LocalCodeViber

LocalCodeViber 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.

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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

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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:

Dataset	Examples	Focus
TeichAI/convo-v1	777	Conversational format, instruction following
AlicanKiraz0/Agentic-Chain-of-Thought-Coding-SFT-Dataset-v1.1	~3,700	Agentic reasoning and tool use
TeichAI/MiniMax-M2.1-Code-SFT	~1,300	Agentic Code generation
TeichAI/MiniMax-M2.1-8800x	8,800	Diverse coding tasks
TeichAI/claude-4.5-opus-high-reasoning-250x	250	High-quality reasoning traces

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.

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Tools

LocalCodeViber understands the following tool schema out of the box:

["read_file", "write_file", "edit_file", "list_directory", "search_code", "run_command", "web_search"]

These match the tools in the training data. Pass them via the standard OpenAI tool calling API.

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Usage

LM Studio (Recommended)

1. Download the GGUF version: Bob-the-Koala/LocalCodeViber-GGUF
2. Load in LM Studio and break free from API costs!

Ollama

ollama run hf.co/Bob-the-Koala/LocalCodeViber-GGUF:Q4_K_M

Transformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained(
    "Bob-the-Koala/LocalCodeViber",
    torch_dtype="auto",
    device_map="auto"
)
tokenizer = AutoTokenizer.from_pretrained("Bob-the-Koala/LocalCodeViber")

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GGUF Versions

Available in Bob-the-Koala/LocalCodeViber-GGUF:

Quantization	Size	Use case
Q4_K_M	~4.8 GB	Everyday use, best balance

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System Prompt

For best results, use this system prompt:

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.

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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

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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

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Acknowledgements

LocalCodeViber was trained using Unsloth and would not exist without the datasets provided by TeichAI and AlicanKiraz0.

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License

This model is released under the Apache 2.0 license

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Built by Bob-the-Koala