Beveridge-Pro-C-Web: decision adapter, config, card

LICENSE

@@ -0,0 +1,204 @@
+Copyright 2026 Dr. Sathiskumar JOTHI
+
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NOTICE

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+Beveridge-Pro-C-Web
+Copyright 2026 Dr. Sathiskumar JOTHI
+
+Beveridge-Pro-C-Web is a decision adapter (adapter/) for Gemma 4 26B-A4B by Google
+(https://huggingface.co/google/gemma-4-26B-A4B-it), licensed under the Apache License, Version 2.0.
+The Gemma weights are not included in this repository: they are downloaded unchanged from Google's
+repository when the model is loaded, and Beveridge's adapter is applied on top.

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:
+- beveridge
+- system-one
+- decision-model
+- typed-decisions
+- calibrated-probabilities
+- web-agent
+- browser-automation
+- computer-use
+- peft
+- lora
+- gemma4
+- commercial-use
+model-index:
+- name: Beveridge-Pro-C-Web
+  results:
+  - task:
+      type: text-classification
+    dataset:
+      name: Web steps on site types never seen in training
+      type: webnav
+    metrics:
+    - type: accuracy
+      value: 0.982
+  - task:
+      type: text-classification
+    dataset:
+      name: Beveridge-Bench (operations documents)
+      type: bench
+    metrics:
+    - type: accuracy
+      value: 0.9209
+---
+
+<img src="assets/header.png" alt="Beveridge-Pro-C-Web: Beveridge logo and model name" width="560">
+
+[![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)
+[![Website](https://img.shields.io/badge/Website-bev.sjothi.com-23614f)](https://bev.sjothi.com/)
+[![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)
+
+**Beveridge-Pro-C with web browsing added.** A System One decision model with open weights that picks the next step of a
+browser agent: what to do (click, type, choose from a list, wait, done) and on which element, with a calibrated probability
+for every option. It answers in one scoring pass per question and never generates text, so the agent always gets one of the
+actions that are actually on the page.
+
+It keeps everything Beveridge-Pro-C does on documents (Beveridge-Bench 92.1%, Beveridge-Pro-C 91.9%), so one model serves both.
+
+This repository holds only Beveridge's decision adapter (about 46 MB). Gemma 4 26B-A4B is downloaded unchanged from
+[Google's repository](https://huggingface.co/google/gemma-4-26B-A4B-it) when you load the model. English, up to 4,096
+input tokens; needs one GPU with about 60 GB of memory (bf16).
+
+| Model | Size | Runs on | Best for | Status |
+|---|---|---|---|---|
+| [Beveridge-Large-C](https://huggingface.co/sathiskumarjothi0/Beveridge-Large-C) | 0.4B (ModernBERT-large base) | laptop CPU/GPU, edge boxes, any GPU | operations documents, logs and alerts, ticket routing, request safety | available |
+| [Beveridge-Pro-C](https://huggingface.co/sathiskumarjothi0/Beveridge-Pro-C) | 26B (Gemma 4 26B-A4B base) | one GPU with about 60 GB | knowledge-heavy decisions on documents | available |
+| **[Beveridge-Pro-C-Web](https://huggingface.co/sathiskumarjothi0/Beveridge-Pro-C-Web)** | 26B (Gemma 4 26B-A4B base) | one GPU with about 60 GB | next step of a browser agent, plus everything Beveridge-Pro-C does | available |
+
+## Installation
+
+```bash
+python -m pip install "bev-decisions[pro]"
+```
+
+Python 3.10 or newer. The first `bev.load()` downloads the adapter from this repository and Gemma 4 from Google's (about
+52 GB), and caches both; after that it works offline.
+
+## Quickstart: one browser step
+
+Describe the page and the goal as the document; ask which operation comes next and which element it applies to.
+
+```python
+import bev
+
+model = bev.load(bev.WEB_MODEL)       # "sathiskumarjothi0/Beveridge-Pro-C-Web"; needs bev-decisions 0.2.5 (older versions: pass the repo id)
+page = {
+    "page": {"url": "https://example-travel.test/", "title": "Find a stay", "text": "Where are you going? ... Find stays"},
+    "elements": ["[1] input: Destination (empty)", "[2] button: Find stays", "[3] checkbox: Free cancellation"],
+    "recent_actions": [],
+}
+goal = "Find stays in Lisbon with free cancellation."
+answers = model.decide(page, {
+    "operation": {"type": "choice", "instructions": {"goal": goal, "rules": "Pick the next single action."},
+                  "criteria": {"CLICK": "Click an element", "TYPE_TEXT": "Enter text in a field",
+                               "WAIT": "Wait for the page to finish loading", "DONE": "Every requirement is visibly satisfied."}},
+    "type_text_target": {"type": "choice", "instructions": {"goal": goal, "operation": "TYPE_TEXT"},
+                         "criteria": {"1": "[1] Destination (empty)"}},
+})
+answers["operation"]["choice"], answers["operation"]["confidence"]    # ("TYPE_TEXT", 0.99...)
+```
+
+Your agent then does the action. Beveridge picks the step and the element; the words to type (here "Lisbon") come from
+the goal or from a small text model in your agent. `bev.gate(answers, threshold=0.8)` returns the unsure answers, so the
+agent can stop and ask a person instead of guessing.
+
+## Results
+
+Accuracy (%) = correct answers ÷ questions (or steps) on held-out tests. October 2026.
+
+### Web steps
+
+| Test | Beveridge-Pro-C | **Beveridge-Pro-C-Web** |
+|---|---|---|
+| Next step on 500 web pages from site types never seen in training (cars, courses, events, libraries) | 49.4 | **98.2** |
+| Next operation on the same 500 pages | 60.2 | **98.4** |
+| 24 recorded steps of real browser runs: same operation as the reference agent | 22/24 | **24/24** |
+
+By kind of step on the 500 held-out pages:
+
+| Kind of step | Accuracy |
+|---|---|
+| type into the search field | 100.0 |
+| pick the right autocomplete suggestion | 98.4 |
+| press Search | 100.0 |
+| apply the requested filters | 85.5 |
+| wait while the page loads | 100.0 |
+| open the requested result | 100.0 |
+| stop when the goal is met | 100.0 |
+
+### Live browsing
+
+The open-source browser loop [jev-ultrafast](https://github.com/browser-use/jev-ultrafast) (MIT) with Beveridge-Pro-C-Web
+choosing every step, on one NVIDIA H200; Llama 3.1 8B (local, temperature 0) writes the words typed into fields. Time =
+the agent's own clock from the first decision to DONE, including the browser work.
+
+| Task | Beveridge-Pro-C | **Beveridge-Pro-C-Web** |
+|---|---|---|
+| Hotel search: Lisbon, Design, Free cancellation, open Casa Flora (local test page) | 0/3 | **3/3**, median 1.86 s |
+| Open the Wikipedia article about Gödel's incompleteness theorems (live site) | 3/3, median 4.9 s | **3/3**, median 4.88 s |
+
+About 0.2 s per decision on the hotel page and 0.7 s on Wikipedia, whose pages are longer.
+
+### Documents (no loss)
+
+| Test | Beveridge-Pro-C | Beveridge-Pro-C-Web |
+|---|---|---|
+| Beveridge-Bench (operations documents, 5,700 questions) | 91.9 | 92.1 |
+
+For every other document test, see the [Beveridge-Pro-C card](https://huggingface.co/sathiskumarjothi0/Beveridge-Pro-C).
+
+## Honest limits
+
+- Two live tasks are a small test. Before you let it act on real accounts, run it on your own sites and keep a person in
+  the loop for anything that buys, sends, deletes or signs.
+- Filters are the weakest kind of step (85.5%): check that the agent applied every filter the goal asks for.
+- It reads the page as text and a list of elements; it does not see images or the page layout.
+- It decides the step; it does not write text. Typed words come from the goal or from a text model you choose.
+- Needs a large GPU. Beveridge-Large-C cannot navigate websites (16.8% on the held-out web steps).
+- English only; text beyond 4,096 tokens is cut.
+- Beveridge can make mistakes. 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 |
+|---|---|
+| `adapter/` | Beveridge's decision weights (LoRA adapter for Gemma 4 26B-A4B) |
+| `config.json` | Beveridge settings, calibration temperatures and the name of the base model |
+| `NOTICE` | Gemma attribution (Apache-2.0) |
+| `assets/` | the header and the browsing video |
+
+## 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)
+- Beveridge-Pro-C: [huggingface.co/sathiskumarjothi0/Beveridge-Pro-C](https://huggingface.co/sathiskumarjothi0/Beveridge-Pro-C)
+- Beveridge-Large-C: [huggingface.co/sathiskumarjothi0/Beveridge-Large-C](https://huggingface.co/sathiskumarjothi0/Beveridge-Large-C)
+
+## Support
+
+If Beveridge 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. Live tests use [jev-ultrafast](https://github.com/browser-use/jev-ultrafast) (MIT) by Browser Use
+and Llama 3.1 8B by Meta.
+
+## About the name
+
+Beveridge is named after Sir William Beveridge (1879–1963), the British economist, born in British India and educated in England, whose 1942 Beveridge Report laid the foundations of the modern welfare state and of the National Health Service (NHS), built for the good of future generations. The name is a reminder of what we want from the model: dependable, fair decisions that serve people, made carefully and checked against the facts.
+
+Other models on Hugging Face are called Bev; they are not related to this model. The Python package keeps its short
+name: `pip install bev-decisions`, `import bev`.

adapter/adapter_config.json

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+{
+  "alora_invocation_tokens": null,
+  "alpha_pattern": {},
+  "arrow_config": null,
+  "auto_mapping": {
+    "base_model_class": "Gemma4ForConditionalGeneration",
+    "parent_library": "transformers.models.gemma4.modeling_gemma4"
+  },
+  "base_model_name_or_path": "google/gemma-4-26B-A4B-it",
+  "bias": "none",
+  "corda_config": null,
+  "ensure_weight_tying": false,
+  "eva_config": null,
+  "exclude_modules": null,
+  "fan_in_fan_out": false,
+  "inference_mode": true,
+  "init_lora_weights": true,
+  "kasa_config": null,
+  "layer_replication": null,
+  "layers_pattern": null,
+  "layers_to_transform": null,
+  "loftq_config": {},
+  "lora_alpha": 32,
+  "lora_bias": false,
+  "lora_dropout": 0.0,
+  "lora_ga_config": null,
+  "megatron_config": null,
+  "megatron_core": "megatron.core",
+  "modules_to_save": null,
+  "monteclora_config": null,
+  "peft_type": "LORA",
+  "peft_version": "0.21.2",
+  "qalora_group_size": 16,
+  "r": 16,
+  "rank_pattern": {},
+  "revision": null,
+  "target_modules": ".*language_model.*\\.(q_proj|k_proj|v_proj|o_proj)",
+  "target_parameters": null,
+  "task_type": null,
+  "trainable_token_indices": null,
+  "use_bdlora": null,
+  "use_dora": false,
+  "use_qalora": false,
+  "use_rslora": false,
+  "velora_config": null
+}

adapter/adapter_model.safetensors

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+size 45991472

config.json

@@ -0,0 +1,9 @@
+{
+  "model_type": "bev-pro",
+  "format": "adapter",
+  "name": "Beveridge-Pro-C-Web",
+  "base_model": "google/gemma-4-26B-A4B-it",
+  "temperatures": {"noul": 1.2, "choice": 1.0, "score": 1.0},
+  "max_len": 4096,
+  "tokens_per_batch": 16384
+}