Update chatcode/README.md

chatcode/README.md

@@ -1,7 +1,7 @@
-# PocketVision Chat & Code — version 2 preparation
+# PocketVision Chat, Code & Writing — source preparation
 
 This version accepts text and produces text. It focuses on ordinary conversation,
-explaining code, generating functions, and debugging. There is no image input,
+explaining code, generating functions, debugging, and source-based article writing. There is no image input,
 vision encoder, image processor, or ChartQA training in the new pipeline.
 
 **Status:** source and training recipe published privately at
@@ -19,19 +19,27 @@ It does not silently run the old model under the new name.
   This is an instruction-tuned text model; its model card includes chat and coding evaluations.
 - Data: [HuggingFaceTB/smoltalk](https://huggingface.co/datasets/HuggingFaceTB/smoltalk),
   pinned to `5feaf2fd3ffca7c237fc38d1861bc30365d48ffa`.
-- Pilot: 12,000 `self-oss-instruct` programming examples and 8,000
-  `smol-magpie-ultra` conversation examples. The coding source primarily covers
+- Planned Hub mix: 10,000 `self-oss-instruct` programming examples, 6,000
+  `smol-magpie-ultra` conversation examples, 2,000 `smol-rewrite` writing/editing
+  examples, and 2,000 `smol-summarize` source summarization examples. The coding source primarily covers
   Python. JavaScript and other languages need additional sources and tests.
 - Attribution for the coding subset: BigCode,
   [self-oss-instruct-sc2-exec-filter-50k](https://huggingface.co/datasets/bigcode/self-oss-instruct-sc2-exec-filter-50k),
   Open Data Commons Attribution License (ODC-BY). The source describes its responses
   as execution-validated; this project does not rerun or independently certify them.
-  Smol-Magpie-Ultra is Apache-2.0. Dependency and underlying source terms still apply.
-- Separate validation and test sets: 128 examples from each source per split.
+  Smol-Magpie-Ultra, Smol-rewrite, and Smol-summarize are Apache-2.0.
+  Dependency and underlying source terms still apply.
+- Separate Hub validation and test sets: 128 examples from each of four sources per split (512 each).
   All three derived splits come from the sources' official **training** split.
   Official test data is unused. Identical normalized opening questions are grouped
   and deduplicated globally. This does not establish novel or semantically disjoint
   data relative to the pretrained base.
+- An additional 20 project-authored examples teach the article JSON and source-review
+  tasks: 12 training, 4 validation, 4 test. They use **fictional practice topics and
+  reserved example.org URLs**, not real references. Both tasks for a source packet
+  stay in the same split, enforced again when the trainer loads data. These small
+  examples teach format; they are not a large factual corpus or evidence of quality.
+  The final default counts are 20,012 train and 516 each validation/test.
 - Examples longer than 2,048 tokens are skipped, preserving complete code/answers.
   Only assistant answer and end-of-turn tokens contribute to loss.
 - One epoch of 4-bit NF4 QLoRA, rank 16, all linear projections, learning rate
@@ -44,6 +52,46 @@ Use the existing environment and run `python app.py`. With no CUDA GPU, it displ
 the text-only interface with sending disabled. No new model weights are fetched.
 The original visual UI is preserved as `vision_app.py` for reproducibility.
 
+## Article writing and Wikipedia preparation
+
+The **Articles & research** tab has two tasks. Reference article produces a short,
+neutral article with a lead, sections, references and research gaps. Wikipedia
+outline and source review produces section questions and source-review tasks for
+a human editor, without article prose. Paste titles, URLs and relevant excerpts;
+the app does not fetch websites or search the web.
+
+The model returns structured text with source IDs. Application code rejects unknown
+or absent IDs and renders URLs only from the supplied metadata. Incomplete JSON,
+uncited paragraphs and model-added raw citation markup are rejected. This is **not
+a factual-entailment or source-reliability checker**. A valid ID can still point to
+an excerpt that does not support the claim, so every draft requires human review.
+No articles are uploaded to Wikipedia by this app.
+
+English Wikipedia's [current guideline](https://en.wikipedia.org/wiki/Wikipedia:Writing_articles_with_large_language_models)
+prohibits LLM-generated/re-written article prose, with limited copyediting and
+translation exceptions. The reference-article task is for other writing contexts;
+use the planning task for Wikipedia assistance.
+
+The [Wikimedia Wikipedia dataset](https://huggingface.co/datasets/wikimedia/wikipedia)
+contains cleaned article text with references removed. It is not in the default
+mix: its raw text is not a ready-made source-grounded instruction dataset. Any later
+use needs article-level provenance and the corpus's CC-BY-SA/GFDL terms recorded.
+Wikipedia articles should not themselves be cited as evidence for new Wikipedia
+article prose; consult their original reliable sources.
+
+## Colab notebook
+
+[cloud_training.ipynb](cloud_training.ipynb) is a cloud training walkthrough.
+Upload it to Colab, select a free GPU if available, add your own existing read token
+in Colab Secrets, and follow its setup. It downloads an immutable snapshot of source
+files and keeps checkpoints in your chosen persistent Drive folder.
+
+The saved notebook was structurally checked, its Python cells compiled, and all
+cells executed **only with `DRY_RUN=True`**. `nbformat` and a Colab GPU runtime are
+not available in this local environment; a full cloud run remains unverified.
+Set `DRY_RUN=False` for cloud setup and separately enable `RUN_TRAINING` to train.
+No subscription, GPU job or model publication is started by this preparation.
+
 ## Train on a remote Linux GPU
 
 Use a Colab GPU runtime or a Hugging Face Job. A 24 GB GPU is a reasonable starting
@@ -94,6 +142,12 @@ The source bundle does **not** include a sandboxed code-execution evaluator;
 never automatically execute arbitrary training data or generated programs on a
 machine with credentials. Use an isolated evaluation environment.
 
+`python -m chatcode.evaluate_articles --adapter /path/to/adapter --output /persistent/article-evaluation.json`
+compares base and adapter formatting/citation-ID acceptance on the four held-out
+fictional behavior cases. It is cloud-GPU only and retains generated outputs for
+human review. Its acceptance rate is not factual accuracy, article quality, or
+Wikipedia suitability. Also evaluate real source packets and chat/code regressions.
+
 Publish a tested candidate to a new private repository or a distinct version branch;
 the old vision adapter must not be relabeled as a chat/code model. Preserve the
 base revision and dataset attribution. No quality improvement is claimed yet.
@@ -107,7 +161,7 @@ environment; this ordinary GPU app is not yet a verified ZeroGPU deployment.
 
 ## Package
 
-`python -m chatcode.package` creates `dist/pocketvision-chat-code-source.zip`.
+`python -m chatcode.package` creates `dist/pocketvision-chat-code-writing-source.zip`.
 It includes only the active app, text pipeline, configuration, documentation,
 and licenses. It excludes old weights, datasets, caches, environments, and credentials.
 Project source remains GPL-3.0-only; original Qwen weights remain Apache-2.0.