Laplaces-Red-Devils/fol-v04-cot-augmented-fol-pretrain-malls-qwen2.5-3

Model Overview

This model, fol-v04-cot-augmented-fol-pretrain-malls-qwen2.5-3, is a fine-tuned version of the Qwen2.5-3B architecture, specifically designed for translating natural language premises into First-Order Logic (FOL). Its primary function is to convert natural language statements into a structured JSON format, populating the premises_fol field.

Key Capabilities

• Natural Language to FOL Translation: Excels at transforming natural language premises into formal First-Order Logic expressions.
• Structured Output: Generates FOL representations within a JSON structure, specifically for the premises_fol key.
• Fine-tuned Performance: Underwent Supervised Fine-Tuning (SFT) with specific hyperparameters, including a max_seq_length of 2048 and 15 training epochs.

Training Details

The model was fine-tuned from Laplaces-Red-Devils/fol-pretrain-malls-qwen2.5-3 using Unsloth for efficiency. While specific benchmark exact match results before and after fine-tuning are not provided in the summary, the model's purpose is clearly defined by its FOL translation task. Inference latency for greedy decoding averaged 23.385 seconds per sample on a test split of 30 samples.

Use Cases

This model is particularly suited for applications requiring:

• Automated logical reasoning systems.
• Knowledge representation and extraction from text.
• Formal verification and semantic parsing tasks where natural language needs to be converted into a formal logical structure.