Jan-Code-4B: A Lightweight Code-Tuned Model for Local Development
Jan-Code-4B Release Details
The Jan team has released Jan-Code-4B, a small code-tuned model built on Jan-v3-4B-base-instruct. This experimental model targets day-to-day coding assistance tasks including code generation, edits/refactors, basic debugging, and writing tests while maintaining a lightweight footprint suitable for local execution.
Intended Use and Performance
Jan-Code-4B is designed as a drop-in replacement for the Haiku model in Claude Code. On coding benchmarks, it shows small improvements over the baseline model and generally feels more reliable for coding-oriented prompts at this size.
How to Run Jan-Code-4B
Setup via Jan Desktop:
- Download Jan Desktop from https://www.jan.ai/
- Download Jan-Code via Jan Hub
Claude Code Integration:
- Jan makes it easier to connect Claude Code to any model
- Replace Haiku model with Jan-Code-4B
Model Links and Parameters
Model downloads:
- Jan-Code: https://huggingface.co/janhq/Jan-code-4b
- Jan-Code-GGUF: https://huggingface.co/janhq/Jan-code-4b-gguf
Recommended parameters:
- Temperature: 0.7
- Top_p: 0.8
- Top_k: 20
The Jan team credits u/Alibaba_Qwen for the base model and u/ggerganov for llama.cpp contributions.
📖 Read the full source: r/LocalLLaMA
👀 See Also
Introducing Roam-Code CLI: A Faster, Deterministic Alternative for Code Exploration
Roam-Code CLI replaces Claude Code's explore phase with a faster, deterministic alternative that indexes codebases for improved efficiency.
Parallel Coding Agents with tmux and Markdown Specs
Manuel Schipper describes a system for running 4-8 parallel coding agents using tmux, Markdown files, bash aliases, and six slash commands. The setup uses Feature Design (FD) Markdown specs tracked through an 8-stage lifecycle.
Comparing Multi-Agent AI Systems: Anthropic's Harness vs Agyn's Engineering Org Model
Anthropic published a harness design for long-running application development, while Agyn's multi-agent system for team-based autonomous software engineering was open-sourced last month. Both systems reject monolithic agents in favor of role separation, structured handoffs, and review loops.
Multi-Agent Memory: Open Source Shared Memory System for AI Agents
Multi-Agent Memory is an open source project that provides a shared memory system for AI agents across different machines, tools, and frameworks. It supports four distinct memory types with specific behaviors and includes features like credential scrubbing, agent isolation, and LLM consolidation.