LLM Benchmarking for Tasks

does this exist: What slows me down the most when testing a new LLM application locally is the fact that some models will work for certain things better than others, like tool calling or outputing JSON correctly. So when it does not work I have to think to myself, would this work if I were using SOTA and not local models, or am I just an idiot and made a mistake. When I think it is just the model I end up retesting with different models I have locally installed. Perhaps a benchmark where you could see which local models are proficient at different tasks, such as outputting JSON and tool calling, would be helpful to me. Then I could easily pick a model for testing purposes rather than just guessing. It probably already exists.

Yes, something like this does exist, though not necessarily in the exact form you're describing. Here are a few resources that might be helpful: ### 1. **Open LLM Leaderboard (Hugging Face)** - [https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard) - Benchmarks various open-weight models on tasks like MMLU, TruthfulQA, and others. - Doesn't focus specifically on JSON output or tool calling but gives a general ranking of model capability. ### 2. **lm-evaluation-harness (EleutherAI)** - [https://github.com/EleutherAI/lm-evaluation-harness](https://github.com/EleutherAI/lm-evaluation-harness) - A framework for benchmarking LLMs across various tasks. You could extend it to test JSON formatting or tool-calling reliability. ### 3. **ARENA Hard Prompts Leaderboard** - [https://huggingface.co/spaces/teknium/OpenHermes-ARENA-Hard](https://huggingface.co/spaces/teknium/OpenHermes-ARENA-Hard) - Tests various models using challenging prompts, sometimes revealing issues with consistency in formatting and structured outputs. ### 4. **Toolformer-Style Benchmarks (Function Calling Tests)** - Some people benchmark models for tool-calling performance, but there isn't a widely adopted leaderboard for this. - You might find discussions or experiments in places like `r/LocalLLaMA` or the Hugging Face forums. If you're interested in something more specific (e.g., JSON adherence and function calling), you might have to roll your own benchmarking framework. A script that runs structured tests across your installed models and logs success rates could help automate your testing process. Would you be interested in setting up a small, reproducible benchmark for local models focused on JSON correctness and tool use?

yes

Great! Setting up a benchmark to test local models for specific tasks like outputting JSON correctly and tool calling is a good way to streamline your workflow. Here’s a simple plan you can follow to get this going: ### 1. **Choose Your Tasks** We'll focus on the two key tasks: - **JSON Output:** Ensuring that the model formats its output in valid JSON when requested. - **Tool Calling:** Testing the model’s ability to call functions or tools correctly (e.g., making API requests, calling functions, etc.). ### 2. **Define Test Cases** For each task, define a set of test cases with clear inputs and expected outputs. #### JSON Output Tests: - **Test Case 1:** Request the model to output structured data (e.g., a dictionary or list) as JSON. - **Input:** "Please give me a list of 3 countries and their populations in JSON format." - **Expected Output:** ```json { "countries": [ {"name": "USA", "population": 331000000}, {"name": "Canada", "population": 38000000}, {"name": "India", "population": 1393409038} ] } ``` - **Test Case 2:** Ensure the model escapes special characters and handles errors. - **Input:** "Please provide a JSON object with the key 'message' and value 'Hello, world!'" - **Expected Output:** ```json { "message": "Hello, world!" } ``` #### Tool Calling Tests: - **Test Case 1:** Request the model to call a specific tool or function (e.g., a weather API, calculator function). - **Input:** "Use the weather tool to get the current temperature in Paris." - **Expected Output:** (Assuming you have a mock API or predefined function): ```json { "tool": "weather_api", "response": {"city": "Paris", "temperature": "16°C"} } ``` - **Test Case 2:** Test the model’s ability to handle errors in tool calling. - **Input:** "Call the stock price API for Apple Inc." - **Expected Output:** ```json { "error": "API call failed, please check your connection or the service." } ``` ### 3. **Set Up the Benchmarking Script** Here’s a Python script that could help automate testing. The script will iterate through a list of test cases, call each local model, and check if the output is valid JSON or whether the tool call succeeds. #### Example Script: ```python import json import subprocess # Define your local models and test cases local_models = ["model_1", "model_2", "model_3"]

now just explain in a paragraph

To create a benchmark for testing local models, you’ll define specific tasks like ensuring correct JSON output and validating tool-calling functionality. For JSON output, you'll test if the model can return structured data (like lists or dictionaries) in valid JSON format, while also handling special characters and errors. For tool-calling, you'll test the model's ability to call functions or APIs, and ensure it handles both successful and error responses correctly. A Python script can automate the process, running each test case for each model, and checking if the output is valid JSON or if the tool call was successfully made. This benchmark will allow you to quickly evaluate which local models perform best for these tasks.