Qwen2.5-coder-minimax-lora
Model Overview
qwen2.5-coder-minimax-lora is a LoRA fine-tuned version of Qwen2.5-Coder-7B, optimized for algorithmic reasoning and structured code generation tasks.
The model was fine-tuned on the MiniMax-M2.1-Code-SFT dataset to improve recursive reasoning, game-tree evaluation, and Minimax-based algorithm implementations.
This project demonstrates parameter-efficient fine-tuning (PEFT) using LoRA with 4-bit quantization for memory-efficient training.
🔹 Base Model
Base: Qwen/Qwen2.5-Coder-7B
Architecture: Decoder-only Transformer
Specialization: Code generation
Quantization: 4-bit (QLoRA during training)
🔹 Capabilities
The model demonstrates improved performance in:
Recursive algorithm generation
Minimax implementation
Game-tree reasoning
Backtracking logic
Structured Python code output
Algorithmic problem solving
🔹 Limitations
Fine-tuned on a relatively small subset (200 samples).
Optimized primarily for algorithmic reasoning tasks.
May still exhibit base-model behavior for unrelated domains.
Does not include reinforcement learning alignment.
