[Example] ggml: add basic example with CI

[juntao]

Hello, I am a code review bot on flows.network. Here are my reviews of code commits in this PR.

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Overall Summary:

The pull request titled "[Example] ggml: add basic example with CI" introduces significant changes to the project related to adding a basic example for WASI-NN with GGML Backend and integrating a new job "StarCoder 2 7B" to the GitHub workflows.

Potential Issues and Errors:

1. Lack of tests for the new example functionality may impact its correctness and reliability.
2. In the README file, there is an apparent issue of content overlap with an unrelated section about Python learning that needs to be reviewed and corrected.
3. Main.rs code changes require comments and documentation for improved readability and maintainability.
4. The commit message could be more descriptive to provide a clear understanding of the changes made.
5. The use of specific environment variables and hardcoded values in the new job script may need validation for error prevention.
6. The validity and accessibility of the URL from which the file is downloaded in the new job should be ensured to avoid CI failures.
7. A thorough testing of the script under the "run" directive in the new job is necessary to prevent errors during the CI process.
8. Error handling mechanisms should be implemented to capture and manage failures during the execution of the job.

Most Important Findings:

The addition of the new example and the new job are valuable contributions to the project. Ensuring comprehensive testing, addressing code readability and documentation, resolving content overlaps, validating environment variables, and handling errors effectively are critical areas that require attention before merging the pull request. Overall, the changes have the potential to enhance the project significantly if the identified issues are resolved.

Details

Commit e073e029d1d4470f616ae0bf2a0c7bb2640587cb

Key Changes:

1. Added a basic example for WASI-NN with GGML Backend. This includes adding Cargo.toml, README.md, main.rs, and the wasm file.
2. Cargo.toml specifies dependencies like serde_json and wasmedge-wasi-nn.
3. README.md provides details on the example, how to download the model, parameters to adjust, and how to execute the WASM.
4. main.rs contains the code for interacting with the WASI-NN graph context, setting options, reading input, executing inferences, etc.
5. A new wasm file wasmedge-ggml-basic.wasm was added.

Potential Problems:

1. The patch does not mention any tests added for the new example. It would be beneficial to include tests for the new functionality to ensure its correctness.
2. It seems there is a potential issue in the README file where the content seems to overlap with an unrelated section about Python learning. This should be reviewed and corrected if required.
3. The code changes in main.rs could benefit from comments and documentation to enhance readability and maintainability.
4. The patch could be improved by providing a more detailed description of the changes made in the commit message.

Overall, the changes look significant and add value to the project, but the points mentioned above should be addressed before merging the pull request.

Commit abe2d363907538117af8ccb661afdbbc1a85c1f4

Key Changes:

1. Added a new job called "StarCoder 2 7B" to the GitHub workflows for the repository.
2. The job includes steps to download a model file, build a project, and run the project using specific environment variables.

Potential Problems:

1. The added job seems to be related to a specific model ("StarCoder 2 7B") and includes downloading a file from a URL. Ensure the URL is valid and accessible during the CI process to prevent failures.
2. The use of specific environment variables like "$NGL" and the hardcoded value "100" for "n_predict" might need validation and could be susceptible to errors if not properly handled.
3. Make sure that all dependencies required for building and running the project are correctly set up and accessible within the CI environment.
4. The script provided under the "run" directive for the job should be well-tested to ensure it executes without errors during the CI process.
5. Consider adding error handling mechanisms in the script to capture any failures during the execution of the job.