203 lines
6.9 KiB
Markdown
203 lines
6.9 KiB
Markdown
---
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language:
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- en
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license: apache-2.0
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base_model: unsloth/Qwen3-14B
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tags:
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- text-generation
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- fraud-detection
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- anti-money-laundering
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- financial-crime
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- unsloth
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- qwen3
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- sft
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- fraudsentinel
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datasets:
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- naazimsnh02/fraud-financial-crime-qwen3-sft-v2
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---
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# FraudSentinel — Qwen3-14B Merged (Tier-2 Intelligence Layer)
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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.
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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).
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---
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## Capabilities
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This model is trained to act as an enterprise fraud detection and AML investigation assistant. It handles six task types:
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- **Structured JSON risk scoring** — calibrated score (0.0–1.0), risk level, typology, key signals, feature importance, recommended action, SAR rationale
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- **Explainable alerts** — evidence-grounded investigator-facing explanations tied to actual transaction features
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- **Typology classification** — card-not-present fraud, account takeover, fan-out, gather-scatter, structuring, smurfing, and more
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- **6-level recommended action** — `AUTO_APPROVE → APPROVE_WITH_MONITORING → STEP_UP_AUTH → TEMPORARY_HOLD → AUTO_BLOCK → SAR_REVIEW`
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- **SAR drafting** — FinCEN-aligned Suspicious Activity Report narrative generation for human review
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- **Multi-turn HITL dialogue** — investigator follow-up conversations with the model
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- **Deep Analysis mode** — Chain-of-Thought reasoning via Qwen3's thinking tokens
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---
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## Training Details
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| Property | Value |
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|---|---|
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| **Base model** | `unsloth/Qwen3-14B` (Apache-2.0) |
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| **Fine-tuning method** | SFT + LoRA, merged to full bf16 weights |
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| **LoRA rank / alpha** | 16 / 32 |
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| **Target modules** | All linear layers (q, k, v, o projections + MLP gate/up/down) |
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| **Trainable parameters (pre-merge)** | 64,225,280 (0.433% of 14.83B) |
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| **Dataset** | `naazimsnh02/fraud-financial-crime-qwen3-sft-v2` (11,016 train examples) |
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| **Epochs** | 2 |
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| **Total steps** | 1,378 |
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| **Effective batch size** | 16 (2 per device × 8 gradient accumulation) |
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| **Learning rate** | 1e-4 (cosine decay, 5% warmup) |
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| **Optimizer** | AdamW 8-bit |
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| **Precision** | bfloat16 |
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| **Max sequence length** | 4,096 |
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| **Hardware** | AMD MI300X, 192 GB VRAM, ROCm 7.0 |
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| **Framework** | Unsloth 2026.6.1, TRL 0.22.2 |
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| **Train loss (final)** | 0.2467 |
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| **Training time** | 70.5 min |
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| **Peak VRAM** | 39.8 GB (20.8% of 192 GB) |
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---
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## Usage
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### Transformers (no PEFT required)
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```python
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from transformers import AutoModelForCausalLM, AutoTokenizer
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import torch
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model = AutoModelForCausalLM.from_pretrained(
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"naazimsnh02/fraudsentinel-qwen3-14b-merged",
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torch_dtype=torch.bfloat16,
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device_map="auto",
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)
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tokenizer = AutoTokenizer.from_pretrained("naazimsnh02/fraudsentinel-qwen3-14b-merged")
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```
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### Unsloth (2× faster inference)
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```python
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from unsloth import FastLanguageModel
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import torch
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model, tokenizer = FastLanguageModel.from_pretrained(
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model_name = "naazimsnh02/fraudsentinel-qwen3-14b-merged",
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max_seq_length = 4096,
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dtype = torch.bfloat16,
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load_in_4bit = False,
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)
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FastLanguageModel.for_inference(model)
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```
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### vLLM (production serving)
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```bash
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vllm serve naazimsnh02/fraudsentinel-qwen3-14b-merged \
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--dtype bfloat16 \
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--max-model-len 4096
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```
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### Inference Example
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```python
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messages = [
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{"role": "system", "content": "You are FraudSentinel, an expert fraud detection and AML investigation assistant."},
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{"role": "user", "content": (
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"Analyze this AML transaction and return a structured JSON risk assessment.\n\n"
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"Transfer: amount_paid=95000 USD, amount_received=94850 EUR, payment_format=ACH, "
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"sender_out_degree=47, sender_in_degree=3, receiver_in_degree=52, "
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"ccy_mismatch=True, is_round=False, is_laundering=True"
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)},
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]
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# Fast mode — thinking OFF (default for Tier-2 triage)
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text = tokenizer.apply_chat_template(
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messages,
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tokenize=False,
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add_generation_prompt=True,
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enable_thinking=False,
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)
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inputs = tokenizer(text, return_tensors="pt").to(model.device)
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with torch.no_grad():
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output = model.generate(
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**inputs,
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max_new_tokens=512,
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temperature=0.1,
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do_sample=True,
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)
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print(tokenizer.decode(output[0][inputs["input_ids"].shape[1]:], skip_special_tokens=True))
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```
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**Deep Analysis mode** (Chain-of-Thought for complex or high-stakes cases):
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```python
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text = tokenizer.apply_chat_template(
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messages,
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tokenize=False,
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add_generation_prompt=True,
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enable_thinking=True, # activates Qwen3 thinking tokens, adds ~3–5 s latency
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)
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```
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---
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## Output Schema (Structured Task)
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```json
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{
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"risk_score": 0.91,
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"risk_level": "CRITICAL",
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"conclusion": "SUSPICIOUS",
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"primary_typology": "layering / fan-in gather-scatter",
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"secondary_typology": "rapid_passthrough",
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"key_signals": [
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"high_receiver_in_degree",
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"cross_currency_conversion",
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"ach_channel_over_representation"
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],
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"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...",
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"feature_importance": {
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"high_receiver_in_degree": 0.41,
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"cross_currency_conversion": 0.33,
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"ach_channel_over_representation": 0.26
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},
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"recommended_action": "SAR_REVIEW",
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"sar_required": true,
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"sar_rationale": "Transaction exhibits layering indicators — high-degree aggregation, cross-currency conversion, and ACH over-representation consistent with structuring."
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}
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```
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---
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## System Prompt
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The model was trained with the following system prompt pattern:
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```
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You are FraudSentinel, an expert fraud detection and AML investigation assistant.
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```
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Consistent use of this system prompt at inference produces the most coherent structured outputs and action recommendations.
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---
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## Limitations
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- 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.
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- AI-generated SAR drafts require human review and edit before filing with FinCEN.
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- The model was trained with thinking mode OFF. Enable it at inference for Deep Analysis; expect 3–5 s additional latency per response.
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- Feature importance values reflect deterministic heuristics from the training data pipeline, not SHAP or gradient-based model explanations.
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- 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.
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---
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## License
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Apache-2.0 (Qwen3 base model and fine-tuning adapter).
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