Model Overview

M-Alkassem/qwen2.5-coder-3b-final-merged is a 3.1 billion parameter model built upon the Qwen/Qwen2.5-Coder-3B-Instruct base. It represents the culmination of a two-stage fine-tuning process, designed to enhance its capabilities for agent-oriented coding tasks. The model has a context length of 32768 tokens.

Key Capabilities

• Agentic Workflow Optimization: Specifically fine-tuned to function as the reasoning core within lightweight coding agents, supporting constrained tool-using workflows.
• Two-Stage Fine-Tuning: Underwent initial coding-focused fine-tuning using the bigcode/self-oss-instruct-sc2-exec-filter-50k dataset, followed by agent-oriented continued fine-tuning on the ernie-research/MEnvData-SWE-Trajectory dataset.
• Code Remediation: Demonstrated ability to run failing tests, identify bugs, rewrite code, and re-run tests until success within an agent workflow.

Intended Use Cases

This model is particularly suited for:

• Lightweight Coding Agents: Serving as the core intelligence for automated code generation, debugging, and testing agents.
• Tool-Using Workflows: Applications requiring a language model to interact with external tools and execute specific actions based on reasoning.

While the base model showed stronger performance in direct, plain answer-only benchmarks, this merged model's value lies in its specialized alignment for agentic and tool-constrained environments.