/2 | 2 | 10 | 111 | |
marko1616 | 1 | 1/1/0 | 7 | 2 | 83 | |
codingma | 1 | 2/1/1 | 2 | 1 | 51 | |
Lao | 1 | 2/1/1 | 2 | 2 | 19 | |
Erich Schubert | 1 | 1/1/0 | 1 | 1 | 2 | |
liuzc | 1 | 0/0/0 | 1 | 1 | 2 | |
None (linpan) | 0 | 1/0/1 | 0 | 0 | 0 | |
Yingbei Tong (tybalex) | 0 | 1/0/1 | 0 | 0 | 0 | |
Xu Song (xu-song) | 0 | 1/0/1 | 0 | 0 | 0 | |
None (Katehuuh) | 0 | 1/0/1 | 0 | 0 | 0 | |
None (liu-zichen) | 0 | 1/1/0 | 0 | 0 | 0 | |
None (BUAADreamer) | 0 | 1/0/0 | 0 | 0 | 0 | |
Saken Tsukenofu (sakentsunofu) | 0 | 1/0/0 | 0 | 0 | 0 | |
kevinpro (Ricardokevins) | 0 | 1/0/1 | 0 | 0 | 0 | |
Louis Brulé Naudet (louisbrulenaudet) | 0 | 0/0/1 | 0 | 0 | 0 |
PRs: created by that dev and opened/merged/closed-unmerged during the period
The LLaMA Factory project is a comprehensive framework for fine-tuning large language models (LLMs), supporting a variety of models and fine-tuning techniques. The project is well-received in the community, evidenced by its high number of stars and forks on GitHub. It is crucial for strategic positioning in the AI and machine learning market, especially given the increasing demand for customizable LLMs across different industries.
The development team shows a robust pace with frequent updates, addressing both bugs and feature requests efficiently. This indicates a healthy project lifecycle and an active community engagement which is vital for keeping the software relevant and competitive. The responsiveness to user-reported issues enhances user trust and satisfaction, potentially increasing adoption rates.
The ability to fine-tune LLMs across various models offers significant market opportunities, particularly in sectors requiring specialized language models like healthcare, legal, and customer service. The project’s support for multimodal LLM fine-tuning (as seen in PR #3394) positions it well to tap into emerging markets that integrate text with other data types, such as images or videos.
Investing in resolving key issues like memory management (Issue #3386) and model compatibility (Issue #3361) can be costly but are necessary to maintain the project's reliability and performance. However, these investments are justified by the potential returns from increased adoption and user satisfaction. Enhancing documentation and examples can also reduce support costs by enabling users to solve more problems independently.
The current team size appears to be adequate for the project's needs, with core developers actively supported by community contributions. However, as the project scales and more features are added, there might be a need to expand the team, particularly in areas like testing, documentation, and support to maintain quality and efficiency.
Enhance Documentation: Improving documentation, especially around new features like DPO training and multimodal fine-tuning, will help users better leverage the project’s capabilities, reducing barriers to entry and fostering wider adoption.
Focus on Scalability: Address issues related to model loading (Issues #3397, #3396) and memory consumption (Issue #3386) to improve scalability. This ensures the framework can handle larger models and datasets which is crucial as user demands grow.
Expand Test Coverage: Increasing test coverage, especially automated tests around new features and edge cases reported in issues, will help catch bugs earlier and reduce maintenance costs over time.
Community Engagement: Continue fostering community engagement through regular updates, quick responses to issues and pull requests, and perhaps periodic community calls or webinars. This will enhance user loyalty and attract new contributors.
Market Analysis: Conduct regular market analysis to understand emerging needs in the LLM space. This can guide feature development prioritizing those that add significant market value or competitive advantage.
The LLaMA Factory project is strategically positioned with strong potential for growth in the rapidly evolving AI landscape. By focusing on scalability, documentation improvement, and proactive community engagement, the project can enhance its market position and meet future challenges effectively.
Developer | Avatar | Branches | PRs | Commits | Files | Changes |
---|---|---|---|---|---|---|
hoshi-hiyouga | 1 | 0/0/0 | 64 | 69 | 2365 | |
Ledzy | 1 | 1/1/0 | 3 | 10 | 227 | |
Marco | 1 | 3/1/2 | 2 | 10 | 111 | |
marko1616 | 1 | 1/1/0 | 7 | 2 | 83 | |
codingma | 1 | 2/1/1 | 2 | 1 | 51 | |
Lao | 1 | 2/1/1 | 2 | 2 | 19 | |
Erich Schubert | 1 | 1/1/0 | 1 | 1 | 2 | |
liuzc | 1 | 0/0/0 | 1 | 1 | 2 | |
None (linpan) | 0 | 1/0/1 | 0 | 0 | 0 | |
Yingbei Tong (tybalex) | 0 | 1/0/1 | 0 | 0 | 0 | |
Xu Song (xu-song) | 0 | 1/0/1 | 0 | 0 | 0 | |
None (Katehuuh) | 0 | 1/0/1 | 0 | 0 | 0 | |
None (liu-zichen) | 0 | 1/1/0 | 0 | 0 | 0 | |
None (BUAADreamer) | 0 | 1/0/0 | 0 | 0 | 0 | |
Saken Tsukenofu (sakentsunofu) | 0 | 1/0/0 | 0 | 0 | 0 | |
kevinpro (Ricardokevins) | 0 | 1/0/1 | 0 | 0 | 0 | |
Louis Brulé Naudet (louisbrulenaudet) | 0 | 0/0/1 | 0 | 0 | 0 |
PRs: created by that dev and opened/merged/closed-unmerged during the period
Developer | Avatar | Branches | PRs | Commits | Files | Changes |
---|---|---|---|---|---|---|
hoshi-hiyouga | 1 | 0/0/0 | 64 | 69 | 2365 | |
Ledzy | 1 | 1/1/0 | 3 | 10 | 227 | |
Marco | 1 | 3/1/2 | 2 | 10 | 111 | |
marko1616 | 1 | 1/1/0 | 7 | 2 | 83 | |
codingma | 1 | 2/1/1 | 2 | 1 | 51 | |
Lao | 1 | 2/1/1 | 2 | 2 | 19 | |
Erich Schubert | 1 | 1/1/0 | 1 | 1 | 2 | |
liuzc | 1 | 0/0/0 | 1 | 1 | 2 | |
None (linpan) | 0 | 1/0/1 | 0 | 0 | 0 | |
Yingbei Tong (tybalex) | 0 | 1/0/1 | 0 | 0 | 0 | |
Xu Song (xu-song) | 0 | 1/0/1 | 0 | 0 | 0 | |
None (Katehuuh) | 0 | 1/0/1 | 0 | 0 | 0 | |
None (liu-zichen) | 0 | 1/1/0 | 0 | 0 | 0 | |
None (BUAADreamer) | 0 | 1/0/0 | 0 | 0 | 0 | |
Saken Tsukenofu (sakentsunofu) | 0 | 1/0/0 | 0 | 0 | 0 | |
kevinpro (Ricardokevins) | 0 | 1/0/1 | 0 | 0 | 0 | |
Louis Brulé Naudet (louisbrulenaudet) | 0 | 0/0/1 | 0 | 0 | 0 |
PRs: created by that dev and opened/merged/closed-unmerged during the period
Based on the provided information and the context of the issues, here is a detailed analysis of the open issues for the software project "LLaMA-Factory":
Issue #3398: The issue reports an out-of-memory error during model evaluation. This indicates that the evaluation process is consuming more memory than available, which could be due to large batch sizes or model sizes. It's critical to address this to prevent crashes and ensure smooth evaluations.
Issue #3397 and #3396: These issues involve problems with model loading after fine-tuning, which could indicate inconsistencies in how models are saved or restored, especially concerning different fine-tuning methods like full-parameter fine-tuning and LoRA.
Issue #3395: The user is experiencing difficulties understanding how to properly train with DPO (Dynamic Prompt Optimization). The lack of clarity in the README about merging LoRA weights and inference procedures suggests that documentation may need to be improved for better user guidance.
Issue #3394: This issue is about adding support for multimodal LLM finetuning, which is currently a work in progress. Multimodal capabilities are significant as they expand the use cases of LLMs beyond text-based tasks.
Issue #3393: The user is asking for examples related to longlora and streaming llm support, indicating a need for more comprehensive examples or documentation for these features.
Issue #3392: The user requests support for num_beams>1
during SFT prediction, which implies a need for enhanced generation capabilities during inference.
Issue #3391: There seems to be an issue with identity adaptation not reflecting in model responses, suggesting potential problems with adapter training or merging.
Issue #3389: The user is asking about implementing a custom tool for outputting weather information, indicating a desire for extending functionality through custom tool integration.
Issue #3388 and #3387: These issues report errors related to file handling and parameter passing during inference, which could affect usability and robustness.
Issue #3386: A user reports increased memory consumption when training llama3 compared to qwen14b, suggesting possible inefficiencies or bugs in memory management for different models.
Issue #3385: An export error during llamafactory finetuning indicates potential issues with serialization or tensor sharing that need to be addressed.
Issue #3384: A user reports that training gets stuck after a few iterations, which could indicate deadlocks or inefficiencies in the training loop.
Issue #3382 and #3381: Users are reporting errors when using Llama3-8B-Chinese-Chat, pointing towards potential compatibility issues or bugs with specific models.
Issue #3374: A user suggests that shuffle should be written earlier in code, indicating potential improvements in data processing logic.
Issue #3373: A user reports that using lora sft sometimes results in non-stopping model outputs during beam search inference, suggesting possible issues with stopping criteria or beam search implementation.
Issue #3371: A user has initiated a Russian README file but it's still under development, indicating ongoing efforts to make documentation more accessible to non-English speakers.
Issue #3370: This issue discusses potential problems with model format and extended training length when merging LoRA adapters, suggesting complexities in managing training states across different configurations.
Issue #3361: A user reports an inability to perform inference with LLaMA3-8B, pointing towards possible issues with model compatibility or setup procedures.
Issue #3359: A user experiences access denial when saving files during training, which could be related to system permissions or resource locks on the storage medium.
Issue #3353: A report of loss being zero during lora sft training indicates either a bug in loss computation or an issue with the training setup that needs investigation.
Issue #3347: Users report errors related to data type mismatches when using vllm inference with bf16 precision, suggesting potential issues with datatype handling during model execution.
Issue #3344: Users encounter CUDA errors when using dbrx-base and dbrx-instruct models for inference, indicating potential compatibility issues with specific hardware or software configurations.
The recently closed issues indicate active maintenance and responsiveness from maintainers in addressing reported bugs and feature requests. For instance:
README_ru.md
) has been added.Closed without being merged due to being a duplicate of another pull request (#2445). This indicates good housekeeping by avoiding redundant updates.
Closed after several updates by the contributor. It appears that this was more of a development branch rather than a specific feature or bug fix.
Merged successfully after review. This indicates that new features are being added to the project after thorough review.
--max_samples
error in streaming modeClosed after addressing an error related to --max_samples
in streaming mode. This indicates responsiveness to fixing bugs that affect functionality.
Merged successfully after review. This indicates enhancements to dataset handling within the project.
Closed without being merged due to unresolved issues with MoD (Mixture of Depths) implementation. This indicates ongoing challenges with integrating new algorithms.
Merged successfully after review and necessary changes. This indicates that performance-enhancing algorithms are being considered for inclusion in the project.
Merged successfully after review. This indicates attention to detail in ensuring model configurations are correct.
Closed without being merged due to direct loading issues with the dataset. This indicates attentiveness to dataset usability within the project.
Closed without being merged after contributor acknowledgment. It appears there was no need for these changes at this time.
Closed without being merged due to unresolved issues with training using MoD. This suggests that complex features may require additional development time before they are ready for integration.
Merged successfully after review. This suggests improvements in prediction capabilities for single-card setups.
Closed without being merged as it appears there was no significant change required or it was addressed elsewhere.
Merged successfully after review and necessary changes. This suggests that templates supporting specific models or use cases are being added to enhance usability.
The analysis shows active development within the hiyouga/LLaMA-Factory repository with several open pull requests focused on adding new features such as multimodal fine-tuning (#3394), new algorithms like LISA (#3103), and utility scripts (#2845). Closed pull requests indicate responsiveness to community contributions, with several merges introducing new algorithms (e.g., BAdam in #3287) and enhancements (e.g., custom dataset previewing in #3291).
Key Observations: 1. Some pull requests remain open for extended periods (e.g., AdaLoRA in #844), suggesting either complexity in integration or lower priority compared to other updates. 2. Closed pull requests without merges often lack necessary information or face unresolved issues (e.g., MoD-related problems in #3263). 3. Merged pull requests show careful consideration by maintainers through reviews and discussions before integration into the main branch (e.g., Mixture of Depth addition in #3338).
Recommendations: 1. Prioritize resolving open pull requests with notable issues such as high VRAM consumption during fine-tuning (#3103). 2. Consider closing long-standing open pull requests if they are no longer relevant or if alternative solutions have been implemented (e.g., AdaLoRA in #844). 3. Continue monitoring newly opened pull requests closely (e.g., Russian Readme file init in #3371) for timely integration into the project after thorough review.
The LLaMA Factory is a software project that provides a unified framework for the efficient fine-tuning of over 100 different large language models (LLMs). It is hosted on GitHub under the repository hiyouga/LLaMA-Factory and is licensed under the Apache License 2.0. The project was created on May 28, 2023, and has seen active development since then. As of the last update, the repository has amassed a significant following with 17,803 stars and 2,149 forks.
The LLaMA Factory offers a wide range of features for model training and fine-tuning, including support for various models such as LLaMA, Mistral, Mixtral-MoE, Qwen, Yi, Gemma, Baichuan, ChatGLM, Phi, and others. It integrates methods like pre-training, supervised fine-tuning, reward modeling, PPO, DPO, and ORPO. The project also provides scalable resources for different precision levels during training and advanced algorithms like GaLore and BAdam.
The project's README file provides comprehensive documentation on getting started with the software, including links to Colab notebooks for easy access to GPU resources for training. It also includes benchmarks showcasing the performance improvements offered by the LLaMA Factory's tuning methods compared to other approaches.
hoshi-hiyouga:
mlinmg:
Ledzy:
codemayq:
marko1616:
liuzc:
The development team is actively maintaining the project with frequent commits addressing new features, bug fixes, performance improvements, and user-reported issues. The contributions are mainly from a core group of developers with occasional contributions from the community.
The team seems responsive to community feedback as evidenced by their interaction with issues and pull requests. There is a focus on ensuring compatibility across different environments (e.g., Docker support) and improving user experience (e.g., updating examples and documentation).
Given the high level of activity and responsiveness of the development team, it can be concluded that the LLaMA Factory project is in a healthy state with ongoing efforts to enhance its capabilities and usability.
Note: The detailed commit messages have been omitted for brevity but can be provided upon request.
The provided source code files from the LLaMA-Factory repository are part of a larger framework designed for efficient fine-tuning of large language models (LLMs). Here's an analysis of each file based on its structure, quality, and potential areas for improvement:
src/llmtuner/extras/constants.py
Purpose: This file contains constants and configurations for different models, providing insights into the model's architecture and settings.
Analysis:
src/llmtuner/data/template.py
Purpose: Manages templates for data formatting, crucial for understanding how data is processed and used within the project.
Analysis:
src/llmtuner/train/utils.py
Purpose: Includes utility functions for training, revealing important mechanisms or optimizations used in the training process.
Analysis:
src/llmtuner/model/loader.py
Purpose: Responsible for loading models, providing details on how different models are integrated and managed within the framework.
Analysis:
load_model
appears to be doing too much, handling multiple conditional paths for different configurations. Refactoring to separate these concerns into smaller, more focused functions would enhance readability and maintainability.src/llmtuner/api/app.py
Purpose: Defines the API structure and endpoints, useful for understanding how external interactions are handled.
Analysis:
Overall, the project demonstrates good software engineering practices but could benefit from some refinements to handle complexity as it scales.