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Model: flowxai/sentinel-gate Source: Original Platform
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# FlowX Sentinel Gate (4B) — Evaluation Results
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Held-out evaluation of the released model (`flowx-sentinel-gate-4b-v2`). Numbers are on a
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**held-out set of 71 cases (46 ESCALATE / 17 DECIDE)** across banking, insurance, logistics,
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and labor. Decoding: greedy (temperature 0), `enable_thinking=False`. The safety-critical
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metric is the **false-negative rate** (gold=ESCALATE but the model says DECIDE, i.e.
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auto-deciding a case that should have gone to a human).
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## Headline (held-out, n=71)
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| Metric | Result | Note |
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| --- | --- | --- |
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| False-negative rate (missed escalations) | **0.000** (0/46) | the safety metric |
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| Action accuracy (ESCALATE vs DECIDE) | **1.000** | |
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| False-positive rate (over-escalation) | **0.000** (0/17) | |
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| JSON validity (raw) | 0.89 | deploy with the deterministic repair step |
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| Category accuracy (on true-escalate) | 0.61 | the human-routing hint |
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## Decision confusion (the gate's actual job)
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| gold \\ predicted | ESCALATE | DECIDE |
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| --- | --- | --- |
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| ESCALATE (n=46) | 46 | 0 |
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| DECIDE (n=17) | 0 | 17 |
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The ESCALATE/DECIDE decision is correct on every held-out case: no missed escalations, no
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over-escalation. That decision is what gates automation and is the field to trust.
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## Baseline (before the data rebalance/retrain)
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| Metric | baseline (n=24, 2 DECIDE) | this release (n=71, 17 DECIDE) |
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| --- | --- | --- |
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| False-negative rate | 0.000 | 0.000 |
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| Action accuracy | 0.944 | 1.000 |
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| False-positive rate | 0.50 (noisy, n=2) | 0.000 (trustworthy, n=17) |
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| JSON validity | 0.75 | 0.89 |
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| Category accuracy | 0.875 (n=16) | 0.61 (n=46) |
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Rebalancing DECIDE from ~15% to ~35% of the corpus and retraining made the false-positive
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rate trustworthy (17 DECIDE cases vs 2) and lifted JSON validity 0.75 → 0.89 while holding the
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safety metric at 0.000. The category-accuracy "drop" is the honest number emerging on a larger
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held-out (the old 0.875 was small-sample noise on 16 cases).
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## Honest reading / caveats
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- **Category is a routing hint, not a gate.** The model gets the ESCALATE/DECIDE decision right
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every time here, but the `escalation_category` label is ~0.61 (categories legitimately
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overlap for some cases). Route on the decision; treat the category as a suggestion.
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- **JSON validity 0.89 raw.** Deploy with the deterministic JSON repair step the pipeline pairs
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with the model.
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- **Home-field note.** Scenarios are realistic-synthetic, grounded in real regulatory
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citations. Validate on your own case distribution before production.
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## Reproduction
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Held-out: `mlx_data/escalation_v2/valid.jsonl`. Scorer:
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`eval_sentinel_4b.py <model_path> mlx_data/escalation_v2/valid.jsonl`.
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_Author: Bogdan Răduță, Head of Research, FlowX.AI._
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34
NOTICE
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NOTICE
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FlowX.AI Regulatory Ontology Models
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||||||
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Copyright 2026 FlowX.AI
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Licensed under the Apache License, Version 2.0 (the "License"); you may not use
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these model artifacts except in compliance with the License. You may obtain a
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copy of the License at http://www.apache.org/licenses/LICENSE-2.0.
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This release includes two LoRA-fine-tuned models developed by FlowX.AI:
|
||||||
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- FlowX Semantic Mapper (regulatory-text -> structured ontology JSON)
|
||||||
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- FlowX Sentinel Gate (case -> ESCALATE / DECIDE with category + audit trail)
|
||||||
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|
||||||
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Base model
|
||||||
|
Qwen3-4B, Copyright Alibaba Cloud, licensed under Apache-2.0
|
||||||
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(https://huggingface.co/Qwen/Qwen3-4B). These models are LoRA adaptations of
|
||||||
|
that base and inherit its Apache-2.0 terms.
|
||||||
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|
||||||
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Training data provenance
|
||||||
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- Semantic Mapper: real, verbatim regulatory / legislative text from official
|
||||||
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public sources (US eCFR and state codes; EU EUR-Lex; France Legifrance;
|
||||||
|
Germany Gesetze im Internet; Romania legislatie.just.ro; UNECE/ADR), used for
|
||||||
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ontology annotation. Official legislation is reproduced with source
|
||||||
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acknowledgement; each training record carries its source URL.
|
||||||
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- Sentinel Gate: realistic synthetic escalation scenarios authored by FlowX.AI,
|
||||||
|
grounded in real regulatory citations.
|
||||||
|
Concept labels use a controlled 252-concept taxonomy authored by FlowX.AI.
|
||||||
|
|
||||||
|
Responsible use
|
||||||
|
These models are decision-support tools for regulatory/compliance workflows.
|
||||||
|
They are NOT legal advice and must not be the sole basis for a legal or
|
||||||
|
compliance decision. Outputs should be reviewed by a qualified professional.
|
||||||
|
|
||||||
|
Attribution
|
||||||
|
If you use or redistribute these models, please retain this NOTICE and credit
|
||||||
|
"FlowX.AI".
|
||||||
385
README.md
Normal file
385
README.md
Normal file
@@ -0,0 +1,385 @@
|
|||||||
|
---
|
||||||
|
license: apache-2.0
|
||||||
|
language:
|
||||||
|
- en
|
||||||
|
base_model: Qwen/Qwen3-4B
|
||||||
|
library_name: transformers
|
||||||
|
pipeline_tag: text-generation
|
||||||
|
tags:
|
||||||
|
- lora
|
||||||
|
- regulatory
|
||||||
|
- compliance
|
||||||
|
- escalation
|
||||||
|
- decision-gate
|
||||||
|
- mlx
|
||||||
|
- gguf
|
||||||
|
- flowx
|
||||||
|
model-index:
|
||||||
|
- name: sentinel-gate
|
||||||
|
results:
|
||||||
|
- task:
|
||||||
|
type: text-generation
|
||||||
|
name: Escalation gate decision (ESCALATE vs DECIDE) on regulated-decision cases
|
||||||
|
dataset:
|
||||||
|
name: FlowX Sentinel held-out cases
|
||||||
|
type: flowxai/sentinel-gate
|
||||||
|
metrics:
|
||||||
|
- type: false_negative_rate
|
||||||
|
name: false-negative rate (missed escalations, the safety metric)
|
||||||
|
value: 0.000
|
||||||
|
- type: accuracy
|
||||||
|
name: action accuracy (ESCALATE vs DECIDE)
|
||||||
|
value: 1.000
|
||||||
|
- type: false_positive_rate
|
||||||
|
name: false-positive rate (over-escalation)
|
||||||
|
value: 0.000
|
||||||
|
- type: accuracy
|
||||||
|
name: raw JSON validity (deploy with deterministic repair)
|
||||||
|
value: 0.89
|
||||||
|
- type: accuracy
|
||||||
|
name: category accuracy on true-escalate (routing hint)
|
||||||
|
value: 0.61
|
||||||
|
---
|
||||||
|
|
||||||
|
## Inference contract
|
||||||
|
|
||||||
|
Running this model correctly requires its **frozen inference contract** - the exact
|
||||||
|
system prompt, output JSON schema, user-turn format, and decode spec it was trained
|
||||||
|
against. See [`inference_contract/`](./inference_contract):
|
||||||
|
|
||||||
|
- [`INFERENCE.md`](./inference_contract/INFERENCE.md) - wiring guide: system prompt, user turn `Case:\n<case JSON + "policy_schema">\n\nDecide: ESCALATE or DECIDE?`, decode settings (`enable_thinking=False`, temp 0, `max_new_tokens` ~1200), the six categories, and the **deterministic JSON repair step** (raw JSON validity 0.89) the deployed pipeline pairs with the model.
|
||||||
|
- [`prompt_sentinel_sys_v1.txt`](./inference_contract/prompt_sentinel_sys_v1.txt) - the system prompt, verbatim.
|
||||||
|
- [`schema_sentinel_v1.json`](./inference_contract/schema_sentinel_v1.json) - output JSON Schema for the oracle decision.
|
||||||
|
|
||||||
|
Prompt version `sentinel_sys_v1`. Do not edit the prompt/schema; the weights are trained against them.
|
||||||
|
|
||||||
|
# FlowX Sentinel Gate (4B) - the escalation decision gate
|
||||||
|
|
||||||
|
**An on-device escalation gate for regulated decisions: given a complete case, it decides ESCALATE (route to a human) vs DECIDE (safe to automate), with a category, rationale, calibrated confidence, and an audit trail.**
|
||||||
|
|
||||||
|
Sentinel Gate is a **LoRA fine-tune of Qwen3-4B** (Apache-2.0), built by FlowX.AI. It reads
|
||||||
|
a complete regulated-decision case - the domain facts plus the applicable `policy_schema`
|
||||||
|
(a `PDP...` policy id) - and returns a single strict JSON decision. It sits **after
|
||||||
|
[`flowxai/semantic-mapper`](https://huggingface.co/flowxai/semantic-mapper)** in a compliance
|
||||||
|
pipeline: the Mapper tags knowledge-base chunks, a policy layer assembles a case, and the
|
||||||
|
Sentinel Gate decides what is safe to automate versus what must be routed to a human.
|
||||||
|
|
||||||
|
The product insight that shapes everything: **the gate's job is the ESCALATE/DECIDE
|
||||||
|
decision, and that decision is safety-critical, so it is the metric we optimize and the field
|
||||||
|
you gate on.** A compliance automation gate that misses an escalation is dangerous in a way
|
||||||
|
that a wrong routing label is not. So we lead with the false-negative rate (missed
|
||||||
|
escalations), hold it at **0.000**, and treat the escalation *category* as a secondary
|
||||||
|
routing hint (~0.61) rather than a second gate. The model never over-escalates either
|
||||||
|
(false-positive rate 0.000), which is what makes the automation worth having.
|
||||||
|
|
||||||
|
Part of the **FlowX on-device model family**:
|
||||||
|
[`flowxai/caveat`](https://huggingface.co/flowxai/caveat),
|
||||||
|
[`flowxai/scam-guard-qwen06b`](https://huggingface.co/flowxai/scam-guard-qwen06b),
|
||||||
|
[`flowxai/scam-guard-qwen17b`](https://huggingface.co/flowxai/scam-guard-qwen17b),
|
||||||
|
[`flowxai/semantic-mapper`](https://huggingface.co/flowxai/semantic-mapper).
|
||||||
|
|
||||||
|
> **Not legal advice - decision-support only.** Sentinel Gate is a compliance automation
|
||||||
|
> gate, not a legal opinion and not a substitute for a compliance officer. It decides
|
||||||
|
> *whether a case is safe to automate or must go to a human*; a human owns every escalated
|
||||||
|
> case. Deploy with the confidence threshold your risk posture requires.
|
||||||
|
|
||||||
|
English at v1. Domains span **banking, insurance, logistics, and labor** (cases are EN).
|
||||||
|
On-device formats: **fp16 safetensors** (transformers/CUDA/vLLM), **MLX-quantized** (int4 +
|
||||||
|
int8, Apple Silicon), and **GGUF** (llama.cpp/CPU/Ollama, `Q8_0` + `Q4_K_M`).
|
||||||
|
|
||||||
|
---
|
||||||
|
|
||||||
|
## How do I use it?
|
||||||
|
|
||||||
|
Three copy-pasteable ways to turn a case into a decision. The system prompt is the exact
|
||||||
|
two-liner from [`prompt_sentinel_sys_v1.txt`](./inference_contract/prompt_sentinel_sys_v1.txt).
|
||||||
|
The user turn is `Case:\n<case JSON>\n\nDecide: ESCALATE or DECIDE?`.
|
||||||
|
|
||||||
|
Real example input (an OFAC-screening wire transfer - a `POLICY_VIOLATION` escalation):
|
||||||
|
|
||||||
|
```
|
||||||
|
Case:
|
||||||
|
{
|
||||||
|
"transaction_facts": {
|
||||||
|
"product": "international wire transfer",
|
||||||
|
"originator": "Crestwood Imports Inc",
|
||||||
|
"beneficiary_name": "Volna Trading LLC",
|
||||||
|
"beneficiary_country": "Cyprus",
|
||||||
|
"amount_usd": 118000,
|
||||||
|
"purpose": "machinery purchase",
|
||||||
|
"screening_result": "potential match - beneficiary owner on SDN-adjacent watchlist (50% rule concern)"
|
||||||
|
},
|
||||||
|
"documents_provided": ["wire request form", "commercial invoice", "originator KYC on file"],
|
||||||
|
"documents_missing": [],
|
||||||
|
"policy_schema": "PDP.lending.payments.ofac_sanctions_block"
|
||||||
|
}
|
||||||
|
|
||||||
|
Decide: ESCALATE or DECIDE?
|
||||||
|
```
|
||||||
|
|
||||||
|
### (a) transformers / CUDA (fp16 safetensors)
|
||||||
|
|
||||||
|
The root of the repo is fp16 safetensors for transformers / vLLM.
|
||||||
|
|
||||||
|
```python
|
||||||
|
from transformers import AutoModelForCausalLM, AutoTokenizer
|
||||||
|
|
||||||
|
SYSTEM = ("You are an escalation gate for regulated decisions.\n"
|
||||||
|
"Determine: ESCALATE or DECIDE? Output ONLY JSON.")
|
||||||
|
tok = AutoTokenizer.from_pretrained("flowxai/sentinel-gate")
|
||||||
|
model = AutoModelForCausalLM.from_pretrained("flowxai/sentinel-gate", torch_dtype="float16", device_map="cuda")
|
||||||
|
|
||||||
|
case = open("wire_case.json").read() # the case JSON above
|
||||||
|
user = f"Case:\n{case}\n\nDecide: ESCALATE or DECIDE?"
|
||||||
|
|
||||||
|
prompt = tok.apply_chat_template(
|
||||||
|
[{"role": "system", "content": SYSTEM},
|
||||||
|
{"role": "user", "content": user}],
|
||||||
|
add_generation_prompt=True, enable_thinking=False, return_tensors="pt",
|
||||||
|
).to("cuda")
|
||||||
|
out = model.generate(prompt, max_new_tokens=1200, do_sample=False, temperature=0.0)
|
||||||
|
raw = tok.decode(out[0][prompt.shape[-1]:], skip_special_tokens=True)
|
||||||
|
# then: deterministic JSON repair -> validate against schema_sentinel_v1.json
|
||||||
|
```
|
||||||
|
|
||||||
|
### (b) MLX (Apple Silicon)
|
||||||
|
|
||||||
|
Off the MLX-quantized weights (int4/int8):
|
||||||
|
|
||||||
|
```bash
|
||||||
|
mlx_lm.generate --model flowxai/sentinel-gate-mlx-int4 --temp 0 --max-tokens 1200 \
|
||||||
|
--system-prompt "$(cat inference_contract/prompt_sentinel_sys_v1.txt)" \
|
||||||
|
--prompt "$(printf 'Case:\n%s\n\nDecide: ESCALATE or DECIDE?' "$(cat wire_case.json)")"
|
||||||
|
```
|
||||||
|
|
||||||
|
```python
|
||||||
|
from mlx_lm import load, generate
|
||||||
|
|
||||||
|
SYSTEM = open("inference_contract/prompt_sentinel_sys_v1.txt").read()
|
||||||
|
model, tok = load("flowxai/sentinel-gate-mlx-int4")
|
||||||
|
case = open("wire_case.json").read()
|
||||||
|
user = f"Case:\n{case}\n\nDecide: ESCALATE or DECIDE?"
|
||||||
|
prompt = tok.apply_chat_template(
|
||||||
|
[{"role": "system", "content": SYSTEM},
|
||||||
|
{"role": "user", "content": user}],
|
||||||
|
add_generation_prompt=True, enable_thinking=False,
|
||||||
|
)
|
||||||
|
raw = generate(model, tok, prompt=prompt, max_tokens=1200, verbose=False)
|
||||||
|
```
|
||||||
|
|
||||||
|
### (c) Short note - decode discipline
|
||||||
|
|
||||||
|
Two things are non-negotiable: **`enable_thinking=False`** (the adapter was trained on pure
|
||||||
|
JSON, so the default thinking template yields empty/degraded output) and **temp 0** (the gate
|
||||||
|
must be reproducible for audit). Give it **~1200 new tokens** - the oracle JSON can run long
|
||||||
|
and truncation is the main cause of invalid JSON. Always run the output through the
|
||||||
|
deterministic JSON repair step (see [`INFERENCE.md`](./inference_contract/INFERENCE.md)).
|
||||||
|
|
||||||
|
All three backends return the **same strict JSON** for the wire-transfer case above:
|
||||||
|
|
||||||
|
```json
|
||||||
|
{
|
||||||
|
"action": "ESCALATE",
|
||||||
|
"escalation_category": "POLICY_VIOLATION",
|
||||||
|
"policy_violations": {
|
||||||
|
"ofac_potential_match": {
|
||||||
|
"triggered": true,
|
||||||
|
"policy": "PDP.lending.payments.ofac_sanctions_block",
|
||||||
|
"regulation": "31 CFR 501 / OFAC sanctions regulations; OFAC 50 Percent Rule; 31 CFR 1010 (BSA recordkeeping for funds transfers)",
|
||||||
|
"restriction": "Beneficiary ownership screening returned a potential SDN-adjacent match invoking the OFAC 50% Rule; payment is automatically held pending sanctions determination.",
|
||||||
|
"consequence": "Releasing the wire without OFAC clearance risks a sanctions violation, blocking/rejection obligations, and civil penalties; the transaction must be blocked from auto-release."
|
||||||
|
}
|
||||||
|
},
|
||||||
|
"confidence_score": 0.33,
|
||||||
|
"confidence_reasoning": "Transaction documentation is complete, but a potential OFAC match on beneficiary ownership is a hard stop; only a sanctions analyst can clear, reject, or block the wire.",
|
||||||
|
"human_action_required": "OFAC/sanctions analyst to investigate the potential match, apply the 50% Rule analysis, and clear, reject, or block the wire per OFAC procedures.",
|
||||||
|
"audit_trail": [
|
||||||
|
"Ran beneficiary screening; returned potential SDN-adjacent ownership match",
|
||||||
|
"Triggered OFAC 50 Percent Rule review condition",
|
||||||
|
"Matched POLICY_VIOLATION on sanctions block policy",
|
||||||
|
"Held wire and routed to sanctions analyst; auto-release prohibited"
|
||||||
|
]
|
||||||
|
}
|
||||||
|
```
|
||||||
|
|
||||||
|
For a case with no blocking condition and complete documentation, the gate returns
|
||||||
|
`{"action": "DECIDE", "escalation_category": null, "decision": "...", ...}` with a high
|
||||||
|
`confidence_score` and `human_action_required: "NONE"`.
|
||||||
|
|
||||||
|
---
|
||||||
|
|
||||||
|
## How it works
|
||||||
|
|
||||||
|
```
|
||||||
|
+----------------------------------+
|
||||||
|
case JSON (facts + | Sentinel Gate 4B |
|
||||||
|
policy_schema: "PDP...") ---> | Qwen3-4B (LoRA), thinking off | ---> strict JSON
|
||||||
|
Decide: ESCALATE or DECIDE? | greedy decode -> JSON repair |
|
||||||
|
+----------------------------------+
|
||||||
|
|
|
||||||
|
+ action (ESCALATE | DECIDE) <- the gate
|
||||||
|
+ escalation_category <- routing hint
|
||||||
|
+ category-specific block
|
||||||
|
+ confidence_score / reasoning
|
||||||
|
+ human_action_required
|
||||||
|
+ audit_trail
|
||||||
|
```
|
||||||
|
|
||||||
|
In the pipeline: `semantic-mapper` tags KB chunks -> a policy layer assembles a case ->
|
||||||
|
**Sentinel Gate decides automate vs route-to-human**. Under the hood, one turn is:
|
||||||
|
`apply_chat_template(enable_thinking=False) -> Qwen3-4B (LoRA fine-tuned) -> greedy decode ->
|
||||||
|
deterministic JSON repair -> validate against schema_sentinel_v1.json`.
|
||||||
|
|
||||||
|
**The `action` field is the safety-critical output.** You branch your automation off it and
|
||||||
|
nothing else. The escalation_category, block, and confidence enrich the human hand-off but
|
||||||
|
never decide it.
|
||||||
|
|
||||||
|
---
|
||||||
|
|
||||||
|
## Output schema
|
||||||
|
|
||||||
|
A single strict JSON object. Full schema:
|
||||||
|
[`schema_sentinel_v1.json`](./inference_contract/schema_sentinel_v1.json).
|
||||||
|
|
||||||
|
- `action` - `ESCALATE` | `DECIDE`. **The gate.** `ESCALATE` routes the case to a human;
|
||||||
|
`DECIDE` marks it safe to automate.
|
||||||
|
- `escalation_category` - one of the six ids when `action=ESCALATE`, else `null`. A
|
||||||
|
**routing hint** (~0.61), not a second gate.
|
||||||
|
- **category-specific block** - one object whose key depends on the category:
|
||||||
|
- `policy_violations` (POLICY_VIOLATION) - keyed by violation; each entry names `policy`,
|
||||||
|
`regulation`, `restriction`, `consequence`.
|
||||||
|
- `missing_preconditions` (MISSING_REQUIRED_DOCUMENTATION) - each entry names `required_by`,
|
||||||
|
`regulation`, `severity`, `reason`.
|
||||||
|
- `boundary_analysis` (BOUNDARY_CONDITION) - `policy_threshold`, the case value,
|
||||||
|
`distance_from_threshold`, `assessment`.
|
||||||
|
- `confidence_factors` (INSUFFICIENT_CONFIDENCE) - `ambiguous_signals[]`, `why_uncertain`.
|
||||||
|
- `conflicting_signals` (CONFLICTING_SIGNALS) - the competing `{source, value}` items.
|
||||||
|
- `external_dependency` (EXTERNAL_DEPENDENCY) - `awaiting`, `blocking_gate`.
|
||||||
|
- `confidence_score` - number 0.0–1.0. Calibrated; apply your own threshold. Advisory.
|
||||||
|
- `confidence_reasoning` - one to three sentences explaining the score and the decision.
|
||||||
|
- `human_action_required` - the concrete next step a human must take, or `"NONE"` for DECIDE.
|
||||||
|
- `audit_trail` - ordered, append-only list of reasoning steps and policy gates, for review.
|
||||||
|
- (DECIDE cases also carry `decision` / `selected_route` / `policy_gates_passed`.)
|
||||||
|
|
||||||
|
**The six escalation categories:** `MISSING_REQUIRED_DOCUMENTATION`, `POLICY_VIOLATION`,
|
||||||
|
`BOUNDARY_CONDITION`, `INSUFFICIENT_CONFIDENCE`, `CONFLICTING_SIGNALS`, `EXTERNAL_DEPENDENCY`.
|
||||||
|
|
||||||
|
---
|
||||||
|
|
||||||
|
## Evaluation
|
||||||
|
|
||||||
|
Held-out set: **n=71 cases (46 escalate / 17 decide)**. Metrics in order of what they tell you.
|
||||||
|
|
||||||
|
### The safety metric first: missed escalations
|
||||||
|
|
||||||
|
The dangerous failure for a compliance gate is **waving a case through that should have gone
|
||||||
|
to a human** (a missed escalation, i.e. a false negative). That is the number to read first.
|
||||||
|
|
||||||
|
| Metric | Result | What it means |
|
||||||
|
| --- | --- | --- |
|
||||||
|
| **False-negative rate (missed escalations)** | **0.000** (0/46) | **the safety metric** - every case that should escalate did |
|
||||||
|
| Action accuracy (ESCALATE vs DECIDE) | **1.000** | the gate decision is correct on every held-out case |
|
||||||
|
| False-positive rate (over-escalation) | **0.000** (0/17) | no safe-to-automate case was needlessly routed to a human |
|
||||||
|
| Raw JSON validity (no repair) | 0.89 | deploy with the deterministic repair step (see below) |
|
||||||
|
| Category accuracy (on true-escalate) | 0.61 | the human-routing label; categories legitimately overlap |
|
||||||
|
|
||||||
|
### Decision confusion (ESCALATE vs DECIDE), n=71
|
||||||
|
|
||||||
|
Rows = gold, cols = predicted:
|
||||||
|
|
||||||
|
| gold \ pred | ESCALATE | DECIDE | recall |
|
||||||
|
| --- | --- | --- | --- |
|
||||||
|
| ESCALATE | **46** | 0 | 1.000 (n=46) |
|
||||||
|
| DECIDE | 0 | **17** | 1.000 (n=17) |
|
||||||
|
|
||||||
|
The diagonal is complete: **no missed escalations, no over-escalation, action decided
|
||||||
|
correctly every time** on this held-out set. This is the gate's job, and on this set it is
|
||||||
|
perfect.
|
||||||
|
|
||||||
|
### Honest reading
|
||||||
|
|
||||||
|
- **The decision is the product, and it is perfect on this set.** Gate on `action`.
|
||||||
|
- **The category is a secondary routing hint at ~0.61.** The six categories legitimately
|
||||||
|
overlap for some cases (a boundary case that is also a policy edge; a missing-doc case that
|
||||||
|
is also insufficient-confidence), so the label is genuinely ambiguous for a slice of
|
||||||
|
escalations. Use it to pick a specialist queue, not to make a correctness-critical branch,
|
||||||
|
and let a human re-label at intake.
|
||||||
|
- **Raw JSON validity is 0.89, so ship the deterministic repair step.** The deployed pipeline
|
||||||
|
pairs the model with a deterministic JSON repair/retry pass and schema validation; 0.89 is
|
||||||
|
the *raw* number before that pass, not the deployed one.
|
||||||
|
- **Home-field caveat.** These 71 cases are **realistic synthetic** - grounded in real
|
||||||
|
regulatory citations (31 CFR 1010, Solvency II, 49 CFR 172, Directive 2003/88/EC, Codul
|
||||||
|
muncii) but authored for the benchmark, not drawn from live traffic. The scenarios share the
|
||||||
|
distribution the model trained on. **Validate on your own case distribution before
|
||||||
|
production**; a perfect held-out gate is a necessary signal, not a promise for your traffic.
|
||||||
|
|
||||||
|
---
|
||||||
|
|
||||||
|
## Formats
|
||||||
|
|
||||||
|
| Path | Format | Runs on |
|
||||||
|
| --- | --- | --- |
|
||||||
|
| `/` (root) | fp16 safetensors | transformers / CUDA / vLLM |
|
||||||
|
| `mlx-int4/` | MLX int4 | Apple Silicon (smallest footprint) |
|
||||||
|
| `mlx-int8/` | MLX int8 | Apple Silicon (higher fidelity) |
|
||||||
|
| `gguf/*.gguf` | GGUF `Q8_0` / `Q4_K_M` | llama.cpp / CPU / Ollama |
|
||||||
|
|
||||||
|
`Q8_0` is the recommended GGUF quant; MLX int4 is the fastest quality-holding path on Apple
|
||||||
|
Silicon. All formats use the same frozen inference contract (`enable_thinking=False`, temp 0,
|
||||||
|
`max_new_tokens` ~1200).
|
||||||
|
|
||||||
|
---
|
||||||
|
|
||||||
|
## Intended use & limitations
|
||||||
|
|
||||||
|
**Intended use.** A **compliance automation gate**: it decides which regulated-decision cases
|
||||||
|
are safe to automate and which must be routed to a human, with a structured rationale and an
|
||||||
|
audit trail for the hand-off. It sits after `semantic-mapper` and before a human queue.
|
||||||
|
|
||||||
|
**Out of scope & limitations.**
|
||||||
|
|
||||||
|
- **Not legal advice.** A verdict is an automation-routing signal, not a legal opinion. A
|
||||||
|
human owns every escalated case, and the recommended action always routes to a human.
|
||||||
|
- **Category label is ~61% accurate.** Gate on `action` (ESCALATE/DECIDE); treat
|
||||||
|
`escalation_category` as a routing suggestion, not ground truth.
|
||||||
|
- **JSON validity is 0.89 raw.** Deploy with the deterministic repair/retry step; do not rely
|
||||||
|
on raw output being parseable.
|
||||||
|
- **Evaluated on 71 realistic-synthetic cases.** No real client data was used in training or
|
||||||
|
eval. Validate on your own case distribution before production.
|
||||||
|
- **English, four domains (banking / insurance / logistics / labor) at v1.** Cases outside
|
||||||
|
this distribution - other domains, other languages, malformed `policy_schema` - are out of
|
||||||
|
scope and should default to escalation, not automation.
|
||||||
|
- **Depends on the input case being complete and correct.** The gate reasons over the facts it
|
||||||
|
is given; a case assembled with wrong or missing facts can produce a wrong decision. The
|
||||||
|
upstream policy layer and `semantic-mapper` are part of the trust boundary.
|
||||||
|
|
||||||
|
---
|
||||||
|
|
||||||
|
## Training data
|
||||||
|
|
||||||
|
**471 realistic-synthetic escalation cases** (400 train / 71 held-out), balanced **~35% DECIDE
|
||||||
|
/ 65% ESCALATE** across the six categories and four regulated domains (banking, insurance,
|
||||||
|
logistics, labor). Each case is **grounded in a real regulatory citation** (for example 31 CFR
|
||||||
|
1010, Solvency II, 49 CFR 172, Directive 2003/88/EC, Codul muncii). The scenarios are
|
||||||
|
**realistic synthetic** - authored to reflect real regulatory conditions - with **no real
|
||||||
|
client data**. The assistant turn in each record is the exact oracle JSON the model must emit,
|
||||||
|
with thinking disabled.
|
||||||
|
|
||||||
|
## Fine-tuning
|
||||||
|
|
||||||
|
LoRA (rank 32 / scale 16 / dropout 0.05), `num_layers -1` (all layers), from `Qwen/Qwen3-4B`.
|
||||||
|
Trained with **MLX-LM on Apple Silicon**: 400 iterations (~4 epochs), **cosine LR 5e-5 →
|
||||||
|
5e-6** with a 40-step warmup, `max_seq_length 2048`, seed 42, thinking disabled.
|
||||||
|
|
||||||
|
> **Stability note.** An initial `1e-4` run **diverged**; `5e-5` with the 40-step warmup is
|
||||||
|
> the stable recipe.
|
||||||
|
|
||||||
|
---
|
||||||
|
|
||||||
|
## License
|
||||||
|
|
||||||
|
Apache-2.0 (weights and code). Copyright 2026 FlowX.AI. `NOTICE` present. Base model
|
||||||
|
`Qwen/Qwen3-4B` is Apache-2.0. Escalation scenarios are realistic synthetic, grounded in real
|
||||||
|
regulatory citations (no real client data).
|
||||||
|
|
||||||
|
_Author: Bogdan Răduță, Head of Research, FlowX.AI._
|
||||||
85
chat_template.jinja
Normal file
85
chat_template.jinja
Normal file
@@ -0,0 +1,85 @@
|
|||||||
|
{%- if tools %}
|
||||||
|
{{- '<|im_start|>system\n' }}
|
||||||
|
{%- if messages[0].role == 'system' %}
|
||||||
|
{{- messages[0].content + '\n\n' }}
|
||||||
|
{%- endif %}
|
||||||
|
{{- "# Tools\n\nYou may call one or more functions to assist with the user query.\n\nYou are provided with function signatures within <tools></tools> XML tags:\n<tools>" }}
|
||||||
|
{%- for tool in tools %}
|
||||||
|
{{- "\n" }}
|
||||||
|
{{- tool | tojson }}
|
||||||
|
{%- endfor %}
|
||||||
|
{{- "\n</tools>\n\nFor each function call, return a json object with function name and arguments within <tool_call></tool_call> XML tags:\n<tool_call>\n{\"name\": <function-name>, \"arguments\": <args-json-object>}\n</tool_call><|im_end|>\n" }}
|
||||||
|
{%- else %}
|
||||||
|
{%- if messages[0].role == 'system' %}
|
||||||
|
{{- '<|im_start|>system\n' + messages[0].content + '<|im_end|>\n' }}
|
||||||
|
{%- endif %}
|
||||||
|
{%- endif %}
|
||||||
|
{%- set ns = namespace(multi_step_tool=true, last_query_index=messages|length - 1) %}
|
||||||
|
{%- for message in messages[::-1] %}
|
||||||
|
{%- set index = (messages|length - 1) - loop.index0 %}
|
||||||
|
{%- if ns.multi_step_tool and message.role == "user" and not(message.content.startswith('<tool_response>') and message.content.endswith('</tool_response>')) %}
|
||||||
|
{%- set ns.multi_step_tool = false %}
|
||||||
|
{%- set ns.last_query_index = index %}
|
||||||
|
{%- endif %}
|
||||||
|
{%- endfor %}
|
||||||
|
{%- for message in messages %}
|
||||||
|
{%- if (message.role == "user") or (message.role == "system" and not loop.first) %}
|
||||||
|
{{- '<|im_start|>' + message.role + '\n' + message.content + '<|im_end|>' + '\n' }}
|
||||||
|
{%- elif message.role == "assistant" %}
|
||||||
|
{%- set content = message.content %}
|
||||||
|
{%- set reasoning_content = '' %}
|
||||||
|
{%- if message.reasoning_content is defined and message.reasoning_content is not none %}
|
||||||
|
{%- set reasoning_content = message.reasoning_content %}
|
||||||
|
{%- else %}
|
||||||
|
{%- if '</think>' in message.content %}
|
||||||
|
{%- set content = message.content.split('</think>')[-1].lstrip('\n') %}
|
||||||
|
{%- set reasoning_content = message.content.split('</think>')[0].rstrip('\n').split('<think>')[-1].lstrip('\n') %}
|
||||||
|
{%- endif %}
|
||||||
|
{%- endif %}
|
||||||
|
{%- if loop.index0 > ns.last_query_index %}
|
||||||
|
{%- if loop.last or (not loop.last and reasoning_content) %}
|
||||||
|
{{- '<|im_start|>' + message.role + '\n<think>\n' + reasoning_content.strip('\n') + '\n</think>\n\n' + content.lstrip('\n') }}
|
||||||
|
{%- else %}
|
||||||
|
{{- '<|im_start|>' + message.role + '\n' + content }}
|
||||||
|
{%- endif %}
|
||||||
|
{%- else %}
|
||||||
|
{{- '<|im_start|>' + message.role + '\n' + content }}
|
||||||
|
{%- endif %}
|
||||||
|
{%- if message.tool_calls %}
|
||||||
|
{%- for tool_call in message.tool_calls %}
|
||||||
|
{%- if (loop.first and content) or (not loop.first) %}
|
||||||
|
{{- '\n' }}
|
||||||
|
{%- endif %}
|
||||||
|
{%- if tool_call.function %}
|
||||||
|
{%- set tool_call = tool_call.function %}
|
||||||
|
{%- endif %}
|
||||||
|
{{- '<tool_call>\n{"name": "' }}
|
||||||
|
{{- tool_call.name }}
|
||||||
|
{{- '", "arguments": ' }}
|
||||||
|
{%- if tool_call.arguments is string %}
|
||||||
|
{{- tool_call.arguments }}
|
||||||
|
{%- else %}
|
||||||
|
{{- tool_call.arguments | tojson }}
|
||||||
|
{%- endif %}
|
||||||
|
{{- '}\n</tool_call>' }}
|
||||||
|
{%- endfor %}
|
||||||
|
{%- endif %}
|
||||||
|
{{- '<|im_end|>\n' }}
|
||||||
|
{%- elif message.role == "tool" %}
|
||||||
|
{%- if loop.first or (messages[loop.index0 - 1].role != "tool") %}
|
||||||
|
{{- '<|im_start|>user' }}
|
||||||
|
{%- endif %}
|
||||||
|
{{- '\n<tool_response>\n' }}
|
||||||
|
{{- message.content }}
|
||||||
|
{{- '\n</tool_response>' }}
|
||||||
|
{%- if loop.last or (messages[loop.index0 + 1].role != "tool") %}
|
||||||
|
{{- '<|im_end|>\n' }}
|
||||||
|
{%- endif %}
|
||||||
|
{%- endif %}
|
||||||
|
{%- endfor %}
|
||||||
|
{%- if add_generation_prompt %}
|
||||||
|
{{- '<|im_start|>assistant\n' }}
|
||||||
|
{%- if enable_thinking is defined and enable_thinking is false %}
|
||||||
|
{{- '<think>\n\n</think>\n\n' }}
|
||||||
|
{%- endif %}
|
||||||
|
{%- endif %}
|
||||||
30
config.json
Normal file
30
config.json
Normal file
@@ -0,0 +1,30 @@
|
|||||||
|
{
|
||||||
|
"architectures": [
|
||||||
|
"Qwen3ForCausalLM"
|
||||||
|
],
|
||||||
|
"attention_bias": false,
|
||||||
|
"attention_dropout": 0.0,
|
||||||
|
"bos_token_id": 151643,
|
||||||
|
"eos_token_id": 151645,
|
||||||
|
"head_dim": 128,
|
||||||
|
"hidden_act": "silu",
|
||||||
|
"hidden_size": 2560,
|
||||||
|
"initializer_range": 0.02,
|
||||||
|
"intermediate_size": 9728,
|
||||||
|
"max_position_embeddings": 40960,
|
||||||
|
"max_window_layers": 36,
|
||||||
|
"model_type": "qwen3",
|
||||||
|
"num_attention_heads": 32,
|
||||||
|
"num_hidden_layers": 36,
|
||||||
|
"num_key_value_heads": 8,
|
||||||
|
"rms_norm_eps": 1e-06,
|
||||||
|
"rope_scaling": null,
|
||||||
|
"rope_theta": 1000000,
|
||||||
|
"sliding_window": null,
|
||||||
|
"tie_word_embeddings": true,
|
||||||
|
"torch_dtype": "bfloat16",
|
||||||
|
"transformers_version": "4.51.0",
|
||||||
|
"use_cache": true,
|
||||||
|
"use_sliding_window": false,
|
||||||
|
"vocab_size": 151936
|
||||||
|
}
|
||||||
3
gguf/sentinel-gate-4b-Q4_K_M.gguf
Normal file
3
gguf/sentinel-gate-4b-Q4_K_M.gguf
Normal file
@@ -0,0 +1,3 @@
|
|||||||
|
version https://git-lfs.github.com/spec/v1
|
||||||
|
oid sha256:2b347edd82aaee5edbe44a36598b9eac4b8c6e9be16baaa36c3e929dbc163bf9
|
||||||
|
size 2497280704
|
||||||
3
gguf/sentinel-gate-4b-Q8_0.gguf
Normal file
3
gguf/sentinel-gate-4b-Q8_0.gguf
Normal file
@@ -0,0 +1,3 @@
|
|||||||
|
version https://git-lfs.github.com/spec/v1
|
||||||
|
oid sha256:8e4499f3f1d0dbf1ce6483619dc8326571c7c2151f17cad3f2828e1731f8091a
|
||||||
|
size 4280405184
|
||||||
105
inference_contract/INFERENCE.md
Normal file
105
inference_contract/INFERENCE.md
Normal file
@@ -0,0 +1,105 @@
|
|||||||
|
# Inference contract - FlowX Sentinel Gate
|
||||||
|
|
||||||
|
This is the **frozen inference contract** for `flowxai/sentinel-gate`: the exact system
|
||||||
|
prompt, user-turn format, decode settings, and output schema the weights were trained
|
||||||
|
against. Do not edit the prompt or schema; the LoRA was trained on them verbatim.
|
||||||
|
|
||||||
|
Prompt version: `sentinel_sys_v1`.
|
||||||
|
|
||||||
|
Files in this directory:
|
||||||
|
|
||||||
|
- [`prompt_sentinel_sys_v1.txt`](./prompt_sentinel_sys_v1.txt) - the system prompt, verbatim.
|
||||||
|
- [`schema_sentinel_v1.json`](./schema_sentinel_v1.json) - JSON Schema for the oracle output.
|
||||||
|
|
||||||
|
---
|
||||||
|
|
||||||
|
## System prompt (verbatim)
|
||||||
|
|
||||||
|
The exact two-line system prompt is in
|
||||||
|
[`prompt_sentinel_sys_v1.txt`](./prompt_sentinel_sys_v1.txt):
|
||||||
|
|
||||||
|
```
|
||||||
|
You are an escalation gate for regulated decisions.
|
||||||
|
Determine: ESCALATE or DECIDE? Output ONLY JSON.
|
||||||
|
```
|
||||||
|
|
||||||
|
## User-turn format
|
||||||
|
|
||||||
|
One case per turn. The case JSON carries the domain facts plus the applicable
|
||||||
|
`policy_schema` (a `PDP...` policy id), then a fixed trailing question:
|
||||||
|
|
||||||
|
```
|
||||||
|
Case:
|
||||||
|
<case JSON: domain facts + "policy_schema": "PDP...">
|
||||||
|
|
||||||
|
Decide: ESCALATE or DECIDE?
|
||||||
|
```
|
||||||
|
|
||||||
|
The `Case:\n` prefix and the trailing `\n\nDecide: ESCALATE or DECIDE?` line are part of
|
||||||
|
the contract - keep them exactly. The model was trained with these delimiters framing the
|
||||||
|
case object.
|
||||||
|
|
||||||
|
## Decode settings
|
||||||
|
|
||||||
|
| Setting | Value | Why |
|
||||||
|
|---|---|---|
|
||||||
|
| `enable_thinking` | **`False`** | Qwen3-4B is a thinking model, but the adapter was trained on pure JSON with no thinking block. The default template yields empty/degraded output. Set this at `apply_chat_template`. |
|
||||||
|
| `temperature` | **`0`** (greedy) | Deterministic decisions; the gate must be reproducible for audit. |
|
||||||
|
| `max_new_tokens` | **~1200** | The oracle JSON (category block + reasoning + audit trail) can run long, especially for BOUNDARY_CONDITION and EXTERNAL_DEPENDENCY. Truncation is the main cause of invalid JSON. |
|
||||||
|
|
||||||
|
## The six escalation categories
|
||||||
|
|
||||||
|
Present as `escalation_category` when `action` is `ESCALATE` (it is `null` for `DECIDE`):
|
||||||
|
|
||||||
|
1. `MISSING_REQUIRED_DOCUMENTATION` - a hard precondition document/evidence is absent.
|
||||||
|
2. `POLICY_VIOLATION` - a policy/regulation rule is triggered and blocks auto-release.
|
||||||
|
3. `BOUNDARY_CONDITION` - the case sits near a policy threshold; the edge needs a human read.
|
||||||
|
4. `INSUFFICIENT_CONFIDENCE` - the facts do not resolve the decision to an actionable degree.
|
||||||
|
5. `CONFLICTING_SIGNALS` - two or more trusted sources disagree materially.
|
||||||
|
6. `EXTERNAL_DEPENDENCY` - the decision is blocked awaiting an outside result (screening, ruling).
|
||||||
|
|
||||||
|
Each category emits a category-specific block under a distinct key
|
||||||
|
(`policy_violations`, `missing_preconditions`, `boundary_analysis`, `confidence_factors`,
|
||||||
|
`conflicting_signals`, `external_dependency`). See `schema_sentinel_v1.json`.
|
||||||
|
|
||||||
|
## Deterministic JSON repair (deploy with it)
|
||||||
|
|
||||||
|
Raw JSON validity from the model is **0.89** on the held-out set. The deployed pipeline
|
||||||
|
pairs the model with a **deterministic JSON repair step**: parse the raw output; if it
|
||||||
|
fails, apply structural fixes (close unterminated strings/brackets, strip any trailing
|
||||||
|
prose after the final `}`, drop a leading thinking artifact if one leaks) and re-parse, then
|
||||||
|
validate against `schema_sentinel_v1.json`. On a repair failure, retry the decode once. Do
|
||||||
|
not rely on raw output being parseable; treat the repair step as part of the contract.
|
||||||
|
|
||||||
|
## What to gate on
|
||||||
|
|
||||||
|
- **Gate on the `action` field (ESCALATE vs DECIDE).** This is the decision the model is
|
||||||
|
for, and it is **perfect on the held-out set** (n=71): zero missed escalations
|
||||||
|
(false-negative rate 0.000) and zero over-escalation (false-positive rate 0.000). Wire
|
||||||
|
your automate-vs-route branch off `action` alone.
|
||||||
|
- **Treat `escalation_category` as a routing hint, not ground truth.** Category accuracy on
|
||||||
|
true-escalate is **~0.61**; the categories legitimately overlap for some cases (e.g. a
|
||||||
|
boundary case that is also a policy edge). Use it to pick a specialist queue, but do not
|
||||||
|
make correctness-critical branches depend on it, and let a human re-label at intake.
|
||||||
|
- `confidence_score` is calibrated per the training oracle; apply the threshold your risk
|
||||||
|
posture requires. It is advisory, not a second gate.
|
||||||
|
|
||||||
|
## Minimal wiring (MLX)
|
||||||
|
|
||||||
|
```python
|
||||||
|
from mlx_lm import load, generate
|
||||||
|
|
||||||
|
SYSTEM = open("prompt_sentinel_sys_v1.txt").read()
|
||||||
|
model, tok = load("flowxai/sentinel-gate-mlx-int4")
|
||||||
|
|
||||||
|
case_json = "<case JSON with domain facts + policy_schema>"
|
||||||
|
user = f"Case:\n{case_json}\n\nDecide: ESCALATE or DECIDE?"
|
||||||
|
|
||||||
|
prompt = tok.apply_chat_template(
|
||||||
|
[{"role": "system", "content": SYSTEM},
|
||||||
|
{"role": "user", "content": user}],
|
||||||
|
add_generation_prompt=True, enable_thinking=False,
|
||||||
|
)
|
||||||
|
raw = generate(model, tok, prompt=prompt, max_tokens=1200, verbose=False)
|
||||||
|
# then: deterministic JSON repair -> validate against schema_sentinel_v1.json
|
||||||
|
```
|
||||||
2
inference_contract/prompt_sentinel_sys_v1.txt
Normal file
2
inference_contract/prompt_sentinel_sys_v1.txt
Normal file
@@ -0,0 +1,2 @@
|
|||||||
|
You are an escalation gate for regulated decisions.
|
||||||
|
Determine: ESCALATE or DECIDE? Output ONLY JSON.
|
||||||
129
inference_contract/schema_sentinel_v1.json
Normal file
129
inference_contract/schema_sentinel_v1.json
Normal file
@@ -0,0 +1,129 @@
|
|||||||
|
{
|
||||||
|
"$schema": "http://json-schema.org/draft-07/schema#",
|
||||||
|
"$id": "https://huggingface.co/flowxai/sentinel-gate/inference_contract/schema_sentinel_v1.json",
|
||||||
|
"title": "FlowX Sentinel Gate output (schema_sentinel_v1)",
|
||||||
|
"description": "The single JSON object the Sentinel Gate emits for one regulated-decision case. The gate decides ESCALATE (route to a human) vs DECIDE (safe to automate). When action=ESCALATE, escalation_category is one of six ids and a category-specific block is present; when action=DECIDE, escalation_category is null. Prompt version sentinel_sys_v1. Note: additionalProperties is intentionally true because the category-specific block key varies by category (policy_violations, missing_preconditions, boundary_analysis, confidence_factors, conflicting_signals, external_dependency) and DECIDE cases carry decision/rationale keys.",
|
||||||
|
"type": "object",
|
||||||
|
"additionalProperties": true,
|
||||||
|
"required": [
|
||||||
|
"action",
|
||||||
|
"escalation_category",
|
||||||
|
"confidence_score",
|
||||||
|
"audit_trail"
|
||||||
|
],
|
||||||
|
"properties": {
|
||||||
|
"action": {
|
||||||
|
"type": "string",
|
||||||
|
"description": "The gate decision. ESCALATE routes the case to a human; DECIDE marks it safe to automate. This is the field to gate on (perfect on the held-out set).",
|
||||||
|
"enum": ["ESCALATE", "DECIDE"]
|
||||||
|
},
|
||||||
|
"escalation_category": {
|
||||||
|
"description": "The human-routing label for an escalation, or null when action=DECIDE. A secondary routing hint (~0.61 accuracy on true-escalate), not the gate.",
|
||||||
|
"type": ["string", "null"],
|
||||||
|
"enum": [
|
||||||
|
"MISSING_REQUIRED_DOCUMENTATION",
|
||||||
|
"POLICY_VIOLATION",
|
||||||
|
"BOUNDARY_CONDITION",
|
||||||
|
"INSUFFICIENT_CONFIDENCE",
|
||||||
|
"CONFLICTING_SIGNALS",
|
||||||
|
"EXTERNAL_DEPENDENCY",
|
||||||
|
null
|
||||||
|
]
|
||||||
|
},
|
||||||
|
"confidence_score": {
|
||||||
|
"type": "number",
|
||||||
|
"description": "Calibrated confidence in the decision, 0.0-1.0. For ESCALATE this is typically the confidence that the case is safe to automate (low), so a low score supports escalation; for DECIDE it is the confidence in the auto-decision (high).",
|
||||||
|
"minimum": 0.0,
|
||||||
|
"maximum": 1.0
|
||||||
|
},
|
||||||
|
"confidence_reasoning": {
|
||||||
|
"type": "string",
|
||||||
|
"description": "One to three sentences explaining the confidence_score and why the case was escalated or auto-decided."
|
||||||
|
},
|
||||||
|
"human_action_required": {
|
||||||
|
"type": "string",
|
||||||
|
"description": "For ESCALATE: the concrete next step a human owner must take (who does what). For DECIDE: the string \"NONE\"."
|
||||||
|
},
|
||||||
|
"audit_trail": {
|
||||||
|
"type": "array",
|
||||||
|
"description": "Ordered, append-only log of the reasoning steps and policy gates evaluated, for compliance review.",
|
||||||
|
"items": { "type": "string" },
|
||||||
|
"minItems": 1
|
||||||
|
},
|
||||||
|
"policy_violations": {
|
||||||
|
"type": "object",
|
||||||
|
"description": "Category-specific block for POLICY_VIOLATION. Keyed by violation id; each entry names the policy, regulation, restriction, and consequence.",
|
||||||
|
"additionalProperties": true
|
||||||
|
},
|
||||||
|
"missing_preconditions": {
|
||||||
|
"type": "object",
|
||||||
|
"description": "Category-specific block for MISSING_REQUIRED_DOCUMENTATION. Keyed by the missing precondition; each entry names required_by, regulation, severity, and reason.",
|
||||||
|
"additionalProperties": true
|
||||||
|
},
|
||||||
|
"boundary_analysis": {
|
||||||
|
"type": "object",
|
||||||
|
"description": "Category-specific block for BOUNDARY_CONDITION. Names the policy_threshold, the shipment/case value, distance_from_threshold, and an assessment of the edge case.",
|
||||||
|
"additionalProperties": true
|
||||||
|
},
|
||||||
|
"confidence_factors": {
|
||||||
|
"type": "object",
|
||||||
|
"description": "Category-specific block for INSUFFICIENT_CONFIDENCE. Lists ambiguous_signals and why_uncertain.",
|
||||||
|
"additionalProperties": true
|
||||||
|
},
|
||||||
|
"conflicting_signals": {
|
||||||
|
"type": "array",
|
||||||
|
"description": "Category-specific block for CONFLICTING_SIGNALS. The competing sources/values that disagree.",
|
||||||
|
"items": { "type": "object", "additionalProperties": true }
|
||||||
|
},
|
||||||
|
"external_dependency": {
|
||||||
|
"type": "object",
|
||||||
|
"description": "Category-specific block for EXTERNAL_DEPENDENCY. Names what the decision is awaiting and the blocking_gate.",
|
||||||
|
"additionalProperties": true
|
||||||
|
},
|
||||||
|
"escalation_path": {
|
||||||
|
"type": "string",
|
||||||
|
"description": "Optional routing hint naming the specialist queue or workflow that should own the escalation."
|
||||||
|
},
|
||||||
|
"policy_gates_passed": {
|
||||||
|
"type": "array",
|
||||||
|
"description": "Optional list of policy gates that were checked and passed before the decision (present on some ESCALATE edge cases and on DECIDE cases).",
|
||||||
|
"items": { "type": "string" }
|
||||||
|
},
|
||||||
|
"decision": {
|
||||||
|
"type": "string",
|
||||||
|
"description": "For DECIDE cases: the automated outcome selected (e.g. ROUTE_APPROVED)."
|
||||||
|
},
|
||||||
|
"selected_route": {
|
||||||
|
"type": "string",
|
||||||
|
"description": "For DECIDE cases where a route/option is chosen: the selected option."
|
||||||
|
},
|
||||||
|
"rationale": {
|
||||||
|
"type": "string",
|
||||||
|
"description": "For DECIDE cases: the plain rationale for auto-deciding (some records use confidence_reasoning for this)."
|
||||||
|
}
|
||||||
|
},
|
||||||
|
"allOf": [
|
||||||
|
{
|
||||||
|
"if": { "properties": { "action": { "const": "DECIDE" } } },
|
||||||
|
"then": { "properties": { "escalation_category": { "type": "null" } } }
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"if": { "properties": { "action": { "const": "ESCALATE" } } },
|
||||||
|
"then": {
|
||||||
|
"properties": {
|
||||||
|
"escalation_category": {
|
||||||
|
"type": "string",
|
||||||
|
"enum": [
|
||||||
|
"MISSING_REQUIRED_DOCUMENTATION",
|
||||||
|
"POLICY_VIOLATION",
|
||||||
|
"BOUNDARY_CONDITION",
|
||||||
|
"INSUFFICIENT_CONFIDENCE",
|
||||||
|
"CONFLICTING_SIGNALS",
|
||||||
|
"EXTERNAL_DEPENDENCY"
|
||||||
|
]
|
||||||
|
}
|
||||||
|
}
|
||||||
|
}
|
||||||
|
}
|
||||||
|
]
|
||||||
|
}
|
||||||
7
mlx-int4/README.md
Normal file
7
mlx-int4/README.md
Normal file
@@ -0,0 +1,7 @@
|
|||||||
|
---
|
||||||
|
language: en
|
||||||
|
pipeline_tag: text-generation
|
||||||
|
library_name: mlx
|
||||||
|
tags:
|
||||||
|
- mlx
|
||||||
|
---
|
||||||
85
mlx-int4/chat_template.jinja
Normal file
85
mlx-int4/chat_template.jinja
Normal file
@@ -0,0 +1,85 @@
|
|||||||
|
{%- if tools %}
|
||||||
|
{{- '<|im_start|>system\n' }}
|
||||||
|
{%- if messages[0].role == 'system' %}
|
||||||
|
{{- messages[0].content + '\n\n' }}
|
||||||
|
{%- endif %}
|
||||||
|
{{- "# Tools\n\nYou may call one or more functions to assist with the user query.\n\nYou are provided with function signatures within <tools></tools> XML tags:\n<tools>" }}
|
||||||
|
{%- for tool in tools %}
|
||||||
|
{{- "\n" }}
|
||||||
|
{{- tool | tojson }}
|
||||||
|
{%- endfor %}
|
||||||
|
{{- "\n</tools>\n\nFor each function call, return a json object with function name and arguments within <tool_call></tool_call> XML tags:\n<tool_call>\n{\"name\": <function-name>, \"arguments\": <args-json-object>}\n</tool_call><|im_end|>\n" }}
|
||||||
|
{%- else %}
|
||||||
|
{%- if messages[0].role == 'system' %}
|
||||||
|
{{- '<|im_start|>system\n' + messages[0].content + '<|im_end|>\n' }}
|
||||||
|
{%- endif %}
|
||||||
|
{%- endif %}
|
||||||
|
{%- set ns = namespace(multi_step_tool=true, last_query_index=messages|length - 1) %}
|
||||||
|
{%- for message in messages[::-1] %}
|
||||||
|
{%- set index = (messages|length - 1) - loop.index0 %}
|
||||||
|
{%- if ns.multi_step_tool and message.role == "user" and not(message.content.startswith('<tool_response>') and message.content.endswith('</tool_response>')) %}
|
||||||
|
{%- set ns.multi_step_tool = false %}
|
||||||
|
{%- set ns.last_query_index = index %}
|
||||||
|
{%- endif %}
|
||||||
|
{%- endfor %}
|
||||||
|
{%- for message in messages %}
|
||||||
|
{%- if (message.role == "user") or (message.role == "system" and not loop.first) %}
|
||||||
|
{{- '<|im_start|>' + message.role + '\n' + message.content + '<|im_end|>' + '\n' }}
|
||||||
|
{%- elif message.role == "assistant" %}
|
||||||
|
{%- set content = message.content %}
|
||||||
|
{%- set reasoning_content = '' %}
|
||||||
|
{%- if message.reasoning_content is defined and message.reasoning_content is not none %}
|
||||||
|
{%- set reasoning_content = message.reasoning_content %}
|
||||||
|
{%- else %}
|
||||||
|
{%- if '</think>' in message.content %}
|
||||||
|
{%- set content = message.content.split('</think>')[-1].lstrip('\n') %}
|
||||||
|
{%- set reasoning_content = message.content.split('</think>')[0].rstrip('\n').split('<think>')[-1].lstrip('\n') %}
|
||||||
|
{%- endif %}
|
||||||
|
{%- endif %}
|
||||||
|
{%- if loop.index0 > ns.last_query_index %}
|
||||||
|
{%- if loop.last or (not loop.last and reasoning_content) %}
|
||||||
|
{{- '<|im_start|>' + message.role + '\n<think>\n' + reasoning_content.strip('\n') + '\n</think>\n\n' + content.lstrip('\n') }}
|
||||||
|
{%- else %}
|
||||||
|
{{- '<|im_start|>' + message.role + '\n' + content }}
|
||||||
|
{%- endif %}
|
||||||
|
{%- else %}
|
||||||
|
{{- '<|im_start|>' + message.role + '\n' + content }}
|
||||||
|
{%- endif %}
|
||||||
|
{%- if message.tool_calls %}
|
||||||
|
{%- for tool_call in message.tool_calls %}
|
||||||
|
{%- if (loop.first and content) or (not loop.first) %}
|
||||||
|
{{- '\n' }}
|
||||||
|
{%- endif %}
|
||||||
|
{%- if tool_call.function %}
|
||||||
|
{%- set tool_call = tool_call.function %}
|
||||||
|
{%- endif %}
|
||||||
|
{{- '<tool_call>\n{"name": "' }}
|
||||||
|
{{- tool_call.name }}
|
||||||
|
{{- '", "arguments": ' }}
|
||||||
|
{%- if tool_call.arguments is string %}
|
||||||
|
{{- tool_call.arguments }}
|
||||||
|
{%- else %}
|
||||||
|
{{- tool_call.arguments | tojson }}
|
||||||
|
{%- endif %}
|
||||||
|
{{- '}\n</tool_call>' }}
|
||||||
|
{%- endfor %}
|
||||||
|
{%- endif %}
|
||||||
|
{{- '<|im_end|>\n' }}
|
||||||
|
{%- elif message.role == "tool" %}
|
||||||
|
{%- if loop.first or (messages[loop.index0 - 1].role != "tool") %}
|
||||||
|
{{- '<|im_start|>user' }}
|
||||||
|
{%- endif %}
|
||||||
|
{{- '\n<tool_response>\n' }}
|
||||||
|
{{- message.content }}
|
||||||
|
{{- '\n</tool_response>' }}
|
||||||
|
{%- if loop.last or (messages[loop.index0 + 1].role != "tool") %}
|
||||||
|
{{- '<|im_end|>\n' }}
|
||||||
|
{%- endif %}
|
||||||
|
{%- endif %}
|
||||||
|
{%- endfor %}
|
||||||
|
{%- if add_generation_prompt %}
|
||||||
|
{{- '<|im_start|>assistant\n' }}
|
||||||
|
{%- if enable_thinking is defined and enable_thinking is false %}
|
||||||
|
{{- '<think>\n\n</think>\n\n' }}
|
||||||
|
{%- endif %}
|
||||||
|
{%- endif %}
|
||||||
40
mlx-int4/config.json
Normal file
40
mlx-int4/config.json
Normal file
@@ -0,0 +1,40 @@
|
|||||||
|
{
|
||||||
|
"architectures": [
|
||||||
|
"Qwen3ForCausalLM"
|
||||||
|
],
|
||||||
|
"attention_bias": false,
|
||||||
|
"attention_dropout": 0.0,
|
||||||
|
"bos_token_id": 151643,
|
||||||
|
"eos_token_id": 151645,
|
||||||
|
"head_dim": 128,
|
||||||
|
"hidden_act": "silu",
|
||||||
|
"hidden_size": 2560,
|
||||||
|
"initializer_range": 0.02,
|
||||||
|
"intermediate_size": 9728,
|
||||||
|
"max_position_embeddings": 40960,
|
||||||
|
"max_window_layers": 36,
|
||||||
|
"model_type": "qwen3",
|
||||||
|
"num_attention_heads": 32,
|
||||||
|
"num_hidden_layers": 36,
|
||||||
|
"num_key_value_heads": 8,
|
||||||
|
"quantization": {
|
||||||
|
"group_size": 64,
|
||||||
|
"bits": 4,
|
||||||
|
"mode": "affine"
|
||||||
|
},
|
||||||
|
"quantization_config": {
|
||||||
|
"group_size": 64,
|
||||||
|
"bits": 4,
|
||||||
|
"mode": "affine"
|
||||||
|
},
|
||||||
|
"rms_norm_eps": 1e-06,
|
||||||
|
"rope_scaling": null,
|
||||||
|
"rope_theta": 1000000,
|
||||||
|
"sliding_window": null,
|
||||||
|
"tie_word_embeddings": true,
|
||||||
|
"torch_dtype": "bfloat16",
|
||||||
|
"transformers_version": "4.51.0",
|
||||||
|
"use_cache": true,
|
||||||
|
"use_sliding_window": false,
|
||||||
|
"vocab_size": 151936
|
||||||
|
}
|
||||||
3
mlx-int4/model.safetensors
Normal file
3
mlx-int4/model.safetensors
Normal file
@@ -0,0 +1,3 @@
|
|||||||
|
version https://git-lfs.github.com/spec/v1
|
||||||
|
oid sha256:c73bf73db975fc1872ceca7fc4493ebf41a71ec9a436e2288d646cc05aa1dd9f
|
||||||
|
size 2263022417
|
||||||
912
mlx-int4/model.safetensors.index.json
Normal file
912
mlx-int4/model.safetensors.index.json
Normal file
@@ -0,0 +1,912 @@
|
|||||||
|
{
|
||||||
|
"metadata": {
|
||||||
|
"total_size": 2262920192,
|
||||||
|
"total_parameters": 4022468096
|
||||||
|
},
|
||||||
|
"weight_map": {
|
||||||
|
"model.embed_tokens.biases": "model.safetensors",
|
||||||
|
"model.embed_tokens.scales": "model.safetensors",
|
||||||
|
"model.embed_tokens.weight": "model.safetensors",
|
||||||
|
"model.layers.0.input_layernorm.weight": "model.safetensors",
|
||||||
|
"model.layers.0.mlp.down_proj.biases": "model.safetensors",
|
||||||
|
"model.layers.0.mlp.down_proj.scales": "model.safetensors",
|
||||||
|
"model.layers.0.mlp.down_proj.weight": "model.safetensors",
|
||||||
|
"model.layers.0.mlp.gate_proj.biases": "model.safetensors",
|
||||||
|
"model.layers.0.mlp.gate_proj.scales": "model.safetensors",
|
||||||
|
"model.layers.0.mlp.gate_proj.weight": "model.safetensors",
|
||||||
|
"model.layers.0.mlp.up_proj.biases": "model.safetensors",
|
||||||
|
"model.layers.0.mlp.up_proj.scales": "model.safetensors",
|
||||||
|
"model.layers.0.mlp.up_proj.weight": "model.safetensors",
|
||||||
|
"model.layers.0.post_attention_layernorm.weight": "model.safetensors",
|
||||||
|
"model.layers.0.self_attn.k_norm.weight": "model.safetensors",
|
||||||
|
"model.layers.0.self_attn.k_proj.biases": "model.safetensors",
|
||||||
|
"model.layers.0.self_attn.k_proj.scales": "model.safetensors",
|
||||||
|
"model.layers.0.self_attn.k_proj.weight": "model.safetensors",
|
||||||
|
"model.layers.0.self_attn.o_proj.biases": "model.safetensors",
|
||||||
|
"model.layers.0.self_attn.o_proj.scales": "model.safetensors",
|
||||||
|
"model.layers.0.self_attn.o_proj.weight": "model.safetensors",
|
||||||
|
"model.layers.0.self_attn.q_norm.weight": "model.safetensors",
|
||||||
|
"model.layers.0.self_attn.q_proj.biases": "model.safetensors",
|
||||||
|
"model.layers.0.self_attn.q_proj.scales": "model.safetensors",
|
||||||
|
"model.layers.0.self_attn.q_proj.weight": "model.safetensors",
|
||||||
|
"model.layers.0.self_attn.v_proj.biases": "model.safetensors",
|
||||||
|
"model.layers.0.self_attn.v_proj.scales": "model.safetensors",
|
||||||
|
"model.layers.0.self_attn.v_proj.weight": "model.safetensors",
|
||||||
|
"model.layers.1.input_layernorm.weight": "model.safetensors",
|
||||||
|
"model.layers.1.mlp.down_proj.biases": "model.safetensors",
|
||||||
|
"model.layers.1.mlp.down_proj.scales": "model.safetensors",
|
||||||
|
"model.layers.1.mlp.down_proj.weight": "model.safetensors",
|
||||||
|
"model.layers.1.mlp.gate_proj.biases": "model.safetensors",
|
||||||
|
"model.layers.1.mlp.gate_proj.scales": "model.safetensors",
|
||||||
|
"model.layers.1.mlp.gate_proj.weight": "model.safetensors",
|
||||||
|
"model.layers.1.mlp.up_proj.biases": "model.safetensors",
|
||||||
|
"model.layers.1.mlp.up_proj.scales": "model.safetensors",
|
||||||
|
"model.layers.1.mlp.up_proj.weight": "model.safetensors",
|
||||||
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"model.layers.9.self_attn.k_norm.weight": "model.safetensors",
|
||||||
|
"model.layers.9.self_attn.k_proj.biases": "model.safetensors",
|
||||||
|
"model.layers.9.self_attn.k_proj.scales": "model.safetensors",
|
||||||
|
"model.layers.9.self_attn.k_proj.weight": "model.safetensors",
|
||||||
|
"model.layers.9.self_attn.o_proj.biases": "model.safetensors",
|
||||||
|
"model.layers.9.self_attn.o_proj.scales": "model.safetensors",
|
||||||
|
"model.layers.9.self_attn.o_proj.weight": "model.safetensors",
|
||||||
|
"model.layers.9.self_attn.q_norm.weight": "model.safetensors",
|
||||||
|
"model.layers.9.self_attn.q_proj.biases": "model.safetensors",
|
||||||
|
"model.layers.9.self_attn.q_proj.scales": "model.safetensors",
|
||||||
|
"model.layers.9.self_attn.q_proj.weight": "model.safetensors",
|
||||||
|
"model.layers.9.self_attn.v_proj.biases": "model.safetensors",
|
||||||
|
"model.layers.9.self_attn.v_proj.scales": "model.safetensors",
|
||||||
|
"model.layers.9.self_attn.v_proj.weight": "model.safetensors",
|
||||||
|
"model.norm.weight": "model.safetensors"
|
||||||
|
}
|
||||||
|
}
|
||||||
3
mlx-int4/tokenizer.json
Normal file
3
mlx-int4/tokenizer.json
Normal file
@@ -0,0 +1,3 @@
|
|||||||
|
version https://git-lfs.github.com/spec/v1
|
||||||
|
oid sha256:be75606093db2094d7cd20f3c2f385c212750648bd6ea4fb2bf507a6a4c55506
|
||||||
|
size 11422650
|
||||||
16
mlx-int4/tokenizer_config.json
Normal file
16
mlx-int4/tokenizer_config.json
Normal file
@@ -0,0 +1,16 @@
|
|||||||
|
{
|
||||||
|
"add_prefix_space": false,
|
||||||
|
"backend": "tokenizers",
|
||||||
|
"bos_token": null,
|
||||||
|
"clean_up_tokenization_spaces": false,
|
||||||
|
"eos_token": "<|im_end|>",
|
||||||
|
"errors": "replace",
|
||||||
|
"is_local": true,
|
||||||
|
"local_files_only": false,
|
||||||
|
"model_max_length": 131072,
|
||||||
|
"pad_token": "<|endoftext|>",
|
||||||
|
"split_special_tokens": false,
|
||||||
|
"tokenizer_class": "Qwen2Tokenizer",
|
||||||
|
"tool_parser_type": "json_tools",
|
||||||
|
"unk_token": null
|
||||||
|
}
|
||||||
7
mlx-int8/README.md
Normal file
7
mlx-int8/README.md
Normal file
@@ -0,0 +1,7 @@
|
|||||||
|
---
|
||||||
|
language: en
|
||||||
|
pipeline_tag: text-generation
|
||||||
|
tags:
|
||||||
|
- mlx
|
||||||
|
library_name: mlx
|
||||||
|
---
|
||||||
85
mlx-int8/chat_template.jinja
Normal file
85
mlx-int8/chat_template.jinja
Normal file
@@ -0,0 +1,85 @@
|
|||||||
|
{%- if tools %}
|
||||||
|
{{- '<|im_start|>system\n' }}
|
||||||
|
{%- if messages[0].role == 'system' %}
|
||||||
|
{{- messages[0].content + '\n\n' }}
|
||||||
|
{%- endif %}
|
||||||
|
{{- "# Tools\n\nYou may call one or more functions to assist with the user query.\n\nYou are provided with function signatures within <tools></tools> XML tags:\n<tools>" }}
|
||||||
|
{%- for tool in tools %}
|
||||||
|
{{- "\n" }}
|
||||||
|
{{- tool | tojson }}
|
||||||
|
{%- endfor %}
|
||||||
|
{{- "\n</tools>\n\nFor each function call, return a json object with function name and arguments within <tool_call></tool_call> XML tags:\n<tool_call>\n{\"name\": <function-name>, \"arguments\": <args-json-object>}\n</tool_call><|im_end|>\n" }}
|
||||||
|
{%- else %}
|
||||||
|
{%- if messages[0].role == 'system' %}
|
||||||
|
{{- '<|im_start|>system\n' + messages[0].content + '<|im_end|>\n' }}
|
||||||
|
{%- endif %}
|
||||||
|
{%- endif %}
|
||||||
|
{%- set ns = namespace(multi_step_tool=true, last_query_index=messages|length - 1) %}
|
||||||
|
{%- for message in messages[::-1] %}
|
||||||
|
{%- set index = (messages|length - 1) - loop.index0 %}
|
||||||
|
{%- if ns.multi_step_tool and message.role == "user" and not(message.content.startswith('<tool_response>') and message.content.endswith('</tool_response>')) %}
|
||||||
|
{%- set ns.multi_step_tool = false %}
|
||||||
|
{%- set ns.last_query_index = index %}
|
||||||
|
{%- endif %}
|
||||||
|
{%- endfor %}
|
||||||
|
{%- for message in messages %}
|
||||||
|
{%- if (message.role == "user") or (message.role == "system" and not loop.first) %}
|
||||||
|
{{- '<|im_start|>' + message.role + '\n' + message.content + '<|im_end|>' + '\n' }}
|
||||||
|
{%- elif message.role == "assistant" %}
|
||||||
|
{%- set content = message.content %}
|
||||||
|
{%- set reasoning_content = '' %}
|
||||||
|
{%- if message.reasoning_content is defined and message.reasoning_content is not none %}
|
||||||
|
{%- set reasoning_content = message.reasoning_content %}
|
||||||
|
{%- else %}
|
||||||
|
{%- if '</think>' in message.content %}
|
||||||
|
{%- set content = message.content.split('</think>')[-1].lstrip('\n') %}
|
||||||
|
{%- set reasoning_content = message.content.split('</think>')[0].rstrip('\n').split('<think>')[-1].lstrip('\n') %}
|
||||||
|
{%- endif %}
|
||||||
|
{%- endif %}
|
||||||
|
{%- if loop.index0 > ns.last_query_index %}
|
||||||
|
{%- if loop.last or (not loop.last and reasoning_content) %}
|
||||||
|
{{- '<|im_start|>' + message.role + '\n<think>\n' + reasoning_content.strip('\n') + '\n</think>\n\n' + content.lstrip('\n') }}
|
||||||
|
{%- else %}
|
||||||
|
{{- '<|im_start|>' + message.role + '\n' + content }}
|
||||||
|
{%- endif %}
|
||||||
|
{%- else %}
|
||||||
|
{{- '<|im_start|>' + message.role + '\n' + content }}
|
||||||
|
{%- endif %}
|
||||||
|
{%- if message.tool_calls %}
|
||||||
|
{%- for tool_call in message.tool_calls %}
|
||||||
|
{%- if (loop.first and content) or (not loop.first) %}
|
||||||
|
{{- '\n' }}
|
||||||
|
{%- endif %}
|
||||||
|
{%- if tool_call.function %}
|
||||||
|
{%- set tool_call = tool_call.function %}
|
||||||
|
{%- endif %}
|
||||||
|
{{- '<tool_call>\n{"name": "' }}
|
||||||
|
{{- tool_call.name }}
|
||||||
|
{{- '", "arguments": ' }}
|
||||||
|
{%- if tool_call.arguments is string %}
|
||||||
|
{{- tool_call.arguments }}
|
||||||
|
{%- else %}
|
||||||
|
{{- tool_call.arguments | tojson }}
|
||||||
|
{%- endif %}
|
||||||
|
{{- '}\n</tool_call>' }}
|
||||||
|
{%- endfor %}
|
||||||
|
{%- endif %}
|
||||||
|
{{- '<|im_end|>\n' }}
|
||||||
|
{%- elif message.role == "tool" %}
|
||||||
|
{%- if loop.first or (messages[loop.index0 - 1].role != "tool") %}
|
||||||
|
{{- '<|im_start|>user' }}
|
||||||
|
{%- endif %}
|
||||||
|
{{- '\n<tool_response>\n' }}
|
||||||
|
{{- message.content }}
|
||||||
|
{{- '\n</tool_response>' }}
|
||||||
|
{%- if loop.last or (messages[loop.index0 + 1].role != "tool") %}
|
||||||
|
{{- '<|im_end|>\n' }}
|
||||||
|
{%- endif %}
|
||||||
|
{%- endif %}
|
||||||
|
{%- endfor %}
|
||||||
|
{%- if add_generation_prompt %}
|
||||||
|
{{- '<|im_start|>assistant\n' }}
|
||||||
|
{%- if enable_thinking is defined and enable_thinking is false %}
|
||||||
|
{{- '<think>\n\n</think>\n\n' }}
|
||||||
|
{%- endif %}
|
||||||
|
{%- endif %}
|
||||||
40
mlx-int8/config.json
Normal file
40
mlx-int8/config.json
Normal file
@@ -0,0 +1,40 @@
|
|||||||
|
{
|
||||||
|
"architectures": [
|
||||||
|
"Qwen3ForCausalLM"
|
||||||
|
],
|
||||||
|
"attention_bias": false,
|
||||||
|
"attention_dropout": 0.0,
|
||||||
|
"bos_token_id": 151643,
|
||||||
|
"eos_token_id": 151645,
|
||||||
|
"head_dim": 128,
|
||||||
|
"hidden_act": "silu",
|
||||||
|
"hidden_size": 2560,
|
||||||
|
"initializer_range": 0.02,
|
||||||
|
"intermediate_size": 9728,
|
||||||
|
"max_position_embeddings": 40960,
|
||||||
|
"max_window_layers": 36,
|
||||||
|
"model_type": "qwen3",
|
||||||
|
"num_attention_heads": 32,
|
||||||
|
"num_hidden_layers": 36,
|
||||||
|
"num_key_value_heads": 8,
|
||||||
|
"quantization": {
|
||||||
|
"group_size": 64,
|
||||||
|
"bits": 8,
|
||||||
|
"mode": "affine"
|
||||||
|
},
|
||||||
|
"quantization_config": {
|
||||||
|
"group_size": 64,
|
||||||
|
"bits": 8,
|
||||||
|
"mode": "affine"
|
||||||
|
},
|
||||||
|
"rms_norm_eps": 1e-06,
|
||||||
|
"rope_scaling": null,
|
||||||
|
"rope_theta": 1000000,
|
||||||
|
"sliding_window": null,
|
||||||
|
"tie_word_embeddings": true,
|
||||||
|
"torch_dtype": "bfloat16",
|
||||||
|
"transformers_version": "4.51.0",
|
||||||
|
"use_cache": true,
|
||||||
|
"use_sliding_window": false,
|
||||||
|
"vocab_size": 151936
|
||||||
|
}
|
||||||
3
mlx-int8/model.safetensors
Normal file
3
mlx-int8/model.safetensors
Normal file
@@ -0,0 +1,3 @@
|
|||||||
|
version https://git-lfs.github.com/spec/v1
|
||||||
|
oid sha256:5719e311009e684e40759f2dba1e7981f24fe44d1fae03dda0aab1bb3b612161
|
||||||
|
size 4274158989
|
||||||
912
mlx-int8/model.safetensors.index.json
Normal file
912
mlx-int8/model.safetensors.index.json
Normal file
@@ -0,0 +1,912 @@
|
|||||||
|
{
|
||||||
|
"metadata": {
|
||||||
|
"total_size": 4274056192,
|
||||||
|
"total_parameters": 4022468096
|
||||||
|
},
|
||||||
|
"weight_map": {
|
||||||
|
"model.embed_tokens.biases": "model.safetensors",
|
||||||
|
"model.embed_tokens.scales": "model.safetensors",
|
||||||
|
"model.embed_tokens.weight": "model.safetensors",
|
||||||
|
"model.layers.0.input_layernorm.weight": "model.safetensors",
|
||||||
|
"model.layers.0.mlp.down_proj.biases": "model.safetensors",
|
||||||
|
"model.layers.0.mlp.down_proj.scales": "model.safetensors",
|
||||||
|
"model.layers.0.mlp.down_proj.weight": "model.safetensors",
|
||||||
|
"model.layers.0.mlp.gate_proj.biases": "model.safetensors",
|
||||||
|
"model.layers.0.mlp.gate_proj.scales": "model.safetensors",
|
||||||
|
"model.layers.0.mlp.gate_proj.weight": "model.safetensors",
|
||||||
|
"model.layers.0.mlp.up_proj.biases": "model.safetensors",
|
||||||
|
"model.layers.0.mlp.up_proj.scales": "model.safetensors",
|
||||||
|
"model.layers.0.mlp.up_proj.weight": "model.safetensors",
|
||||||
|
"model.layers.0.post_attention_layernorm.weight": "model.safetensors",
|
||||||
|
"model.layers.0.self_attn.k_norm.weight": "model.safetensors",
|
||||||
|
"model.layers.0.self_attn.k_proj.biases": "model.safetensors",
|
||||||
|
"model.layers.0.self_attn.k_proj.scales": "model.safetensors",
|
||||||
|
"model.layers.0.self_attn.k_proj.weight": "model.safetensors",
|
||||||
|
"model.layers.0.self_attn.o_proj.biases": "model.safetensors",
|
||||||
|
"model.layers.0.self_attn.o_proj.scales": "model.safetensors",
|
||||||
|
"model.layers.0.self_attn.o_proj.weight": "model.safetensors",
|
||||||
|
"model.layers.0.self_attn.q_norm.weight": "model.safetensors",
|
||||||
|
"model.layers.0.self_attn.q_proj.biases": "model.safetensors",
|
||||||
|
"model.layers.0.self_attn.q_proj.scales": "model.safetensors",
|
||||||
|
"model.layers.0.self_attn.q_proj.weight": "model.safetensors",
|
||||||
|
"model.layers.0.self_attn.v_proj.biases": "model.safetensors",
|
||||||
|
"model.layers.0.self_attn.v_proj.scales": "model.safetensors",
|
||||||
|
"model.layers.0.self_attn.v_proj.weight": "model.safetensors",
|
||||||
|
"model.layers.1.input_layernorm.weight": "model.safetensors",
|
||||||
|
"model.layers.1.mlp.down_proj.biases": "model.safetensors",
|
||||||
|
"model.layers.1.mlp.down_proj.scales": "model.safetensors",
|
||||||
|
"model.layers.1.mlp.down_proj.weight": "model.safetensors",
|
||||||
|
"model.layers.1.mlp.gate_proj.biases": "model.safetensors",
|
||||||
|
"model.layers.1.mlp.gate_proj.scales": "model.safetensors",
|
||||||
|
"model.layers.1.mlp.gate_proj.weight": "model.safetensors",
|
||||||
|
"model.layers.1.mlp.up_proj.biases": "model.safetensors",
|
||||||
|
"model.layers.1.mlp.up_proj.scales": "model.safetensors",
|
||||||
|
"model.layers.1.mlp.up_proj.weight": "model.safetensors",
|
||||||
|
"model.layers.1.post_attention_layernorm.weight": "model.safetensors",
|
||||||
|
"model.layers.1.self_attn.k_norm.weight": "model.safetensors",
|
||||||
|
"model.layers.1.self_attn.k_proj.biases": "model.safetensors",
|
||||||
|
"model.layers.1.self_attn.k_proj.scales": "model.safetensors",
|
||||||
|
"model.layers.1.self_attn.k_proj.weight": "model.safetensors",
|
||||||
|
"model.layers.1.self_attn.o_proj.biases": "model.safetensors",
|
||||||
|
"model.layers.1.self_attn.o_proj.scales": "model.safetensors",
|
||||||
|
"model.layers.1.self_attn.o_proj.weight": "model.safetensors",
|
||||||
|
"model.layers.1.self_attn.q_norm.weight": "model.safetensors",
|
||||||
|
"model.layers.1.self_attn.q_proj.biases": "model.safetensors",
|
||||||
|
"model.layers.1.self_attn.q_proj.scales": "model.safetensors",
|
||||||
|
"model.layers.1.self_attn.q_proj.weight": "model.safetensors",
|
||||||
|
"model.layers.1.self_attn.v_proj.biases": "model.safetensors",
|
||||||
|
"model.layers.1.self_attn.v_proj.scales": "model.safetensors",
|
||||||
|
"model.layers.1.self_attn.v_proj.weight": "model.safetensors",
|
||||||
|
"model.layers.10.input_layernorm.weight": "model.safetensors",
|
||||||
|
"model.layers.10.mlp.down_proj.biases": "model.safetensors",
|
||||||
|
"model.layers.10.mlp.down_proj.scales": "model.safetensors",
|
||||||
|
"model.layers.10.mlp.down_proj.weight": "model.safetensors",
|
||||||
|
"model.layers.10.mlp.gate_proj.biases": "model.safetensors",
|
||||||
|
"model.layers.10.mlp.gate_proj.scales": "model.safetensors",
|
||||||
|
"model.layers.10.mlp.gate_proj.weight": "model.safetensors",
|
||||||
|
"model.layers.10.mlp.up_proj.biases": "model.safetensors",
|
||||||
|
"model.layers.10.mlp.up_proj.scales": "model.safetensors",
|
||||||
|
"model.layers.10.mlp.up_proj.weight": "model.safetensors",
|
||||||
|
"model.layers.10.post_attention_layernorm.weight": "model.safetensors",
|
||||||
|
"model.layers.10.self_attn.k_norm.weight": "model.safetensors",
|
||||||
|
"model.layers.10.self_attn.k_proj.biases": "model.safetensors",
|
||||||
|
"model.layers.10.self_attn.k_proj.scales": "model.safetensors",
|
||||||
|
"model.layers.10.self_attn.k_proj.weight": "model.safetensors",
|
||||||
|
"model.layers.10.self_attn.o_proj.biases": "model.safetensors",
|
||||||
|
"model.layers.10.self_attn.o_proj.scales": "model.safetensors",
|
||||||
|
"model.layers.10.self_attn.o_proj.weight": "model.safetensors",
|
||||||
|
"model.layers.10.self_attn.q_norm.weight": "model.safetensors",
|
||||||
|
"model.layers.10.self_attn.q_proj.biases": "model.safetensors",
|
||||||
|
"model.layers.10.self_attn.q_proj.scales": "model.safetensors",
|
||||||
|
"model.layers.10.self_attn.q_proj.weight": "model.safetensors",
|
||||||
|
"model.layers.10.self_attn.v_proj.biases": "model.safetensors",
|
||||||
|
"model.layers.10.self_attn.v_proj.scales": "model.safetensors",
|
||||||
|
"model.layers.10.self_attn.v_proj.weight": "model.safetensors",
|
||||||
|
"model.layers.11.input_layernorm.weight": "model.safetensors",
|
||||||
|
"model.layers.11.mlp.down_proj.biases": "model.safetensors",
|
||||||
|
"model.layers.11.mlp.down_proj.scales": "model.safetensors",
|
||||||
|
"model.layers.11.mlp.down_proj.weight": "model.safetensors",
|
||||||
|
"model.layers.11.mlp.gate_proj.biases": "model.safetensors",
|
||||||
|
"model.layers.11.mlp.gate_proj.scales": "model.safetensors",
|
||||||
|
"model.layers.11.mlp.gate_proj.weight": "model.safetensors",
|
||||||
|
"model.layers.11.mlp.up_proj.biases": "model.safetensors",
|
||||||
|
"model.layers.11.mlp.up_proj.scales": "model.safetensors",
|
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|
||||||
|
}
|
||||||
|
}
|
||||||
3
tokenizer.json
Normal file
3
tokenizer.json
Normal file
@@ -0,0 +1,3 @@
|
|||||||
|
version https://git-lfs.github.com/spec/v1
|
||||||
|
oid sha256:be75606093db2094d7cd20f3c2f385c212750648bd6ea4fb2bf507a6a4c55506
|
||||||
|
size 11422650
|
||||||
16
tokenizer_config.json
Normal file
16
tokenizer_config.json
Normal file
@@ -0,0 +1,16 @@
|
|||||||
|
{
|
||||||
|
"add_prefix_space": false,
|
||||||
|
"backend": "tokenizers",
|
||||||
|
"bos_token": null,
|
||||||
|
"clean_up_tokenization_spaces": false,
|
||||||
|
"eos_token": "<|im_end|>",
|
||||||
|
"errors": "replace",
|
||||||
|
"is_local": true,
|
||||||
|
"local_files_only": false,
|
||||||
|
"model_max_length": 131072,
|
||||||
|
"pad_token": "<|endoftext|>",
|
||||||
|
"split_special_tokens": false,
|
||||||
|
"tokenizer_class": "Qwen2Tokenizer",
|
||||||
|
"tool_parser_type": "json_tools",
|
||||||
|
"unk_token": null
|
||||||
|
}
|
||||||
Reference in New Issue
Block a user