Qwen3-4B-Thinking-2507-Heretic-CodeFeedback-Agentic-Tessa-8K-2048-Experimental-LoRA

Status

This is an experimental adapter kept for transparency, comparison, and future analysis.

In a small local Python coding benchmark, this adapter regressed compared with the previous CodeFeedback checkpoint.

Delta:

Observed behavior

The adapter did not fail completely, but it became worse at strict executable-code output.

Observed regressions:

• flatten failed with a type error.
• valid_parentheses failed to output executable code.
• lru_cache remained incomplete.
• The model showed more explanatory / agentic behavior instead of always returning compact executable code.

This suggests that the larger Agentic Tessa continuation pushed the model toward a more verbose agentic-assistant style, which may be useful for some workflows but is worse for strict code-output benchmarks.

Training configuration

Training result

Benchmark files

Benchmark artifacts are included under:

text
Copy code
benchmark/

Files:

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

Intended use

This adapter is intended for:

• comparison against the previous CodeFeedback checkpoint
• studying regression from larger agentic fine-tuning
• analyzing output-style drift
• future experiments with smaller learning rates or filtered datasets

It is not recommended for strict agentic coding workflows that require compact executable code output.

For the stronger current baseline, prefer:

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

Loading example

python
Copy code
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-8K-2048-Experimental-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.

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.