osprey-narrator-v0.1

Osprey Narrator v0.1

A fine-tuned LLM that automatically generates SAR-style compliance narratives from transaction monitoring alerts.

Osprey Narrator takes raw JSON output from the Osprey transaction monitoring engine and produces structured, analyst-ready compliance narratives — replacing hours of manual report writing with seconds of inference.

Model Details

Performance

Training Details

Loss Curve

Steady decrease with no divergence — healthy training with no overfitting.

What It Does

Given a JSON evaluation from the Osprey transaction monitoring engine, the model generates a structured compliance narrative with these sections:

1. Alert Summary — Alert ID, transaction type, amount, parties, decision
2. Transaction Details — Contextual description of the transaction
3. Risk Assessment — Overall risk analysis with score interpretation
4. Rules Triggered — Table of all 12 FATF rules with scores
5. Typology Analysis — Table of 6 FATF typologies with match status
6. Narrative — Human-readable explanation connecting the dots
7. Recommended Actions — Next steps for the compliance team

FATF Rules Covered

Typologies Covered

Quick Start

With Unsloth (recommended for inference)

from unsloth import FastLanguageModel
import json

model, tokenizer = FastLanguageModel.from_pretrained(
    model_name="josephgoksu/osprey-narrator-v0.1",
    max_seq_length=4096,
    load_in_4bit=True,
)
FastLanguageModel.for_inference(model)

# Your Osprey evaluation JSON
evaluation = {
    "decision": "ALRT",
    "composite_score": 724.50,
    "threshold": 500.0,
    "transaction": {
        "type": "wire_transfer",
        "amount": 147500.00,
        "currency": "USD",
        "originator": {"name": "Atlas Global Ventures", "country": "AE"},
        "beneficiary": {"name": "Pinnacle Commodities Ltd.", "country": "NG"}
    },
    # ... full evaluation JSON
}

messages = [
    {"role": "system", "content": "You are an experienced AML/CFT compliance analyst..."},
    {"role": "user", "content": json.dumps(evaluation)}
]

inputs = tokenizer.apply_chat_template(
    messages,
    tokenize=True,
    add_generation_prompt=True,
    enable_thinking=False,
    return_tensors="pt",
).to("cuda")

outputs = model.generate(
    input_ids=inputs,
    max_new_tokens=1024,
    temperature=0.7,
    top_p=0.9,
)

narrative = tokenizer.decode(outputs[0][inputs.shape[-1]:], skip_special_tokens=True)
print(narrative)

With HuggingFace Transformers + PEFT

from transformers import AutoModelForCausalLM, AutoTokenizer
from peft import PeftModel
import json

# Load base model
base_model = AutoModelForCausalLM.from_pretrained(
    "Qwen/Qwen3-4B-Instruct-2507",
    device_map="auto",
    load_in_4bit=True,
)
tokenizer = AutoTokenizer.from_pretrained("josephgoksu/osprey-narrator-v0.1")

# Apply LoRA adapter
model = PeftModel.from_pretrained(base_model, "josephgoksu/osprey-narrator-v0.1")

# Generate (same message format as above)
messages = [
    {"role": "system", "content": "You are an experienced AML/CFT compliance analyst..."},
    {"role": "user", "content": json.dumps(evaluation)}
]

inputs = tokenizer.apply_chat_template(messages, return_tensors="pt").to("cuda")
outputs = model.generate(inputs, max_new_tokens=1024, temperature=0.7)
print(tokenizer.decode(outputs[0][inputs.shape[-1]:], skip_special_tokens=True))

With MLX on Apple Silicon (macOS)

pip install mlx-lm

from mlx_lm import load, generate

model, tokenizer = load(
    "josephgoksu/osprey-narrator-v0.1",
    adapter_path="josephgoksu/osprey-narrator-v0.1",
)
# Use the same chat template approach

Example Output

Input: ALRT evaluation with score 724.50, wire transfer of $147,500 USD from AE → NG

Output (abbreviated):

Alert Summary

• Alert ID: eval-7a3b2c1d-4e5f-6a7b-8c9d-0e1f2a3b4c5d
• Transaction Type: wire_transfer
• Amount: 147,500.00 USD
• Decision: ALRT (Alert Generated)
• Composite Score: 724.50 (threshold: 500.0)

Transaction Details

A wire transfer of 147,500.00 USD was initiated by Atlas Global Ventures (AE) to Pinnacle Commodities Ltd. (NG). The transaction involves a cross-border movement between high-risk jurisdictions...

Risk Assessment

The composite risk score of 724.50 significantly exceeds the alert threshold of 500.0, indicating elevated risk. Seven of twelve FATF rules were triggered, with High-Value Transaction (0.875) and Geographic Risk (0.810) contributing the highest individual scores...

Rules Triggered

Rule	Score	Triggered
FATF-R001 High-Value Transaction	0.875	Yes
FATF-R003 Rapid Movement of Funds	0.720	Yes
FATF-R004 Geographic Risk	0.810	Yes
...	...	...

Recommended Actions

• Escalate to senior compliance officer for SAR filing review
• Request enhanced due diligence on both counterparties
• Investigate related transactions within the past 90 days

Training Data

The model was trained on synthetic data only — no real customer or transaction data was used. The synthetic dataset was generated to cover:

• 5 scenario types: Clean transactions (40%), single rule triggers (15%), multi-rule below threshold (10%), typology matches (20%), critical alerts (15%)
• Decision distribution: ~35% ALRT (alert) / ~65% NALT (no alert)
• 12 FATF rules and 6 typologies with realistic score distributions
• 5-8 phrasing variants per rule/typology to prevent memorization

Limitations

• Synthetic training only — may not capture all real-world edge cases
• v0.1 POC — not yet validated against production compliance requirements
• English only — narratives are generated in English
• Token limit — very complex evaluations may be truncated at 4096 tokens
• No real regulatory validation — output should be reviewed by compliance professionals before use in actual SAR filings

Architecture

Osprey TM Engine → JSON Evaluation → Osprey Narrator → SAR Narrative
                                      (this model)

The model sits downstream of the Osprey transaction monitoring engine. It receives the structured evaluation output (rules, typologies, scores, decision) and transforms it into human-readable compliance narratives.

License

Apache 2.0

Citation

@misc{osprey-narrator-2026,
  title={Osprey Narrator: Fine-Tuned SAR Narrative Generation},
  author={Joseph Goksu},
  year={2026},
  url={https://huggingface.co/josephgoksu/osprey-narrator-v0.1}
}