fraudsentinel-qwen3-14b-merged/README.md

---
language:
- en
license: apache-2.0
base_model: unsloth/Qwen3-14B
tags:
- text-generation
- fraud-detection
- anti-money-laundering
- financial-crime
- unsloth
- qwen3
- sft
- fraudsentinel
datasets:
- naazimsnh02/fraud-financial-crime-qwen3-sft-v2
---

# FraudSentinel — Qwen3-14B Merged (Tier-2 Intelligence Layer)

Full 16-bit merged model — Qwen3-14B base with the FraudSentinel LoRA adapter weights merged in. Suitable for direct deployment without PEFT or adapter management overhead. Part of the [FraudSentinel](https://huggingface.co/collections/naazimsnh02/fraudsentinel) two-tier platform.

If you prefer the compact LoRA adapter for hot-swapping or multi-tenant deployments, see [`naazimsnh02/fraudsentinel-qwen3-14b-lora`](https://huggingface.co/naazimsnh02/fraudsentinel-qwen3-14b-lora).

---

## Capabilities

This model is trained to act as an enterprise fraud detection and AML investigation assistant. It handles six task types:

- **Structured JSON risk scoring** — calibrated score (0.0–1.0), risk level, typology, key signals, feature importance, recommended action, SAR rationale
- **Explainable alerts** — evidence-grounded investigator-facing explanations tied to actual transaction features
- **Typology classification** — card-not-present fraud, account takeover, fan-out, gather-scatter, structuring, smurfing, and more
- **6-level recommended action** — `AUTO_APPROVE → APPROVE_WITH_MONITORING → STEP_UP_AUTH → TEMPORARY_HOLD → AUTO_BLOCK → SAR_REVIEW`
- **SAR drafting** — FinCEN-aligned Suspicious Activity Report narrative generation for human review
- **Multi-turn HITL dialogue** — investigator follow-up conversations with the model
- **Deep Analysis mode** — Chain-of-Thought reasoning via Qwen3's thinking tokens

---

## Training Details

| Property | Value |
|---|---|
| **Base model** | `unsloth/Qwen3-14B` (Apache-2.0) |
| **Fine-tuning method** | SFT + LoRA, merged to full bf16 weights |
| **LoRA rank / alpha** | 16 / 32 |
| **Target modules** | All linear layers (q, k, v, o projections + MLP gate/up/down) |
| **Trainable parameters (pre-merge)** | 64,225,280 (0.433% of 14.83B) |
| **Dataset** | `naazimsnh02/fraud-financial-crime-qwen3-sft-v2` (11,016 train examples) |
| **Epochs** | 2 |
| **Total steps** | 1,378 |
| **Effective batch size** | 16 (2 per device × 8 gradient accumulation) |
| **Learning rate** | 1e-4 (cosine decay, 5% warmup) |
| **Optimizer** | AdamW 8-bit |
| **Precision** | bfloat16 |
| **Max sequence length** | 4,096 |
| **Hardware** | AMD MI300X, 192 GB VRAM, ROCm 7.0 |
| **Framework** | Unsloth 2026.6.1, TRL 0.22.2 |
| **Train loss (final)** | 0.2467 |
| **Training time** | 70.5 min |
| **Peak VRAM** | 39.8 GB (20.8% of 192 GB) |

---

## Usage

### Transformers (no PEFT required)

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

model = AutoModelForCausalLM.from_pretrained(
    "naazimsnh02/fraudsentinel-qwen3-14b-merged",
    torch_dtype=torch.bfloat16,
    device_map="auto",
)
tokenizer = AutoTokenizer.from_pretrained("naazimsnh02/fraudsentinel-qwen3-14b-merged")
```

### Unsloth (2× faster inference)

```python
from unsloth import FastLanguageModel
import torch

model, tokenizer = FastLanguageModel.from_pretrained(
    model_name = "naazimsnh02/fraudsentinel-qwen3-14b-merged",
    max_seq_length = 4096,
    dtype = torch.bfloat16,
    load_in_4bit = False,
)
FastLanguageModel.for_inference(model)
```

### vLLM (production serving)

```bash
vllm serve naazimsnh02/fraudsentinel-qwen3-14b-merged \
  --dtype bfloat16 \
  --max-model-len 4096
```

### Inference Example

```python
messages = [
    {"role": "system", "content": "You are FraudSentinel, an expert fraud detection and AML investigation assistant."},
    {"role": "user", "content": (
        "Analyze this AML transaction and return a structured JSON risk assessment.\n\n"
        "Transfer: amount_paid=95000 USD, amount_received=94850 EUR, payment_format=ACH, "
        "sender_out_degree=47, sender_in_degree=3, receiver_in_degree=52, "
        "ccy_mismatch=True, is_round=False, is_laundering=True"
    )},
]

# Fast mode — thinking OFF (default for Tier-2 triage)
text = tokenizer.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True,
    enable_thinking=False,
)
inputs = tokenizer(text, return_tensors="pt").to(model.device)

with torch.no_grad():
    output = model.generate(
        **inputs,
        max_new_tokens=512,
        temperature=0.1,
        do_sample=True,
    )
print(tokenizer.decode(output[0][inputs["input_ids"].shape[1]:], skip_special_tokens=True))
```

**Deep Analysis mode** (Chain-of-Thought for complex or high-stakes cases):

```python
text = tokenizer.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True,
    enable_thinking=True,   # activates Qwen3 thinking tokens, adds ~3–5 s latency
)
```

---

## Output Schema (Structured Task)

```json
{
  "risk_score": 0.91,
  "risk_level": "CRITICAL",
  "conclusion": "SUSPICIOUS",
  "primary_typology": "layering / fan-in gather-scatter",
  "secondary_typology": "rapid_passthrough",
  "key_signals": [
    "high_receiver_in_degree",
    "cross_currency_conversion",
    "ach_channel_over_representation"
  ],
  "explanation": "Sender account shows unusually low inbound activity (in-degree 3) relative to high outbound fan-out (47 unique counterparties). Receiver account aggregates from 52 sources — consistent with layering...",
  "feature_importance": {
    "high_receiver_in_degree": 0.41,
    "cross_currency_conversion": 0.33,
    "ach_channel_over_representation": 0.26
  },
  "recommended_action": "SAR_REVIEW",
  "sar_required": true,
  "sar_rationale": "Transaction exhibits layering indicators — high-degree aggregation, cross-currency conversion, and ACH over-representation consistent with structuring."
}
```

---

## System Prompt

The model was trained with the following system prompt pattern:

```
You are FraudSentinel, an expert fraud detection and AML investigation assistant.
```

Consistent use of this system prompt at inference produces the most coherent structured outputs and action recommendations.

---

## Limitations

- Prototype/research use. Source data is synthetic/semi-synthetic. Do not use for real customer adjudication without independent validation, bias review, and human-in-the-loop controls.
- AI-generated SAR drafts require human review and edit before filing with FinCEN.
- The model was trained with thinking mode OFF. Enable it at inference for Deep Analysis; expect 3–5 s additional latency per response.
- Feature importance values reflect deterministic heuristics from the training data pipeline, not SHAP or gradient-based model explanations.
- The model is 14B parameters at bfloat16 (~28 GB). A GPU with at least 40 GB VRAM is required for full-precision inference; 4-bit quantization can reduce this to ~10 GB with some quality tradeoff.

---

## License

Apache-2.0 (Qwen3 base model and fine-tuning adapter).