---
base_model: unsloth/Qwen2.5-7B-Instruct-bnb-4bit
library_name: peft
tags:
  - lora
  - qwen2.5
  - cyber-investigation
  - cyber-investigator
  - filipino
  - taglish
language:
  - en
  - fil
license: other
---

# Cybercop AI

A LoRA adapter that turns **Qwen2.5-7B-Instruct (4-bit)** into a focused **cyber-investigation assistant** — trained to produce structured, procedure-oriented responses for cybercrime / online-scam / digital-forensics triage and reporting, in **English and Taglish**.

> **Intended use:** Exclusive internal use as an investigative-aid assistant. The adapter is a decision-support tool, not an authority — all outputs must be reviewed by a qualified human investigator before any action.

## Model details

| Field | Value |
|---|---|
| Base model | `unsloth/Qwen2.5-7B-Instruct-bnb-4bit` |
| Adapter type | LoRA (PEFT) |
| LoRA rank `r` | 16 |
| LoRA alpha | 32 |
| LoRA dropout | 0.05 |
| Target modules | `q_proj, k_proj, v_proj, o_proj, gate_proj, up_proj, down_proj` |
| Precision | 4-bit base (bitsandbytes NF4) + bf16 training |
| Training data | `jhenberthf/cyber-investigator` (Alpaca format, 99 rows) |
| Epochs | 2 |
| Max sequence length | 512 |
| Trainable params | ~40.4M (0.53% of base) |

## Usage

> **Prompt format (important):** the adapter was trained on Alpaca-format
> (`### Instruction / ### Input / ### Response`) text. Wrap that text inside a
> single Qwen chat-template user turn — do **not** feed raw Alpaca text, or the
> base Instruct model will echo the instruction and drift off-topic.

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

base = "unsloth/Qwen2.5-7B-Instruct-bnb-4bit"
adapter = "jhenberthf/cybercop-ai"          # local path or "jhenberthf/cybercop-ai"

tokenizer = AutoTokenizer.from_pretrained(base)
if tokenizer.pad_token is None:
    tokenizer.pad_token = tokenizer.eos_token
model = AutoModelForCausalLM.from_pretrained(
    base, device_map={"": "cuda:0"}, torch_dtype=torch.bfloat16
)
model = PeftModel.from_pretrained(model, adapter)
model.eval()

instruction = ("You are a cyber-investigation assistant. Given a complaint, "
               "classify the likely cybercrime type, list immediate preservation "
               "steps, and outline the next investigative actions.")
inp = ("Victim reports being tricked into sending PHP 50,000 via GCash to a "
       "suspect after a 'customer service' impostor promised a refund for a "
       "purchase that was never delivered. The suspect account is now inactive.")

# Alpaca-format text wrapped as a single chat user turn
alpaca = f"### Instruction:\n{instruction}\n\n### Input:\n{inp}\n\n### Response:\n"
prompt = tokenizer.apply_chat_template(
    [{"role": "user", "content": alpaca}],
    tokenize=False, add_generation_prompt=True,
)

inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
out = model.generate(
    **inputs, max_new_tokens=512, do_sample=False, temperature=1.0,
    repetition_penalty=1.05,
)
text = tokenizer.decode(out[0], skip_special_tokens=True)
# strip the prompt prefix, keep only the generated Response
gen = text[len(tokenizer.decode(inputs["input_ids"][0], skip_special_tokens=False)):]
resp = gen.split("### Response:")[-1].strip() if "### Response:" in gen else gen.strip()
print(resp)
```

## Training notes

- Trained locally on a single consumer GPU (RTX 3050 6GB) via QLoRA.
- The dataset is small (99 curated examples); the adapter specializes tone, structure, and procedure rather than broad world knowledge.
- Validation was light (no held-out split). Treat outputs as draft material.

## Limitations & caveats

- **Not legal/operative authority.** Outputs are suggestions; verify against current procedure before acting.
- **Possible hallucination** on unfamiliar schemes, jurisdictions, or technical specifics — always corroborate.
- Small training set → limited coverage; may underperform on domains absent from the source data.
- LoRA only modifies a tiny fraction of weights; base-model limitations (bias, knowledge cutoff) still apply.
- 4-bit base can reduce factual precision vs. a full-precision model.

## Responsible use

- Keep a human in the loop for any investigative or evidentiary decision.
- Do not present outputs as final findings without review.
- Red-team for leakage of operational detail before deployment.

## License

Adapter weights released under a restrictive/internal-use understanding. Base model terms from `unsloth/Qwen2.5-7B-Instruct-bnb-4bit` and Qwen2.5 apply to the underlying weights.