Model card, licence, charts

LICENSE

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+Copyright 2026 Dr. Sathiskumar JOTHI
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README.md

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+---
+license: apache-2.0
+language:
+- en
+library_name: bev-decisions
+pipeline_tag: text-classification
+base_model: google/gemma-4-26B-A4B-it
+tags:
+- bev
+- system-one
+- decision-model
+- typed-decisions
+- calibrated-probabilities
+- classification
+- routing
+- guardrails
+- triage
+- commercial-use
+- gemma4
+- mixture-of-experts
+model-index:
+- name: Bev-Pro-C
+  results:
+  - task:
+      type: text-classification
+    dataset:
+      name: Bev-Bench (operations documents)
+      type: bench
+    metrics:
+    - type: accuracy
+      value: 0.9189
+  - task:
+      type: text-classification
+    dataset:
+      name: Typed decisions
+      type: td
+    metrics:
+    - type: accuracy
+      value: 0.7925
+  - task:
+      type: text-classification
+    dataset:
+      name: Rules-Bench (rules shown)
+      type: rules
+    metrics:
+    - type: accuracy
+      value: 0.9200
+  - task:
+      type: text-classification
+    dataset:
+      name: JailbreakBench refusal
+      type: jbb
+    metrics:
+    - type: accuracy
+      value: 0.8400
+  - task:
+      type: text-classification
+    dataset:
+      name: Aegis 2.0 request safety
+      type: safety_aegis
+    metrics:
+    - type: accuracy
+      value: 0.7817
+---
+
+# Bev-Pro-C
+
+[![PyPI](https://img.shields.io/pypi/v/bev-decisions?label=PyPI)](https://pypi.org/project/bev-decisions/)
+[![GitHub](https://img.shields.io/badge/GitHub-bev--decisions-181717?logo=github)](https://github.com/sathiskumarjothi0-oss/bev-decisions-pypi)
+[![License](https://img.shields.io/badge/License-Apache%202.0-green.svg)](https://opensource.org/licenses/Apache-2.0)
+[![Buy Me A Coffee](https://img.shields.io/badge/Buy%20Me%20A%20Coffee-sjothi-FFDD00?logo=buy-me-a-coffee&logoColor=black)](https://www.buymeacoffee.com/sjothi)
+
+**A System One decision model with open weights.** Give it a document (text, email, ticket, log or JSON) and typed
+questions; it returns typed answers (yes/no, a choice between options you describe, or a score on levels you define)
+with a calibrated probability for every option, in one pass. It never generates text, so there is nothing to parse and
+nothing to hallucinate. It runs on your own machines: your documents never leave them.
+
+About 26B parameters: Google Gemma 4 26B-A4B (mixture of experts, about 4B active per token) with Bev's decision adapter. Both are in this repository, so nothing else is downloaded. English text, up to 4,096 input tokens; needs one GPU with about 60 GB of memory (bf16).
+
+| Model | Size | Runs on | Best for | Status |
+|---|---|---|---|---|
+| Bev-Large-C | 0.4B (ModernBERT-large base) | laptop CPU/GPU, edge boxes, any GPU | operations documents, logs and alerts, counting, amounts and limits, ticket routing, request safety | coming soon |
+| **[Bev-Pro-C](https://huggingface.co/sathiskumarjothi0/Bev-Pro-C)** | 26B (Gemma 4 26B-A4B base) | one GPU with about 60 GB | knowledge-heavy decisions: contracts, finance topics, large intent sets, dates and payment status | available |
+
+## Installation
+
+```bash
+python -m pip install "bev-decisions[pro]"
+```
+
+Python 3.10 or newer. Optional extras: `bev-decisions[serve]` (HTTP server), `bev-decisions[mcp]` (MCP server for AI
+assistants and agents), `bev-decisions[structured]` (Pydantic schemas), `bev-decisions[all]`. The first `bev.load()`
+downloads the model from this repository and caches it; after that it works offline.
+
+## Quickstart
+
+```python
+import bev
+
+model = bev.load("sathiskumarjothi0/Bev-Pro-C")
+answers = model.decide(
+    "Subject: Charged twice for order 88412. I was billed $49.99 twice on 3 October. Please refund one payment today.",
+    {
+        "team":     {"type": "choice", "instructions": "Which team should handle this ticket?",
+                     "criteria": {"billing": "payments, invoices, refunds", "tech": "bugs, outages, errors", "sales": "pricing and plans"}},
+        "urgent":   {"type": "noul", "instructions": "Does the customer ask for urgent handling?"},
+        "severity": {"type": "score", "instructions": "How severe is the problem for the customer?", "criteria": ["low", "medium", "high"]},
+    },
+)
+answers["team"]   # {"choice": ..., "confidence": ..., "probabilities": {...}}
+```
+
+| Type | Use it for | Answer |
+|---|---|---|
+| `noul` | yes or no | `noul`: probability of yes |
+| `choice` | one of the options you define (`criteria`: key → description, up to 300) | `choice`, `confidence`, `probabilities` |
+| `score` | ordered levels (`criteria`: list or key → description, lowest first) | `label`, `score` (expected level), `confidence`, `probabilities` |
+
+Up to 16 questions per call share one read of the document.
+
+## Confidence gating
+
+```python
+sure, review = bev.gate(answers, threshold=0.8)   # act on `sure`, send `review` to a person
+```
+
+## Route mode
+
+Route mode sends every question to Bev-Large-C first and only the unsure answers to Bev-Pro-C:
+
+```python
+router = bev.Router(load_strong=True, threshold=0.8)
+result = router.decide(text, questions)
+result["answers"], result["routing"]          # routing: {"team": "fast", "urgent": "strong"}
+```
+
+Route mode needs Bev-Large-C, which is coming soon.
+
+## Schema-driven decisions
+
+```python
+from typing import Literal
+from pydantic import BaseModel, Field
+
+class Ticket(BaseModel):
+    urgent: bool = Field(description="Does the customer ask for urgent handling?")
+    team: Literal["billing", "tech", "sales"] = Field(description="Which team should handle this ticket?")
+
+ticket = bev.decide_as(model, text, Ticket)       # Ticket(urgent=True, team='billing')
+```
+
+## Self-hosting: HTTP and MCP
+
+```bash
+pip install "bev-decisions[serve]"
+bev serve --model sathiskumarjothi0/Bev-Pro-C --port 8000      # POST /v1/decide, /v1/batch; GET /v1/presets, /health
+pip install "bev-decisions[mcp]"
+bev mcp --model sathiskumarjothi0/Bev-Pro-C                    # tools: decide, decide_preset, list_presets
+```
+
+The server listens on 127.0.0.1 without authentication; put it behind your own gateway before exposing it. Docker files
+and examples are in the [GitHub repository](https://github.com/sathiskumarjothi0-oss/bev-decisions-pypi).
+
+## Benchmarks
+
+![Accuracy on held-out tests](assets/benchmark.png)
+
+Accuracy (%) = correct answers ÷ questions on held-out test sets; the same cases for every system (October 2026). Bold =
+best in the row. * = an area Bev was not built for.
+
+| Test | Bev-Pro-C | Mistral Large 4 | Mistral Large 3 | gpt-oss 120B | Gemma 4 31B | Nemotron 3 Super |
+|---|---|---|---|---|---|---|
+| Bev-Bench (operations documents) | **91.9** | 83.7 | 74.6 | 91.1 | 88.9 | 70.6 |
+| Typed decisions | **79.2** | 72.0 | 68.0 | 68.8 | 75.0 | 62.7 |
+| Rules-Bench (rules shown) | **92.0** | – | – | – | – | – |
+| JailbreakBench refusal | 84.0 | 77.5 | 62.0 | 68.0 | **87.5** | 65.0 |
+| Aegis 2.0 request safety | **78.2** | 70.5 | 66.9 | 69.5 | 73.1 | 69.3 |
+
+| Test | Bev-Pro-C | Mistral Large 4 | Mistral Large 3 | gpt-oss 120B | Gemma 4 31B | Nemotron 3 Super |
+|---|---|---|---|---|---|---|
+| AG News | 90.6 | 90.8 | 88.8 | 88.8 | **91.8** | 87.8 |
+| Emotion | 56.2 | 55.8 | 55.6 | **57.8** | 55.4 | 50.2 |
+| Banking77 (77 intents) | 70.6 | 75.8 | 71.2 | **81.0** | 78.8 | 69.2 |
+| SST-5 (5-level sentiment) | 51.8 | 51.4 | 49.4 | 49.6 | **53.2** | 46.8 |
+| XNLI (English) | 86.4 | 84.8 | 77.6 | 80.2 | **89.2** | 75.0 |
+| MASSIVE intents | 74.8 | 78.4 | 75.2 | 81.6 | **83.8** | 69.4 |
+
+| Test | Bev-Pro-C | Mistral Large 4 | Mistral Large 3 | gpt-oss 120B | Gemma 4 31B | Nemotron 3 Super |
+|---|---|---|---|---|---|---|
+| Product reviews* | 96.4 | 96.4 | 96.6 | 95.4 | **97.2** | 94.8 |
+| Unfair contract terms* | **76.0** | 54.6 | 52.8 | 47.6 | 74.4 | 44.0 |
+| Financial-news sentiment* | 59.2 | 64.0 | 50.4 | 68.4 | **71.2** | 64.0 |
+| Poem sentiment* | **65.1** | 42.6 | 32.1 | 59.8 | 62.7 | 45.5 |
+| Support intents* | 88.0 | 85.4 | 86.2 | 90.0 | **90.4** | 76.6 |
+| Toxic comments* | 79.2 | 68.0 | 73.0 | **80.8** | 76.4 | 69.6 |
+| Financial-news topics* | **69.8** | 61.8 | 61.6 | 63.4 | 64.2 | 57.2 |
+| Legal case holdings* | 71.2 | 69.2 | **72.2** | 61.2 | 71.6 | 55.0 |
+
+Other systems: five LLMs through Ollama Cloud at temperature 0 (Mistral Large 4 preview, Mistral Large 3, gpt-oss 120B,
+Gemma 4 31B, Nemotron 3 Super), asked the same questions with the same options.
+
+### Bev-Bench, question by question
+
+Averages hide weak spots. Bold = within 10 points of always answering the most common label, i.e. little or no skill.
+
+| Question (300 cases each) | Bev-Pro-C | Always-most-common baseline |
+|---|---|---|
+| Invoice: payment status vs due date | 100.0% | 38.3% |
+| Security log: attack category | 100.0% | 20.7% |
+| Security log: severity | 100.0% | 39.0% |
+| Outage alert: root cause | 100.0% | 5.3% |
+| Outage alert: SLO breached? | 100.0% | 82.0% |
+| Ticket: urgent? | 100.0% | 66.0% |
+| Which date is earlier | 100.0% | 50.0% |
+| Choosing among many options | 100.0% | 0.7% |
+| Comparing two amounts | 99.3% | 52.0% |
+| Ticket: which department | 94.7% | 16.0% |
+| Security log: failed-login count | 91.3% | 59.7% |
+| Invoice: how late | 89.3% | 35.3% |
+| Invoice: total over a limit? | 85.0% | 51.3% |
+| Invoice: items above a price | 82.7% | 14.7% |
+| Long log: intrusion? | 74.0% | 52.7% |
+| Ticket: sentiment | 70.3% | 24.3% |
+| Counting items in a list | 62.7% | 10.0% |
+
+### Calibration
+
+![Calibration](assets/reliability.png)
+
+| Test | Accuracy | Answered at confidence ≥ 0.8 | Accuracy of those | ECE |
+|---|---|---|---|---|
+| Bev-Bench (operations documents) | 91.9% | 89.7% | 95.0% | 0.032 |
+| Rules-Bench (rules shown) | 92.0% | 87.2% | 97.0% | 0.011 |
+| Aegis 2.0 request safety | 78.2% | 51.1% | 92.6% | 0.024 |
+| Product reviews | 96.4% | 93.0% | 97.9% | 0.014 |
+| Support intents | 88.0% | 98.8% | 88.3% | 0.105 |
+| Typed decisions | 79.2% | 61.7% | 91.5% | 0.040 |
+
+ECE (expected calibration error) = Σ over 10 confidence bins of (share of answers in the bin × |accuracy − mean
+confidence|); 0 is perfect. Example: a bin holding 40% of the answers at mean confidence 0.95 with 90% of them right adds
+0.4 × 0.05 = 0.02. Calibration temperatures: noul 1.0, choice 1.2, score 1.2.
+
+## Package check
+
+This repository, loaded with the `bev-decisions` package, was checked against the official evaluation run (accuracy = correct ÷ questions on the same cases):
+
+| Test | Questions | Package | Official run |
+|---|---|---|---|
+| Bev-Bench (operations documents) | 1,200 | 88.9% | 89.2% |
+| Typed decisions | 1,500 | 79.9% | 79.3% |
+| Product reviews | 300 | 97.3% | 98.0% |
+
+A few answers that are close to a tie can change with batch size on a mixture-of-experts model in bf16; accuracy stays within 1 point.
+
+## Honest limits
+
+- Knowledge-heavy decisions are not solved: legal case holdings 71.2%, financial-news topics 69.8%.
+- Questions with more than 26 options are answered as a tournament (a winner per group of 26, then a final); accuracy is lower there (Banking77 70.6%) and confidence less well calibrated.
+- Five-level sentiment is hard (SST-5 51.8%).
+- Needs a large GPU; for laptops and edge boxes use Bev-Large-C (coming soon).
+- English only; text beyond 4,096 tokens is cut; no images.
+- Not for medical, legal or financial advice. For decisions with serious consequences, validate on your own data
+  (`bev eval my_cases.jsonl`) and keep a person in the loop.
+
+## Files
+
+| File | Contents |
+|---|---|
+| `model-*.safetensors`, `config.json`, tokenizer files | Gemma 4 26B-A4B, unchanged (bf16) |
+| `adapter/` | Bev's decision adapter, applied when the model loads |
+| `bev_config.json` | Bev settings and calibration temperatures |
+| `NOTICE` | Gemma attribution (Apache-2.0) |
+| `assets/` | the charts on this page |
+
+## Links
+
+- Package: [pypi.org/project/bev-decisions](https://pypi.org/project/bev-decisions/)
+- Code and examples: [github.com/sathiskumarjothi0-oss/bev-decisions-pypi](https://github.com/sathiskumarjothi0-oss/bev-decisions-pypi)
+
+## Support
+
+If Bev is useful to you, you can support its development: [buymeacoffee.com/sjothi](https://www.buymeacoffee.com/sjothi)
+
+## Licence and credit
+
+Apache-2.0, © 2026 Dr. Sathiskumar JOTHI. Built on [Gemma 4 26B-A4B](https://huggingface.co/google/gemma-4-26B-A4B-it) (Apache-2.0) by Google, included unchanged with Bev's decision adapter, see `NOTICE`. Bev is named after Sir William Beveridge (1879–1963), British
+economist, whose 1942 Beveridge Report laid the foundations of the welfare state and the NHS.