OmniCoder-9B: 9B Parameter Coding Agent Fine-Tuned on 425K Agentic Trajectories

Tesslate has released OmniCoder-9B, a 9-billion parameter coding agent model fine-tuned on top of Qwen3.5-9B's hybrid architecture. The architecture uses Gated Delta Networks interleaved with standard attention.

Training Data and Sources

The model was trained on 425,000+ curated agentic coding trajectories spanning real-world software engineering tasks. The training data was specifically built from Claude Opus 4.6 agentic and coding reasoning traces, targeting scaffolding patterns from:

• Claude Code
• OpenCode
• Codex
• Droid

The dataset includes successful trajectories from models like Claude Opus 4.6, GPT-5.4, GPT-5.3-Codex, and Gemini 3.1 Pro.

Key Features

• Trained on Frontier Agent Traces: Built from Claude Opus 4.6, GPT-5.3-Codex, GPT-5.4, and Gemini 3.1 Pro agentic coding trajectories across Claude Code, OpenCode, Codex, and Droid scaffolding
• Hybrid Architecture: Inherits Qwen3.5's Gated Delta Networks interleaved with standard attention for efficient long-context processing
• 262K Native Context: Full 262,144 token context window, extensible to 1M+
• Error Recovery: Learns read-before-write patterns, responds to LSP diagnostics, and applies minimal edit diffs instead of full rewrites
• Thinking Mode: Supports <think>...</think> reasoning chains for complex problem decomposition
• Apache 2.0: Fully open weights, no restrictions

Agentic Behavior

The model shows strong agentic behavior learned directly from the real-world agent trajectories it was trained on. It recovers from errors using read-before-write patterns, responds to LSP diagnostics, and uses proper edit diffs instead of full rewrites.

The model is available at https://huggingface.co/Tesslate/OmniCoder-9B.