Intended purpose

This LoRA is kept and published as an experimental branch for:

• code explanation
• learning-oriented coding assistance
• understanding programming problems
• step-by-step reasoning around code
• comparing OpenCodeInstruct-style behavior against a stricter code-output model

It is not ideal for:

• agentic coding
• test-driven code generation
• benchmark-style exact function output
• tools that require the model to return only executable code
• coding agents that must avoid prose/explanation unless asked

Why this is not the main version

A small local before/after Python code benchmark showed that this OpenCodeInstruct continuation reduced exact-code benchmark performance.

Delta:

The post-training adapter was worse on strict executable-code tasks, especially when the expected output was a compact Python function or class.

However, this does not mean the adapter is useless. It likely shifted behavior toward a more explanatory, learning-oriented style. That can be useful for users who want to understand code, reason through tasks, or receive more guided programming explanations.

Training configuration

Training result

The training run completed successfully after reducing sequence length and using a more conservative GPU configuration.

Benchmark files

The local benchmark artifacts are included in this repository under:

text
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benchmark/

Files:

text
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benchmark/before_summary.md
benchmark/after_summary.md
benchmark/COMPARISON.md
benchmark/comparison.json
benchmark/before_results.jsonl
benchmark/after_results.jsonl

Recommended usage

Use this adapter when you want a model that may be more comfortable explaining code and reasoning through programming tasks.

For stricter agentic coding or benchmark-style executable output, prefer the original merged CodeFeedback model:

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

Loading example

python
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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-OpenCodeInstruct-Learning-LoRA"

tokenizer = AutoTokenizer.from_pretrained(base_model, trust_remote_code=True)

bnb_config = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_quant_type="nf4",
    bnb_4bit_compute_dtype=torch.float16,
    bnb_4bit_use_double_quant=True,
)

model = AutoModelForCausalLM.from_pretrained(
    base_model,
    quantization_config=bnb_config,
    device_map="auto",
    trust_remote_code=True,
)

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

Important notes

This is an experimental LoRA adapter.

It should not be treated as a universal improvement over the previous CodeFeedback model. It is published for transparency, comparison, and reproducibility.

The benchmark results suggest that it is worse for strict agentic coding, but potentially useful for learning-oriented coding assistance.