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

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
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benchmark/

Files:

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benchmark/before_summary.md
benchmark/after_summary.md
benchmark/COMPARISON.md
benchmark/before_results.jsonl
benchmark/after_results.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
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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-Agentic-Tessa-1K-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.

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.