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Quantified Commit Activity Over 14 Days

Developer	Avatar	Branches	PRs	Commits	Files	Changes
hoshi-hiyouga		1	0/0/0	44	74	2070
codingma		1	5/4/1	5	22	175
Shiyu Zhang		1	2/1/1	1	6	33
Elwin		1	1/1/0	3	2	17
Lian Junhong		1	1/1/0	1	2	16
marko1616		1	1/1/0	1	1	5
-.-		1	0/0/0	1	1	2
piamo (piamo)		0	1/0/0	0	0	0
-.- (yzoaim)		0	1/1/0	0	0	0
(Bill) Yuchen Lin (yuchenlin)		0	1/0/0	0	0	0
lgqnb (2481241414)		0	1/0/1	0	0	0
None (01WarpDrive)		0	1/0/0	0	0	0
qianhao (qianhao0713)		0	1/0/0	0	0	0

_{PRs: created by that dev and opened/merged/closed-unmerged during the period}

Report On: Fetch issues

Recent Activity Analysis

The LLaMA-Factory repository has been actively maintained with a focus on fine-tuning large language models using advanced algorithms and techniques. The project supports a wide range of models and has implemented features like GaLore, BAdam, and various quantization methods to enhance training efficiency.

A notable issue in the recent activity is the occurrence of errors during the fine-tuning process, particularly when using specific configurations or hardware setups. For instance, issues #4867 and #4836 report errors related to hardware compatibility and software exceptions during model training and inference. These issues highlight potential challenges in adapting the toolkit to diverse computational environments.

Common themes among the issues include difficulties with model loading, configuration problems, and hardware-specific errors. These recurring themes suggest areas where additional documentation or error handling could improve user experience.

Issue Details

Most Recently Created Issues

1. Issue #4897: "页面训练" - Created 0 days ago.

   • Priority: Medium
   • Status: Closed
   • Created by: yawzhe

2. Issue #4896: "ppo训练完成后推理报错" - Created 0 days ago.

   • Priority: High
   • Status: Closed
   • Created by: XiaoLong (Loong435)

3. Issue #4895: "merge合并多个lora时，怎么平均各个lora再合并" - Created 0 days ago.

   • Priority: Low
   • Status: Closed
   • Created by: LiaoYongyi

Most Recently Updated Issues

1. Issue #4836: "昇腾910b推理glm4-9b-chat出现 NPU function error" - Last updated 2 days ago.

   • Priority: High
   • Status: Closed
   • Updated by: DataAnalysist (AlexYoung757)

2. Issue #4867: "在sft训练之后进行合并提示我 Can't find 'adapter_config.json'" - Last updated 1 day ago.

   • Priority: Medium
   • Status: Closed
   • Updated by: yuge (yugecode)

3. Issue #4844: "运行qwen-14B时报错：AttributeError: module 'transformers_modules.Qwen-14B-Chat.modeling_qwen' has no attribute 'QWenLMHeadModel'" - Last updated 3 days ago.

   • Priority: Critical
   • Status: Closed
   • Updated by: FANSHI LI (brillianti)

Report On: Fetch pull requests

Analysis of Open and Recently Closed Pull Requests in the LLaMA-Factory Repository

Open Pull Requests

1. PR #4892: update deepseek template

   • Status: Open
   • Age: Created today
   • Summary: Adds a new field format_system to the chat template for deepseek. This is a minor change with only 1 line added.
   • Concerns: No tests written for the new addition.

2. PR #4877: Update src\llamafactory\train\sft\metric.py

   • Status: Open
   • Age: Created 1 day ago
   • Summary: Optimizes input parameters for rouge/bleu evaluations and adds support for English data evaluation.
   • Concerns: No new tests written despite significant changes to evaluation metrics.

3. PR #4733: merge easycontext

   • Status: Open
   • Age: Created 10 days ago, last edited 4 days ago
   • Summary: Integrates easycontext's sequence parallel into sft's workflow.
   • Concerns: Contains direct code from easycontext instead of using imports, as suggested by a reviewer.

4. PR #4724: Adding Magpie data link to the Readme

   • Status: Open
   • Age: Created 11 days ago
   • Summary: Adds a link to the Magpie dataset in the README.
   • Concerns: Minor change, but no tests are necessary.

5. PR #4686: support ollama modelfile export

   • Status: Open
   • Age: Created 15 days ago, last edited 1 day ago
   • Summary: Supports exporting ollama model files and includes test cases.
   • Concerns: None noted; inclusion of tests is positive.

6. PR #4680: added ppo v2 from TRL

   • Status: Open
   • Age: Created 15 days ago, last edited 9 days ago
   • Summary: Adds ppo v2 from TRL but lacks detailed description or issue fixing.
   • Concerns: No tests written, and no clear documentation on what issue it fixes.

7. PR #4377: Feature/support qwenvl glm4-v phi3-v(conflict resolving)

   • Status: Open
   • Age: Created 30 days ago, last edited today
   • Summary: Supports additional models and features extensive discussion on implementation details.
   • Concerns: Complex PR with multiple edits and discussions indicating potential integration challenges.

8. PR #4136: Support Several MLLM Models

   • Status: Open
   • Age: Created 43 days ago, last edited 1 day ago
   • Summary: Adds support for several multimodal LLMs with detailed feature additions for fine-tuning and inference.
   • Concerns: The PR is still open and has not been merged, suggesting possible integration issues or ongoing review discussions.

Recently Closed Pull Requests

1. PR #4878: Train the last turing conversation

   • Status: Closed (Merged)
   • Age: Created and closed 1 day ago
   • Summary: Adds mode to fine-tune only the last turn of the conversation in RAG.
   • Note: Successfully merged quickly, indicating clear utility and likely good implementation.

2. PR #4822 & #4821

   • Status: Closed (Merged)
   • Age: Both created and closed within 5 days
   • Summary: Minor updates to GitHub actions and evaluation task naming conventions.
   • Note: Quick merges suggest these were uncontroversial, well-received updates.

3. PR #4793

   • Status: Closed (Not Merged)
   • Age: Created and closed within 7 days
   • Summary: Attempts to fix an AttributeError but was closed as a duplicate.
   • Note: Indicates good monitoring of duplicate efforts but also suggests possible communication gaps in PR purposes.

Summary

The repository maintains active development with several open PRs focused on expanding capabilities and refining existing features. The quick merging of certain PRs like #4878 indicates effective management for straightforward enhancements. However, some long-standing open PRs like #4377 and #4136 suggest complex features that require careful consideration and testing before integration. The presence of a PR closed due to duplication (#4793) highlights the need for contributors to check existing fixes and communications before submission.

Report On: Fetch Files For Assessment

Analysis of Source Code Files

1. src/llamafactory/data/loader.py

Overview

This file is responsible for loading datasets, with support for different data sources like local files, scripts, or directly from Hugging Face's hub. It handles various dataset formats and integrates with the datasets library.

Structure

• Imports necessary libraries and modules.
• Defines a function _load_single_dataset to handle the loading of individual datasets based on specified attributes.
• Provides a function _get_merged_dataset to combine multiple datasets.
• Implements _get_preprocessed_dataset to apply preprocessing functions to the dataset.
• The main function get_dataset orchestrates the loading, merging, and preprocessing of datasets based on training arguments.

Quality Assessment

• Good: Modular structure with clear separation of concerns among functions.
• Improvement Needed: Exception handling could be more specific in places where generic exceptions are raised. More detailed logging might help in debugging data loading issues.

2. src/llamafactory/train/ppo/trainer.py

Overview

This file implements a custom trainer for Proximal Policy Optimization (PPO) adapted for language models, integrating features from both HuggingFace's Trainer and PPO-specific training loops.

Structure

• Extends PPOTrainer from the TRL library.
• Customizes initialization to set up PPO-specific configurations and model preparation.
• Implements ppo_train, a method that encapsulates the training loop specific to PPO.
• Includes methods for creating optimizers and schedulers tailored to PPO requirements.

Quality Assessment

• Good: Integration of multiple functionalities (like logging, callback handling) from HuggingFace's ecosystem.
• Improvement Needed: The complexity of the ppo_train method is high; breaking down into smaller functions could improve readability and maintainability.

3. src/llamafactory/webui/components/train.py

Overview

This file defines UI components for training configuration within a web interface using Gradio. It allows users to configure training parameters through a graphical interface.

Structure

• Uses Gradio for UI components.
• Defines a large number of configurable parameters for training through UI elements like dropdowns, text boxes, and sliders.
• Functions are used to dynamically update UI components based on user interactions.

Quality Assessment

• Good: Effective use of Gradio's capabilities to provide an interactive and user-friendly interface.
• Improvement Needed: The file is quite long and handles many aspects; splitting into smaller modules could enhance manageability.

4. src/llamafactory/hparams/parser.py

Overview

Handles parsing of hyperparameters from command-line arguments or configuration files. It supports different formats like YAML and JSON.

Structure

• Utilizes HfArgumentParser for argument parsing which simplifies integration with HuggingFace's transformers setup.
• Functions are defined to parse training, inference, and evaluation arguments separately.
• Includes validation checks to ensure that provided arguments meet expected criteria.

Quality Assessment

Good: Robust parsing capabilities that integrate well with external configurations and command-line inputs.

Improvement Needed: Could benefit from more detailed error messages that guide users on how to resolve configuration issues.

5. src/llamafactory/data/processors/supervised.py

Overview

Contains functions for preprocessing supervised learning datasets specifically tailored for language models, including handling multi-turn conversations and packing sequences efficiently.

Structure

Functions are defined for encoding supervised examples, preprocessing datasets for supervised tasks, and printing examples for debugging.

Utilizes custom utilities like greedy_knapsack for efficient data handling.

Quality Assessment

Good: Specific focus on efficiency with functions like greedy_knapsack.

Improvement Needed: Could use more inline comments explaining the logic, especially in complex functions like _encode_supervised_example.

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Development Team and Recent Activity

Team Members and Recent Commit Activity

• hoshi-hiyouga (hiyouga)

  • Recent Commits: 44 commits with 2070 changes across 74 files and 1 branch.
  • Activities:
  • Involved in various fixes, updates, and enhancements across multiple files including training workflows, data processing scripts, web UI components, and more.
  • Merged several pull requests from other contributors.

• aofengdaxia (Shiyu Zhang)

  • Recent Commits: 1 commit with 33 changes across 6 files and 1 branch.
  • Activities:
  • Contributed to training dialogues in supervised processors and other related files.

• codemayq (codingma)

  • Recent Commits: 5 commits with 175 changes across 22 files and 1 branch.
  • Activities:
  • Worked on updating test templates, Dockerfiles, and YAML configurations for training models.

• hzhaoy (Elwin)

  • Recent Commits: 3 commits with 17 changes across 2 files and 1 branch.
  • Activities:
  • Focused on Dockerfile updates for CUDA compatibility and other minor fixes.

• marko1616

  • Recent Commits: 1 commit with 5 changes across 1 file and 1 branch.
  • Activities:
  • Updated command preview for Windows in the web UI utils file.

Patterns, Themes, and Conclusions

• High Activity: The repository shows high activity levels with frequent commits mainly from hoshi-hiyouga who appears to be a lead developer or maintainer.
• Focus Areas:
  • Significant focus on refining the training processes (PPO, DPO, KTO) and integrating new model capabilities.
  • Continuous improvements and bug fixes in the web UI components suggest an emphasis on user interaction and usability.
• Collaboration: There is evidence of collaboration among team members, especially in handling issues and merging pull requests.
• Testing and Stability: Frequent updates to test templates and configurations indicate ongoing efforts to ensure the stability and reliability of the software.
• Documentation and Community Engagement: Updates to README files in multiple languages point towards an intention to reach a broader audience and ensure clarity in documentation.