Jeff v1.3: jeff-adapter-aml

README.md

@@ -0,0 +1,228 @@
+---
+base_model: mstrasser/jeff-base
+base_model_relation: adapter
+library_name: peft
+license: apache-2.0
+language:
+- en
+pipeline_tag: text-classification
+tags:
+- jeff
+- lora
+- decision-model
+- compliance
+- calibration
+---
+
+# jeff-adapter-aml
+
+**Anti-money-laundering review.** Applies an institution's monitoring policy to a customer's last 30 days - clear, investigate, escalate or block.
+
+A LoRA adapter for [jeff-base](https://huggingface.co/mstrasser/jeff-base) **v1.3**, a small open decision model
+(a fine-tune of Qwen3.5-0.8B). You send a situation (the *state*) and questions with named options; Jeff returns a
+calibrated probability for every option from one forward pass, with no generated text to parse. One Jeff server loads
+the base once and any number of adapters beside it; each request picks an adapter by name (`"model": "aml"`).
+
+Adapter page, with the full data card: [jeffhub.ai/adapters/aml](https://jeffhub.ai/adapters/aml).
+
+## Results
+
+On this adapter's held-out test sets, never trained on, scored three ways on the same rows: the untrained model Jeff is built from, the Jeff v1.3 base alone, and the base with this adapter. Questions have 2 to 8 options. As of 2026-10-05. [All adapters](https://jeffhub.ai/results)
+
+| Test set | Test rows | Qwen3.5-0.8B untrained | Jeff base v1.3 alone | Jeff base v1.3 + adapter |
+|----|----|----|----|----|
+| `test` | 5,120 | 36.0% · 0.022 | 40.5% · 0.103 | 95.0% · 0.012 |
+| `test-amlworld-v1` | 4,500 | 48.9% · 0.023 | 64.3% · 0.044 | 60.0% · 0.244 |
+
+Each cell: accuracy · calibration error (ECE; lower is better, 0 is perfect).
+
+By group (test set `test`)
+
+| Group | Rows | Qwen3.5-0.8B untrained | Jeff base v1.3 alone | Jeff base v1.3 + adapter |
+|----|----|----|----|----|
+| question: action | 2,386 | 27.3% | 22.4% | 94.1% |
+| question: pattern | 348 | 13.8% | 17.8% | 99.7% |
+| question: suspicious | 2,386 | 47.9% | 62.0% | 95.3% |
+
+By group (test set `test-amlworld-v1`)
+
+| Group | Rows | Qwen3.5-0.8B untrained | Jeff base v1.3 alone | Jeff base v1.3 + adapter |
+|----|----|----|----|----|
+| question: amlworld_window | 4,500 | 48.9% | 64.3% | 60.0% |
+
+### With llama.cpp (GGUF)
+
+The same test, through llama.cpp: the base GGUF ([mstrasser/jeff-base-gguf](https://huggingface.co/mstrasser/jeff-base-gguf)) plus this adapter's LoRA GGUF ([mstrasser/jeff-adapter-aml-gguf](https://huggingface.co/mstrasser/jeff-adapter-aml-gguf)), with the temperature refitted for each format. [Running Jeff with llama.cpp](https://jeffhub.ai/docs/llama-cpp)
+
+| Test set           | Full precision | Q8_0          | Q4_K_M        |
+|--------------------|----------------|---------------|---------------|
+| `test`             | 95.0% · 0.012  | 95.0% · 0.012 | 94.6% · 0.015 |
+| `test-amlworld-v1` | 60.0% · 0.244  | 59.8% · 0.246 | 60.7% · 0.221 |
+
+Not measured yet for v1.3: calibration charts, the commonest confusions and accuracy per answer.
+
+Source of these numbers: `results/sources/v1.3/new-adapters.table.json` in the JeffHub repository, also collected in [jeffhub-v1.3.json](https://jeffhub.ai/data/jeffhub-v1.3.json).
+
+## When to use it
+
+- You have a written transaction-monitoring policy (warning signs, thresholds, high-risk and blocked countries and parties) and want each customer review sorted first.
+- You want the policy's own answer with a calibrated probability, so analysts start with the unsure cases.
+
+## When not to use it
+
+- You want the model to find laundering your policy does not describe. It applies the rules you give it; on raw transactions without a written policy (IBM's AMLworld cross-test) it scores 60.0%, below the base alone.
+- You need a final decision without a person. Reports to the authorities have legal consequences.
+- Your activity summaries look very different from the training format. Test on your own data first.
+
+## How to use it
+
+The adapter runs with Jeff's server on the jeff-base **v1.3** base. Adapter serving arrives with the next Jeff release;
+until then, these commands need the `feat/lora` branch of [firelex/jeff](https://github.com/firelex/jeff).
+
+```bash
+git clone https://github.com/firelex/jeff && cd jeff
+uv sync --no-default-groups --extra lora          # add --extra cuda on NVIDIA GPUs, --extra mac on Apple silicon
+uv run --no-default-groups hf download mstrasser/jeff-base --revision v1.3 --local-dir checkpoints/jeff-base
+uv run --no-default-groups hf download mstrasser/jeff-adapter-aml --revision v1.3 --local-dir adapters/aml
+JEFF_CHECKPOINT=checkpoints/jeff-base JEFF_ADAPTERS=adapters/ PORT=8765 \
+  uv run --no-default-groups jeff-serve          # on a Mac, add JEFF_BACKEND=mlx
+```
+
+Every folder in `adapters/` is served under its folder name; add or replace adapters while the server runs with
+`curl -X POST http://localhost:8765/v1/adapters/reload`. Each adapter records the exact base it was trained on, and
+the server refuses an adapter trained on a different one, so this adapter loads only on jeff-base v1.3 (a v1.2 adapter
+does not load on v1.3). For llama.cpp, use [mstrasser/jeff-adapter-aml-gguf](https://huggingface.co/mstrasser/jeff-adapter-aml-gguf).
+
+### Request format
+
+State (the situation), in this order:
+
+| Key | Changes per request | What it holds |
+|---|---|---|
+| `institution` | no | The institution, its customer groups, and its monitoring policy - warning signs, thresholds, high-risk and blocked countries and parties, and what each action means. |
+| `customer` | no | The customer profile: segment, occupation or business, expected incoming money, senders, recipients and countries, own accounts, documented events. |
+| `activity` | yes | The customer's payments over the review period, with a short summary of the totals the policy uses. |
+
+Questions:
+
+- `suspicious` (choice): Whether the activity is suspicious under the policy. Options: `yes`, `no`
+- `action` (choice): Which action the policy requires. Options: `clear`, `investigate`, `escalate`, `block`
+
+Rules:
+
+- Ask one question per request; each was trained with its own fixed instructions.
+- Keep the institution and the customer profile the same across reviews, and put the activity last, so the unchanging part can be prepared in advance.
+- The request format may still change before release.
+
+General rules for every request: [the request format guide](https://jeffhub.ai/docs/request-format).
+
+### Example
+
+The request below is also in this repository as [`example.json`](example.json).
+
+```json
+{
+  "model": "aml",
+  "state": {
+    "institution": "Meridian Crypto Exchange is a crypto exchange based in Italy. It serves these customer groups: private crypto investor, business accepting crypto payments. Clear means the review is closed with a short note; investigate means an analyst opens a case and reviews the customer's documents; escalate means the case goes to the money-laundering reporting officer, who decides on a report to the authorities; block means the payment is stopped and the account frozen until compliance decides.\n\nMonitoring rules (all amounts in euro):\nEach review covers one customer's last 30 days. Transfers between the customer's own accounts and wallets (listed in the customer profile) are ignored by every rule.\nWarning signs:\nW1 Structuring: 3 or more deposits made with cash vouchers bought at partner kiosks each from 2,400.00 up to just under the reporting threshold of 3,000.00.\nW2 Rapid pass-through: the incoming money totals at least 1,000.00 and at least 90% of it is sent out again (by any payment or cash withdrawal) within 72 hours of arriving.\nW3 Many senders: money comes from more different senders than the customer profile expects per month.\nW4 Many recipients: money goes to more different recipients than the customer profile expects per month.\nW8 High-risk country: a payment to or from a country on the high-risk list that the customer profile does not list as an expected country.\nHigh-risk countries: Algeria, Bulgaria, Cameroon, Lebanon, Mali, Nigeria, Philippines, South Sudan, Venezuela.\nBlocked countries: Belarus, North Korea, Syria.\nBlocked parties: Field Summit EOOD (Bulgaria); Ember Nova Company (Bolivia); Coral Alder Ltd (Malta).\nActions (exactly one applies):\nblock: any payment to or from a blocked country or a blocked party, whatever else the activity shows.\nescalate: two or more different warning signs, or W2 (Rapid pass-through) even on its own.\ninvestigate: exactly one warning sign, other than W2.\nclear: no warning sign and no blocked country or party.",
+    "customer": "Customer: Kenji Okafor (individual), customer since November 2019.\nSegment: private crypto investor. Occupation or business: shop owner investing in crypto.\nExpected incoming: about 620.00 a month.\nExpected senders: up to 8 different senders a month. Expected recipients: up to 6 different recipients a month.\nExpected countries: Italy, Lebanon.\nOwn accounts and wallets: bank account ending 5014 at another bank; own hardware wallet 0xb84f...182c.",
+    "activity": "Review period: 1 June 2026 to 30 June 2026 (30 days). Amounts in euro.\nPayments:\n01 Jun 09:12 received 46.76 by crypto deposit from wallet 0x576b...5f0d at FalconCoin (Italy)\n10 Jun 17:34 sent 376.32 by crypto withdrawal to wallet 0x3292...217b (private wallet, owner and country unknown)\n14 Jun 14:23 received 478.54 by crypto deposit from wallet 0x576b...5f0d at FalconCoin (Italy)\nSummary (transfers between own accounts left out):\n- incoming: 2 payments, total 525.30, from 1 different senders\n- outgoing: 1 payments, total 376.32, to 1 different recipients\n- share of incoming money sent out again within 72 hours: 0%\n- counterparty countries: Italy"
+  },
+  "questions": {
+    "action": {
+      "type": "choice",
+      "instructions": "A compliance analyst at the institution described above reviews this customer's last 30 days of activity. Which action does the institution's monitoring policy require?",
+      "criteria": {
+        "block": "Block: at least one payment goes to or comes from a blocked country or a blocked party, whatever else.",
+        "investigate": "Investigate: exactly one warning sign applies, and the policy does not say to escalate that one alone.",
+        "escalate": "Escalate: two or more different warning signs apply, or a sign that the policy says to escalate alone.",
+        "clear": "Clear: no warning sign applies at all, and no payment involves a blocked country or any blocked party."
+      }
+    }
+  }
+}
+```
+
+```bash
+curl -s localhost:8765/v1/systemone -H 'content-type: application/json' -d @adapters/aml/example.json
+```
+
+The answer holds a probability for each option of each question. A recorded response from the v1.3 adapter is not
+published yet.
+
+## Files
+
+- `adapter_model.safetensors`, `adapter_config.json`: the LoRA weights (PEFT format);
+- `readout.safetensors`: the adapter's own readout over the answer codes;
+- `decision_config.json`: answer codes, temperature, prompt layout and the checksum of the base it was trained on;
+- `example.json`: the example request above.
+
+`adapter_config.json` and `decision_config.json` name the base as `mstrasser/jeff-base`, revision `v1.3`; the server
+checks the base by the checksum of its weights.
+
+## Training
+
+| | |
+|---|---|
+| Base | [mstrasser/jeff-base](https://huggingface.co/mstrasser/jeff-base), revision v1.3 (a fine-tune of Qwen3.5-0.8B) |
+| Prompt layout | live-last: the fixed part of the request first, the changing state field last |
+| Run | `0.8b-aml-v2-20261004-0713`, final checkpoint |
+| Adapter files | 41.5 MB (adapter_model.safetensors and readout.safetensors) |
+| LoRA GGUF for llama.cpp | [mstrasser/jeff-adapter-aml-gguf](https://huggingface.co/mstrasser/jeff-adapter-aml-gguf) |
+
+- **1.3.0** (2026-10-03): First release, trained on Jeff v1.3 with the live-last prompt layout (run 0.8b-aml-v2-20261004-0713, data version 2).
+
+## Data card
+
+**Self-reported.** The numbers come from the adapter’s own maintainers and have not been re-run by anyone else. [What the levels mean](https://jeffhub.ai/docs/submitting-an-adapter#checking-levels)
+
+- Test set: not attached yet
+- QA report: not available here yet; it will be added once sanitised
+
+**How the test set was held out.** Whole simulated institutions are held out: the test's 117 families (institutions, and for the pattern question traced laundering attempts) never appear in training, so its policies and customers are new to the adapter. 5,120 test rows: 2,386 per policy question (suspicious, action) and 348 pattern rows.
+
+**Training data.** Training data not published.
+
+Training data: 40,054 rows (version 2 of the data, built 2026-10-04); development 1,064, calibration 1,136 and test 5,120 rows.
+
+Training mixed in a replay sample of the Jeff base model's own training data: 4,005 rows, about 10% on top of the adapter's 40,054 (inherited from the v1.2 recipe as a precaution; its effect has not been measured).
+
+What it is not: it follows the institution's written policy, not a general laundering detector. On an out-of-distribution cross-test (4,500 windows from IBM's AMLworld simulation, asking only whether any transaction is part of laundering, with no written policy to apply) the adapter scores 60.0% (ECE 0.244), below the base alone at 64.3%. That test is shown for information; it is not what the adapter was trained to do.
+
+Every label is computed by code - the scenario sets the action first, then the written policy's rules are re-applied to the transactions shown, and the build stops if the two disagree. GLM never sets a label.
+
+A second question, which laundering pattern a traced set of transfers forms (fan-out, fan-in, cycle and others), uses rows built from IBM's synthetic AMLworld data.
+
+## Data and licence
+
+**Adapter licence: Apache-2.0.**
+
+**To confirm:** whether the CDLA-Sharing-1.0 terms of the pattern rows affect the adapter's licence or only the data.
+
+It was trained on:
+
+- **Synthetic institutions, customers and transactions**. Licence: Released with the adapter under Apache-2.0 (made for this adapter) · Made by GLM 5.3 (own hardware), for some texts; everything else by code
+
+  Simulated by code with fixed seeds; all names are invented. Some policy introductions, customer profiles and activity texts are written or reworded by GLM 5.3 and checked by code.
+
+- **[IBM Transactions for Anti Money Laundering (AMLworld), laundering-pattern rows](https://www.kaggle.com/datasets/ealtman2019/ibm-transactions-for-anti-money-laundering-aml)**. Licence: Community Data License Agreement - Sharing - Version 1.0 (CDLA-Sharing-1.0); shared data, including modified data, must keep the same terms (open, but shared or changed data must keep the same terms) · Not made by a model
+
+  Altman et al., "Realistic Synthetic Financial Transactions for Anti-Money Laundering Models", NeurIPS 2023 Datasets and Benchmarks. Fully synthetic; no real people or accounts.
+
+## Limitations
+
+- Tied to jeff-base v1.3. It will not load on any other base or version; the server checks the base weights' checksum.
+- Jeff chooses between the options you give it. It does not write text or reason in several steps.
+- Calibration was fitted on this adapter's own calibration rows. On very different data, check it again.
+- Everything listed under *When not to use it* above.
+
+## Links
+
+- Adapter page: [jeffhub.ai/adapters/aml](https://jeffhub.ai/adapters/aml)
+- Base model: [mstrasser/jeff-base](https://huggingface.co/mstrasser/jeff-base) (revision v1.3)
+- LoRA GGUF for llama.cpp: [mstrasser/jeff-adapter-aml-gguf](https://huggingface.co/mstrasser/jeff-adapter-aml-gguf)
+- What changed in v1.3: [release notes](https://jeffhub.ai/docs/release-notes-v1-3)
+- Code and server: [github.com/firelex/jeff](https://github.com/firelex/jeff)
+
+Jeff is an independent project. It uses the same request format as Jev but is not affiliated with or endorsed by
+TypeSafe, the makers of Jev.

adapter_config.json

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+  "alpha_pattern": {},
+  "arrow_config": null,
+  "auto_mapping": {
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+    "parent_library": "transformers.models.qwen3_5.modeling_qwen3_5"
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+  "base_model_name_or_path": "mstrasser/jeff-base",
+  "bias": "none",
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+  "megatron_core": "megatron.core",
+  "modules_to_save": null,
+  "monteclora_config": null,
+  "peft_type": "LORA",
+  "peft_version": "0.21.1",
+  "qalora_group_size": 16,
+  "r": 16,
+  "rank_pattern": {},
+  "revision": "v1.3",
+  "target_modules": [
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+    "up_proj",
+    "o_proj",
+    "gate_proj",
+    "in_proj_qkv",
+    "q_proj",
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+  "task_type": null,
+  "trainable_token_indices": null,
+  "use_bdlora": null,
+  "use_dora": false,
+  "use_qalora": false,
+  "use_rslora": false,
+  "velora_config": null
+}

adapter_model.safetensors

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decision_config.json

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+   "language_model.layers.23.mlp.up_proj",
+   "language_model.layers.23.mlp.down_proj"
+  ],
+  "base_checkpoint": {
+   "path": "mstrasser/jeff-base",
+   "weights_sha256": {
+    "model.safetensors": "d324dd6c9bb61b30af65564135b33f6892c30a9b2bd22667b2e09b9c8118cf77"
+   },
+   "revision": "v1.3"
+  }
+ },
+ "prompt_layout": "live-last"
+}

example.json

@@ -0,0 +1,20 @@
+{
+  "model": "aml",
+  "state": {
+    "institution": "Meridian Crypto Exchange is a crypto exchange based in Italy. It serves these customer groups: private crypto investor, business accepting crypto payments. Clear means the review is closed with a short note; investigate means an analyst opens a case and reviews the customer's documents; escalate means the case goes to the money-laundering reporting officer, who decides on a report to the authorities; block means the payment is stopped and the account frozen until compliance decides.\n\nMonitoring rules (all amounts in euro):\nEach review covers one customer's last 30 days. Transfers between the customer's own accounts and wallets (listed in the customer profile) are ignored by every rule.\nWarning signs:\nW1 Structuring: 3 or more deposits made with cash vouchers bought at partner kiosks each from 2,400.00 up to just under the reporting threshold of 3,000.00.\nW2 Rapid pass-through: the incoming money totals at least 1,000.00 and at least 90% of it is sent out again (by any payment or cash withdrawal) within 72 hours of arriving.\nW3 Many senders: money comes from more different senders than the customer profile expects per month.\nW4 Many recipients: money goes to more different recipients than the customer profile expects per month.\nW8 High-risk country: a payment to or from a country on the high-risk list that the customer profile does not list as an expected country.\nHigh-risk countries: Algeria, Bulgaria, Cameroon, Lebanon, Mali, Nigeria, Philippines, South Sudan, Venezuela.\nBlocked countries: Belarus, North Korea, Syria.\nBlocked parties: Field Summit EOOD (Bulgaria); Ember Nova Company (Bolivia); Coral Alder Ltd (Malta).\nActions (exactly one applies):\nblock: any payment to or from a blocked country or a blocked party, whatever else the activity shows.\nescalate: two or more different warning signs, or W2 (Rapid pass-through) even on its own.\ninvestigate: exactly one warning sign, other than W2.\nclear: no warning sign and no blocked country or party.",
+    "customer": "Customer: Kenji Okafor (individual), customer since November 2019.\nSegment: private crypto investor. Occupation or business: shop owner investing in crypto.\nExpected incoming: about 620.00 a month.\nExpected senders: up to 8 different senders a month. Expected recipients: up to 6 different recipients a month.\nExpected countries: Italy, Lebanon.\nOwn accounts and wallets: bank account ending 5014 at another bank; own hardware wallet 0xb84f...182c.",
+    "activity": "Review period: 1 June 2026 to 30 June 2026 (30 days). Amounts in euro.\nPayments:\n01 Jun 09:12 received 46.76 by crypto deposit from wallet 0x576b...5f0d at FalconCoin (Italy)\n10 Jun 17:34 sent 376.32 by crypto withdrawal to wallet 0x3292...217b (private wallet, owner and country unknown)\n14 Jun 14:23 received 478.54 by crypto deposit from wallet 0x576b...5f0d at FalconCoin (Italy)\nSummary (transfers between own accounts left out):\n- incoming: 2 payments, total 525.30, from 1 different senders\n- outgoing: 1 payments, total 376.32, to 1 different recipients\n- share of incoming money sent out again within 72 hours: 0%\n- counterparty countries: Italy"
+  },
+  "questions": {
+    "action": {
+      "type": "choice",
+      "instructions": "A compliance analyst at the institution described above reviews this customer's last 30 days of activity. Which action does the institution's monitoring policy require?",
+      "criteria": {
+        "block": "Block: at least one payment goes to or comes from a blocked country or a blocked party, whatever else.",
+        "investigate": "Investigate: exactly one warning sign applies, and the policy does not say to escalate that one alone.",
+        "escalate": "Escalate: two or more different warning signs apply, or a sign that the policy says to escalate alone.",
+        "clear": "Clear: no warning sign applies at all, and no payment involves a blocked country or any blocked party."
+      }
+    }
+  }
+}

readout.safetensors

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:e922205867c51855f7f48d7b3e8c4bf1c1b009757dbbd7ef6fe3d29aff42ad6f
+size 522320