Strengths3
- Clear experimental design with honest analysis of results and failure cases.
- Well-documented reproduction steps and modular code structure.
- Minimal external dependencies for non-training components (only Node.js built-ins).
Training Experiment · JavaScript
flisk-model
Can a small model write correct Flisk config files? A fair 62-question test, results for Claude Haiku/Sonnet, Qwen and a LoRA trained on a MacBook M1.
An organisation's project: the project boards rank individual builders, so it is shown here without a rank. Among builders' projects it would sit 56th in NLP & LLMs.
What it is
This repository trains and evaluates a small language model (Qwen2.5-Coder 1.5B) to generate Flisk configuration files, comparing its performance to larger models and analyzing failure cases.
Merit describes the code at this commit. Later pushes count once the owner rescans. How merit is scored
Where it counts
- NLP & LLMs90%
- Machine Learning80%
- Data Science30%
- MLOps20%
How much of the project is about each field, as scoring counts it. It ranks in the field it is most about.
What the judge found
Read from the project's code at its scored commit.
Limitations
The owner keeps this project's limitations private.
Where it would sit on the NLP & LLMs board
Whole board- 54, shared rankAutostock-AI-agent-platformAgent System · TypeScript56.0ImplementationIrine MiltonNLP & LLMs
- 54, shared rankGWIQ-atlasAI Developer Tool · Python56.0ImplementationRickNLP & LLMs
- 56freeclawAgent System · Python55.3Implementationjerry howellNLP & LLMs
- 56, shared rankflisk-modelTraining Experiment · JavaScript55.3ImplementationfliskNLP & LLMs
- 58gemdeskML Application · Python55.0Implementationjerry howellNLP & LLMs
