I've analyzed the output from your attempts to use different models with the script2readme tool. There's a pattern of issues that helps explain why some models aren't producing proper READMEs while others (like qwen2.5-coder:7b) work correctly.
- All models are returning responses - The API call itself is working
- Response format is correct - We're getting proper JSON with message.content
- Content quality is the issue - The content returned doesn't match what we need
Let's look at the actual responses from different models:
{
"message": {
"content": " This appears to be a bash script that performs various tasks related to analyzing and benchmarking scripts. The script seems to be designed to handle large text files, and it includes features for generating reports in both CSV and JSON formats.\n\nHere are the key parts of the script:\n\n* `check_dependencies`: This function checks if all required dependencies are installed on the system. It returns an error code if any dependency is missing.\n* `get_system_info`: This function gathers information about the current system, such as its operating system and CPU architecture.\n* `input_handling`: This section of the script handles input arguments passed to the script. It checks if the input file exists and if it's a valid text file. If any errors are found, the script exits with an error message.\n* `get_models`: This function retrieves a list of available models for the current system. It uses the `OSS` command to retrieve this information.\n* `select_model`: This section of the script allows users to select a model from the list of available models. If no model is specified, it will default to using the first available model. The user can also specify a custom model by providing its name or ID as an argument.\n* `validate_input_file`: This function checks if the input file is valid and if it's a large text file that can be analyzed efficiently. If any errors are found, the script exits with an error message.\n* `analyze_script`: This section of the script actually analyzes the input file using the selected model. It uses the `OSS` command to perform the analysis and generate reports in CSV and JSON formats.\n* `generate_benchmark_summary`: This function generates a summary report for the analysis results, including metrics such as total execution time and response size.\n* `log_benchmark`: This function logs the benchmarking information to both the CSV log file and the JSON metrics file. It also adds a timestamp to the log entries.\n\nOverall, this script is designed to be a comprehensive tool for analyzing and benchmarking large text files using available models on a system. The script includes features for handling input arguments, checking dependencies, and generating reports in various formats."
}
}{
"message": {
"content": "This script appears to be a bash shell script for benchmarking some system or program. It takes input from an argument, processes it and logs the results in various CSV format files as well JSON metrics file containing all necessary details about how long each step of processing took, what error occurred if any etc., this can also help debugging issues related to time consumption during execution times for different parts like user-defined functions or system calls.\n"
}
}The issue is that these models are not following the format instructions in our prompt. Instead of generating a structured README with markdown headers, tables, and sections as requested, they're providing a general analysis of what the script does.
We asked for:
## Table of Contents
- [Overview](#overview)
- [Requirements](#requirements)
...
## Overview
...
## Requirements
...
What we're getting instead is:
This appears to be a bash script that performs various tasks...
Here are the key parts of the script:
* check_dependencies: This function checks...
The qwen2.5-coder:7b model appears to be better at following structured format instructions in the prompt. It's likely responding with the exact structure we requested in our prompt, including proper markdown headers, tables, etc.
-
Model-specific prompt engineering: Create customized prompts for each model type that emphasize format requirements
-
Post-processing responses: Add code to reformat/restructure the responses from these models to match our expected README format
-
Enhanced format instructions: Make the format requirements even more explicit in the prompt
-
Fallback mechanism: Implement a system that detects when a response doesn't have proper markdown structure and automatically retries with a different model
The most practical solution is to improve our prompt engineering specifically for these problematic models:
# For codellama:7b and deepseek-coder:latest
if [[ "$model" == "deepseek-coder:latest" || "$model" == "codellama:7b" || "$model" == "codellama:13b" ]]; then
system_prompt="You are an expert code documentarian tasked with producing professional documentation. YOUR RESPONSE MUST BE IN MARKDOWN FORMAT WITH THE EXACT SECTION HEADERS SPECIFIED BELOW. Do not deviate from this format. You must include Table of Contents, Overview, Requirements, Installation, Usage, Configuration, What the Script Does, Important Notes, Troubleshooting, and Disclaimer sections with appropriate markdown headers (##)."
fiThese models are functioning correctly at the API level. The challenge is getting them to produce responses in the specific markdown structure we need for a README. This is a prompt engineering challenge rather than an API issue. The recommendations above should help address this specific problem.