Report On: Fetch issues

Analyzing the open issues for the software project, we can identify several trends and notable problems:

1. Issue #394: Bad Case Reporting Guidance:

   • This issue, created 38 days ago, instructs users not to report certain types of problems in the Issues section but rather in the Discussions. It lists several issues (#393, #212, #335, #306, #310, #225, #251, #253) that are either difficult to resolve or require more bad case examples to improve the model. This indicates that the project is dealing with some persistent challenges that are not easily solvable with the current model version.

2. Recent Closed Issues:

   • A number of issues have been closed recently, indicating active maintenance and responsiveness from the project maintainers. Notably, issues related to Gradio UI (#674, #672), LoRA training (#663, #662), and streamlit demos (#625, #626) have been addressed. This suggests that the project is actively working on improving user interfaces and fixing bugs related to training and demos.

3. Feature Requests:

   • There are feature requests for enhancing multimodal capabilities (#667), comparing inference performance between vllm and TensorRT-LLM (#664), and adding more functionality to the software (#658, #650, #640, #609, #629). These requests indicate a desire for the project to expand its capabilities and adapt to new use cases.

4. Uncertainties and TODOs:

   • There are uncertainties related to the integration of tools and tool choices (#609), as well as how to properly utilize the model in conjunction with external tools (#607). These issues highlight the need for better documentation or features to support these use cases.

5. Anomalies:

   • Some issues report anomalies such as unexpected behavior when fine-tuning the model (#657, #655), errors in loading state_dict (#657), and issues with CUDA and device compatibility (#674, #672). These anomalies suggest that there might be underlying issues with model compatibility and stability that need to be addressed.

6. Technical Questions and Support:

   • A number of issues are seeking technical support for specific problems, such as errors in script execution (#664, #663, #662), model integration after training (#655), and issues with vocabulary size (#634). These indicate that users are engaging with the project at a technical level and may benefit from more detailed documentation or tutorials.

7. Closed Issues with Quick Resolution:

   • Some issues were closed very quickly after being opened (#660, #658, #657, #655, #653, #651, #650, #649, #647, #636, #634, #633, #630, #629, #628, #627, #626, #625, #622, #621, #618, #616, #614, #609, #607). This could be due to the issues being resolved promptly or because they were deemed not relevant or duplicates. It's a positive sign of active project maintenance.

In summary, the project seems to be actively maintained with a focus on improving user interfaces, addressing training and model integration issues, and expanding capabilities. However, there are persistent challenges related to model behavior and compatibility, as well as a need for better support and documentation for technical issues and feature integration.

Report On: Fetch pull requests

Analyzing the provided list of pull requests (PRs) for a software project, we can observe several patterns and notable points:

Recently Closed Pull Requests:

PR #670 and PR #669:

• Both PRs seem to be related to updating the OpenAI demo.
• They have been merged on the same day they were created, indicating a fast-track process.
• The commits and files changed are very similar, which suggests that PR #669 might have been a subset or a preliminary part of PR #670.
• The changes include support for new OpenAI API features, such as embedding interfaces and function call requests, as well as dependency updates.
• The addition of update_requirements.sh indicates an effort to streamline the dependency update process.
• The line totals indicate a significant amount of code change, which should ideally be reviewed thoroughly for potential issues.

PR #665:

• A simple typo fix in PROMPT_en.md from "readablity" to "readability".
• The quick merge of this PR indicates good maintenance practices for documentation.

PR #654:

• Adds a deployment guide for TRT-LLM, suggesting improvements in performance or scalability.
• Merged quickly, which could mean that the documentation was well-received and considered important for users.

PR #644:

• Adds code for TensorRT-LLM CLI and updates the README.
• The large number of lines removed in cookbook/finetune_muti_classfication.ipynb might indicate a significant refactor or cleanup.
• Merged within a couple of days, which suggests that the changes were either straightforward or urgent.

PR #641:

• This PR was closed without being merged, which is notable.
• It addresses a default device assignment issue for model loading.
• The reason for not merging is not provided, but it could be due to an alternative fix being implemented, the issue being deprioritized, or a disagreement on the solution.

General Observations:

• There is a pattern of PRs being merged very quickly, sometimes on the same day they are created. This could indicate a highly responsive team but also raises concerns about whether the changes are being adequately reviewed and tested.
• The PR descriptions are brief and sometimes vague, which could make it difficult for external contributors or new team members to understand the context and impact of the changes.
• The use of "Unknown" for the commit author suggests a lack of proper attribution or an issue with the tooling or process used to capture this information.
• There is a focus on updating dependencies and supporting new features, which is positive for keeping the project current and functional.
• The closed PRs cover a range of updates, from bug fixes to feature additions and documentation improvements, indicating active development and maintenance of the project.

Overall, the project seems to be actively developed with a focus on improving compatibility with OpenAI's API and enhancing the user experience through better documentation and tooling. However, the quick merging of PRs and the lack of detailed descriptions could potentially lead to issues being overlooked. It would be beneficial for the project to ensure that PRs are adequately reviewed and that commit messages and PR descriptions provide sufficient context for future reference.

Report On: Fetch commits

Overview of the ChatGLM3 Project

The ChatGLM3 project is an open-source software initiative that focuses on developing bilingual chat language models. It is a collaboration between Zhipu.AI and Tsinghua University's KEG lab. The project's main product is the ChatGLM3-6B model, which is part of the ChatGLM3 series and offers several key features:

1. A more powerful base model: ChatGLM3-6B-Base uses diverse training data, more training steps, and a more rational training strategy, claiming to have the strongest performance among sub-10B parameter models.
2. Comprehensive functionality: The model supports complex scenarios like multi-turn dialogues, function calls, code interpretation, and agent tasks.
3. An extensive open-source sequence: In addition to the dialogue model, the project also open-sources the base model and a long-text dialogue model, with weights fully open for academic research and free commercial use upon registration.

The project's README provides a detailed introduction, model list, evaluation results, usage instructions, and links to related resources. It also includes a cautionary note about the limitations of the model and the responsibilities of users regarding data security and the potential risks of misuse.

Apparent Problems, Uncertainties, TODOs, or Anomalies

• The project's license is listed as "Other," which is not specific and may create uncertainty about the terms under which the software can be used.
• There is a single open issue, which may need attention.
• The README is quite comprehensive, but it may be overwhelming for new users. A simplified quick-start guide could be beneficial.
• The project warns users about the potential risks and responsibilities associated with using the model, which is important but also suggests that the model's outputs may not always be reliable.

Recent Activities of the Development Team

The development team has been actively updating the project, with recent commits focusing on:

• Upgrading the OpenAI interface to match the latest OpenAI API, including support for similarity models, function call requests, and the use of the OpenAI client for easier access.
• Adding a TensorRT-LLM deployment guide for accelerated inference using NVIDIA's TensorRT-LLM tool.
• Fixing typos and improving documentation.
• Addressing issues and bugs reported by users, such as problems with history management in demos and input size limitations.
• Enhancing the composite demo with new examples and adjustable parameters.
• Adding support for more fine-tuning parameters for the base model and updating corresponding scripts.
• Introducing an OpenAI API example that includes function call content.
• Creating a cookbook with examples of accurate prompt construction and fine-tuning for multi-classification tasks.

Team Members and Collaborations

The recent commits do not explicitly list the members of the development team. However, based on the commit history, we can identify several contributors:

• zR (zR): They have been very active, making numerous commits related to updating the OpenAI demo, fixing bugs, and improving documentation.
• jershi (jershi): Contributed to adding the TensorRT-LLM deployment guide and updating demo outputs.
• Ikko Eltociear Ashimine (Ikko Eltociear Ashimine): Fixed a typo in the documentation.
• xxw1995 (xxw1995): Supported fine-tuning for the base model demo.
• longerking (longerking): Fixed an issue with the composite demo where tools could not be called multiple times in multi-turn dialogues.

Patterns and Conclusions

The development team is focused on improving the usability and performance of the ChatGLM3 models, as well as ensuring that the project's documentation is clear and helpful. The team seems responsive to community feedback, quickly addressing reported issues. The addition of the TensorRT-LLM deployment guide indicates a commitment to enhancing the model's inference speed and efficiency, which is crucial for real-world applications.

The collaboration between team members is evident from the commits, with some contributors focusing on specific aspects like fine-tuning and deployment, while others work on broader issues like documentation and bug fixes. The project's activity suggests a healthy and dynamic development environment with a focus on continuous improvement and user support.