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:
Table with columns: Model, Adapter, Passed, Pass rate, Avg tokens/s| Model | Adapter | Passed | Pass rate | Avg tokens/s |
|---|
| Before | heretic_F_lora_python5000_codefeedback5000 | 9/10 | 90.00% | 7.80 |
| After | heretic_F_lora_tessa_agentic_1000_test | 9/10 | 90.00% | 7.86 |
Delta:
Table with columns: Metric, Value| Metric | Value |
|---|
| Passes | 0 |
| Pass rate | 0.00% |
| Avg tokens/s | +0.05 |
Unlike the OpenCodeInstruct continuation experiment, this Tessa-based adapter did not regress on the small strict-code benchmark.
Training configuration
Table with columns: Item, Value| Item | Value |
|---|
| Base model | JoaoZaokk/Qwen3-4B-Thinking-2507-Heretic-CodeFeedback |
| Input adapter | heretic_F_lora_python5000_codefeedback5000 |
| Dataset | smirki/Agentic-Coding-Tessa |
| Samples used | 1,000 |
| Sequence length | 1024 |
| Epochs | 1 |
| Learning rate | 1e-6 |
Benchmark files
Benchmark artifacts are included under:
Files:
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
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.