---
base_model: google/gemma-4-E4B-it
library_name: peft
license: apache-2.0
pipeline_tag: text-generation
language: [en]
tags:
- base_model:adapter:google/gemma-4-E4B-it
- lora
- peft
- gemma
- knowledge-base
- tool-use
- research
---

# distributor-wiki@v2 — a LoRA that navigates a wiki instead of memorising it

**Research preview from [lora-kernel](https://github.com/EvolvingAgentsLabs/lora-kernel).** This adapter isn't a general
assistant, and it holds no facts. It learned one habit: walking a library of short markdown pages to
answer a question. It searches, opens a page, opens the one section it needs, follows the link written
inside that sentence, sends arithmetic to a calculator, and cites the statement its answer rests on.
When the library holds nothing on the question, it says `Not in my library.`

The weights hold the navigation and the markdown holds the content. When a fact changes, you edit a
page and leave the model as it is.

## Try it

[![Open in Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/EvolvingAgentsLabs/lora-kernel/blob/main/examples/colab/wiki_walk.ipynb)

The notebook asks six two-hop questions to the bare model and then to this adapter, then changes one number in one markdown page and asks again. On an L4 on 2026-09-26 **[ran]**, the bare model scored 2/6 and the adapter 6/6. After a supplier's lead time was edited from 7 to 14 days, the adapter answered `14 days [rfu§lead-time]` with no retraining. It needs a bf16 GPU and no login.

## What it was measured on [ran]

The evaluation world is a generated distributor wiki (`knowledge/distributor-wiki`, seed 20260924). The
adapter never saw that world in training: its corpus was drawn from 32 *other* generated worlds. An
answer counts only if the value is right **and** the cited section actually says it.

| arm | W9 headline (2–3 hops, 40) | W9 all (67) | comparison band (40) |
|---|---|---|---|
| bare `gemma-4-E4B-it`, same runtime and prompt | 19/40 | — | — |
| `distributor-wiki@v1` (no comparisons in corpus) | 39/40 | 63/67 | 10/40 |
| **`distributor-wiki@v2` (this adapter)** | **39/40** | **63/67** | **37/40** (28 : 1 paired vs v1, p = 1.1e-7) |

Runs: `results/M7-W9-atomic-statements-20260924`, B1, and `results/B5-comparison-corpus-20260926` in the
repository. The release manifest is `releases/distributor-wiki@v2.json`, and the adapter sha256 is
`d4d91ea4aa7a8ffcd82beabb87d07112c02b212388412fb61f43df49d3eb93d6`.

## Limits, stated plainly

- **Every world it has seen or been scored on is generated by one generator.** It hasn't been measured
  on a hand-written or real organisation's library. A model trained on generated data can learn the
  generator's quirks, and this one hasn't been tested outside them.
- **It expects this page format**: YAML front matter (`id`, `when`, `what`) followed by one `§anchor`
  statement per line, with links as `[[page-id]]` inside the statements. See `knowledge/distributor-wiki/`.
- **It needs the runtime.** The model writes `<search>`, `<open>` and `<calc>` tags. The runtime stops at
  each closing tag, writes the result inline after `= `, and lets generation continue. This is not
  OpenAI `tool_calls`: one adapter in this project scored 11/90 served that way and 90/90 served inline
  as it was trained.
- Thinking is **off** (`enable_thinking=false`) in every render. The vision and audio towers are excluded
  from the LoRA.

## How to run it

The runtime lives in `memory/` (`Library`, `Conversation`). The runner that produced the numbers above
is `training/wiki/wiki_arm.py`, served through vLLM with LoRA enabled. To reproduce a score, see
`docs/SERVING.md` and `docs/MEMORY.md` §1.6 in the repository.

Recipe: r 16, alpha 32, 3 epochs, lr 2e-4, effective batch 16, max_seq 1536, all attention and MLP
projections, seed 1. The corpus is `training/wiki/data/train_cmp.jsonl` (728 walks, sha256 `7b307d53…`).

Apache-2.0, like the base model.