JoaoZaokk/Qwen3-4B-Thinking-2507-Heretic-CodeFeedback-Agentic-Tessa-1K-LoRA

Qwen3 4B Thinking 2507 Heretic CodeFeedback — Agentic Tessa 1K LoRA

This repository contains an experimental LoRA adapter trained on top of:

`JoaoZaokk/Qwen3-4B-Thinking-2507-Heretic-CodeFeedback`

This adapter is a small continuation experiment using:

`smirki/Agentic-Coding-Tessa`

The goal was to test whether a small amount of agentic coding data could improve or preserve coding behavior without degrading strict code-output performance.

Status

This is a candidate / experimental adapter, not a claimed major improvement.

I'll be testing some datasets to make the model better for coding, it a tiny improvement, not a game changer, but compared to the previous one this model didn't get worse.

In a small local Python coding benchmark, this adapter preserved the previous score:

Delta:

Unlike the OpenCodeInstruct continuation experiment, this Tessa-based adapter did not regress on the small strict-code benchmark.

Training configuration

Benchmark files

Benchmark artifacts are included under:

~~~text benchmark/ ~~~

Files:

~~~text benchmark/beforesummary.md benchmark/aftersummary.md benchmark/COMPARISON.md benchmark/beforeresults.jsonl benchmark/afterresults.jsonl ~~~

Intended use

This adapter is intended for testing:

• —agentic coding behavior
• —coding assistance
• —code generation
• —code explanation
• —tool-use style coding responses
• —continued fine-tuning experiments

It should be compared against the main CodeFeedback model before use in any serious coding workflow.

Loading example

~~~python from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig from peft import PeftModel import torch

base_model = "JoaoZaokk/Qwen3-4B-Thinking-2507-Heretic-CodeFeedback" adapter = "JoaoZaokk/Qwen3-4B-Thinking-2507-Heretic-CodeFeedback-Agentic-Tessa-1K-LoRA"

tokenizer = AutoTokenizer.frompretrained(basemodel, trustremotecode=True)

bnbconfig = BitsAndBytesConfig( loadin4bit=True, bnb4bitquanttype="nf4", bnb4bitcomputedtype=torch.float16, bnb4bitusedouble_quant=True, )

model = AutoModelForCausalLM.frompretrained( basemodel, quantizationconfig=bnbconfig, devicemap="auto", trustremote_code=True, )

model = PeftModel.from_pretrained(model, adapter) model.eval() ~~~

Important notes

This is an experimental LoRA adapter.

The benchmark used here is small and should not be treated as a formal coding leaderboard. It is mainly useful for local before/after regression testing.

This adapter preserved the current local benchmark score, but further testing is needed before treating it as a better general-purpose coding model.