---
license: apache-2.0
base_model: unsloth/Qwen2.5-Coder-7B-Instruct
library_name: peft
pipeline_tag: text-generation
tags:
  - code
  - code-review
  - security
  - qwen2.5-coder
  - lora
  - bad-theory-labs
model_name: btl-2-coder-7B
---

# BTL-2 Coder 7B

BTL-2 Coder 7B is a LoRA adapter for `unsloth/Qwen2.5-Coder-7B-Instruct`, trained for structured code-review findings.

Code and evaluation scripts are available at:

```text
https://github.com/Badtheorylabs/btl-2-coder
```

## Intended Use

This adapter is intended for local-first code review. It is trained to produce structured findings with:

- severity
- file path
- line number
- title
- evidence
- recommendation
- numeric confidence

The main supported issue classes are SQL injection, path traversal, authorization bypass, missing error handling, boundary/off-by-one logic, and related security/correctness findings.

The adapter is optimized for review output rather than broad chat behavior.

## Training

- Base model: `unsloth/Qwen2.5-Coder-7B-Instruct`
- Method: LoRA SFT with Unsloth
- Data mix: `4,000` API-generated review traces + `1,000` template traces
- Train/eval split: `4,500` train examples + `500` eval examples
- Epochs: `2`
- Max sequence length: `4096`

Only redacted, opt-in traces should be used for future training.

## Recommended Prompt Contract

Use strict schema prompting:

```text
Return only a JSON array. No markdown and no wrapper object.
Each finding must include: severity, file, line, title, evidence, recommendation, confidence.
severity must be exactly one of: critical, high, medium, low.
Never put a category in severity.
confidence must be a number from 0 to 1, never a string label.
Every finding must include concrete evidence and a non-empty recommendation.
```

Example output:

```json
[
  {
    "severity": "critical",
    "file": "src/users.ts",
    "line": 42,
    "title": "SQL injection through string-built query",
    "evidence": "The user id is concatenated directly into the SQL string.",
    "recommendation": "Use a parameterized query.",
    "confidence": 0.96
  }
]
```

## Load The Adapter

```python
from peft import PeftModel
from transformers import AutoModelForCausalLM, AutoTokenizer

base = "unsloth/Qwen2.5-Coder-7B-Instruct"
adapter = "badtheorylabs/btl-2-coder"

tokenizer = AutoTokenizer.from_pretrained(adapter)
model = AutoModelForCausalLM.from_pretrained(base, device_map="auto")
model = PeftModel.from_pretrained(model, adapter)
```

## Evaluation

Measured on an NVIDIA H200 with 4-bit adapter inference.

| Eval | JSON parse | Schema valid | Numeric confidence | Category hit | File hit | Precision | Recall | Weighted severity recall |
| --- | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: |
| Heldout 100 strict | 1.000 | 0.952 | 1.000 | 0.783 | 0.840 | n/a | n/a | n/a |
| Heldout 30 strict v2 | 1.000 | 0.975 | 1.000 | 0.867 | 0.867 | n/a | n/a | n/a |
| Seeded 15 strict | 1.000 | 1.000 | 1.000 | 0.933 | 1.000 | 0.933 | 0.933 | 0.956 |

Notes:

- Heldout precision/recall is marked `n/a` because the heldout set is broader and does not use one normalized ground-truth finding per example.
- The seeded benchmark is a controlled regression suite with known findings.
- Reported results use the recommended strict schema prompt.

## Scope

- Primary task: structured security and correctness review.
- Output format: JSON findings with severity, location, evidence, recommendation, and confidence.
- Best runtime path: strict schema prompting, with optional constrained decoding.
- Evaluation focus: code-review findings, file hits, schema validity, and seeded precision/recall.
- Next track: patch proposals and terminal workflows.

## Files

This repository contains a PEFT/LoRA adapter:

- `adapter_model.safetensors`
- `adapter_config.json`
- `tokenizer.json`
- `tokenizer_config.json`
- `chat_template.jinja`
- `training_args.bin`
- `SHA256SUMS`