LocalCodeViber v0.1 is a local-first agentic coding model built on Qwen3-8B, fine-tuned for tool-calling, multi-step code generation, and autonomous error recovery. Designed to run entirely on consumer hardware — no API, no cloud, no cost per token.
This is the SFT foundation model. Reinforcement learning is ongoing.
What it does
LocalCodeViber was trained to operate as a coding agent — not just generate code, but use tools to read files, write files, run commands, search the web, and recover from failures just like a real developer would.
It can:
Read and edit files in a workspace
Write complete, working code from a single prompt
Execute shell commands and interpret the output
Recover from failed tool calls without giving up
Create pull requests on GitHub repositories
Think through problems step by step using native <think> tags before acting
Model Details
Base Model
Qwen3-8B-Base
Architecture
Qwen3 transformer, 36 layers
Training Data
LocalCodeViber was trained on a curated mix of 14,837 examples across 5 datasets:
The dataset mix emphasises real agentic tool-use patterns including failed tool calls that are identified, diagnosed, and corrected — giving the model genuine error recovery capability rather than just pattern matching on success cases.
Tools
LocalCodeViber understands the following tool schema out of the box:
You are a helpful coding assistant with access to file operations and code analysis tools.
Complete the user's task thoroughly and efficiently.
When given a coding task, create working code files in the workspace.
Limitations
Base model started from bnb-4bit weights — quality ceiling is below a full precision 8B model
SFT only — reinforcement learning is in progress and will significantly improve reasoning quality
Not suitable for tasks requiring knowledge past Qwen3's training cutoff
Roadmap
LocalCodeViber-RL — reinforcement learning on top of this SFT base, optimising for code correctness and task completion
LocalCodeViber-Claw — fine-tuned specifically for OpenClaw skill schemas, channel routing, extra safety, and memory system
LocalCodeViber-14B — same training recipe on Qwen3-14B for substantially higher capability
Acknowledgements
LocalCodeViber was trained using Unsloth and would not exist without the datasets provided by TeichAI and AlicanKiraz0.
License
This model is released under the Apache 2.0 license
