python-vibe-0.5b

LoRA adapters (step 100) on Qwen2.5-Coder-0.5B-Instruct, 4-bit MLX, for short Python drafts. Owned by YauhenBichel.

These weights are a style prior, not a coding agent. They shape how a draft is written. They do not plan, explore a repository, or use tools well. Read the measurements below before choosing them for anything.

The code is at github.com/YauhenBichel/python-vibe.

What python-vibe is

A deterministic harness around a small local model. The model proposes; the harness decides what is allowed to happen. Everything except the model call is ordinary Python with no third-party dependencies, so the same behaviour is reproducible and testable without a GPU, a token, or a network.

What the harness does on its own, before and around any model output:

• Restricts writes. Every change is resolved inside the project directory, is checked for syntax, and leaves a .bak. A rewrite that shrinks a file by more than a third is refused.
• Finds the file first. When a task names a file, that file is opened and the model is told to change it and no other. When it names a symbol, the symbol is searched for before the model's first turn.
• Refuses the common failure. A question that tries to edit, a repeated search that cannot teach it anything, an answer that repeats an instruction back instead of answering, a task finished with nothing changed.
• Asks when the task is unclear. A request that names no file and no function is put back to the user rather than guessed at.
• Reports structure. Import cycles, ungrouped packages, oversized modules and missing tests, worst first, with one change named.

Which model to use

Model	Role
An 8B such as llama3.1:8b through Ollama	Everyday explore, edit and run
These 0.5B adapters	Short single-file drafts, and harness smoke tests

The 0.5B adapters are published so the harness can be demonstrated and tested by anyone, at no cost and with no GPU. They are not the everyday model.

Install

Works the same on macOS, Linux and Windows. The harness needs only the standard library.

git clone https://github.com/YauhenBichel/python-vibe.git
cd python-vibe
pip install -e .

python-vibe brief  ./your-project      # summary, no model
python-vibe layout ./your-project      # structure report, no model
python-vibe ask    ./your-project "what does compute_total return?"
python-vibe run    ./your-project "add multiply(a, b) and a unit test"

from pathlib import Path
from harness import Agent, AgentOptions

result = Agent(AgentOptions(project=Path("~/app"))).run("fix the NameError")
result.summary, result.writes, result.refusals

AgentOptions(allow_writes=False) makes a run read-only: no file is changed and the model is told so.

Using these adapters

hf download YauhenBichel/python-vibe-0.5b --local-dir adapters/python-vibe

from mlx_lm import load, generate

model, tokenizer = load(
    "mlx-community/Qwen2.5-Coder-0.5B-Instruct-4bit",
    adapter_path="adapters/python-vibe",
)

MLX is macOS only. Elsewhere, ollama pull qwen2.5-coder:0.5b runs the base model through the same harness, not these adapters.

adapters.safetensors is step 100, not the last step: a longer run overfit after that checkpoint.

Base weights: Qwen/Qwen2.5-Coder-0.5B-Instruct, Apache-2.0.

Which model to run it with

Three local models were measured on the same eleven jobs, each checked by running the code afterwards. One laptop, 29 August 2026, through Ollama.

Model	Write a test, add a component, fix a bug	Platform work
llama3.1:8b	9 / 9	1 / 4
qwen2.5-coder:7b	7 / 9	2 / 4
qwen3coder (30B)	not run	0 / 4, every case timed out

A code-specialised 7B is better at operations work and worse at everything else. A 30B does not finish a single task on this hardware. The 8B stays the default.

These adapters are not in that table on purpose. They are a style prior: they draft one short file, and they miss the Action: lines the loop needs.

What the harness does that the model does not

The cases that pass every time are the ones finished without asking a model at all:

cover-discount   steps=0   0.2s
fix-nameerror    steps=0   0.1s

A misspelled name beside the right one, a missing import for a well-known module, a test appended to a file that already has one — those are compiler jobs, and doing them deterministically means they cannot be got wrong.

What still fails is reasoning, not formatting: a flag reader that does not treat "0" as false, a retry that never calls what it was given. That is the argument against reaching for more training first — the protocol is not where these runs fail.

What was measured

Full write-up: which model · research-vibe-review.

• About 45 training pairs. Validation was best near step 100, which is what this repository ships.
• Held-out tasks (print a weekday, count .md files, add a docstring) run through the harness, but the Python is often wrong. A style prior, not a reliable one.
• A real repository does not fit in the context window. Review is one small file at a time, roughly 200 to 2500 bytes.
• One hundred files reviewed as "no issues" is not a review. Read scratch/batch-review.jsonl before keeping any --fix write.

Faults found by pointing the harness at its own repository, and fixed in it:

• A fixture path in the system prompt made an 8B create pkg/mathy.py inside unrelated projects.
• A question was "answered" by pasting the instruction back; the check that should have caught it matched a type name inside the instruction's own example.
• Told to fix a named file, the model patched a different one, because the harness searched for a word taken from the directory path instead of opening the file it had been given.

Open questions: 45 pairs vs style prior · guard evasion.