Record replacement publication and original model deletion

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# PocketVision Chat & Code — version 2 preparation

This version accepts text and produces text. It focuses on ordinary conversation,
explaining code, generating functions, and debugging. There is no image input,
vision encoder, image processor, or ChartQA training in the new pipeline.

**Status:** source and training recipe published privately at
[gamedevelop/pocketvision-chat-code](https://huggingface.co/gamedevelop/pocketvision-chat-code);
no new model trained or deployed. The original `gamedevelop/pocketvision` repository
was deleted at the owner's request. Its local SmolVLM/ChartQA weights and ZIP backup
remain available and are incompatible with this version.
The local app shows an interface preview without downloading the new base weights.
It does not silently run the old model under the new name.

## Model and training data

- Base: [Qwen/Qwen3-4B-Instruct-2507](https://huggingface.co/Qwen/Qwen3-4B-Instruct-2507),
  Apache-2.0, pinned to `cdbee75f17c01a7cc42f958dc650907174af0554`.
  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
  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.
  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.
- 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
  5e-5, microbatch 1, accumulation 16. This is a starting recipe, not validated
  evidence of improved model quality or a guarantee that it fits every GPU.

## Local interface preview

Use the existing environment and run `python app.py`. With no CUDA GPU, it displays
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.

## Train on a remote Linux GPU

Use a Colab GPU runtime or a Hugging Face Job. A 24 GB GPU is a reasonable starting
target, subject to an actual memory smoke test. None has been rented or started.
Run these commands **on the cloud machine**, from the unpacked source directory:

```bash
python -m pip install torch==2.9.1 --index-url https://download.pytorch.org/whl/cu128
python -m pip install -r chatcode/requirements-cloud.txt
python -m pip check
python -m chatcode.data --output /checkpoints/chatcode-data
python -m chatcode.train --data /checkpoints/chatcode-data --output /checkpoints/smoke --max-steps 25
```

`/checkpoints` must be your mounted **persistent** bucket/volume or Drive directory.
Do not use a transient runtime directory as the only copy: Jobs discard local disk
when they end, and notebook sessions can be terminated. Checkpoints include optimizer,
scheduler, trainer and random state; they are saved every 25 steps.

First inspect the smoke run's GPU memory and steps/second. Estimate cost and agree
on a run limit before launching the full run. A 25-step smoke run is still paid
compute when using a paid GPU. Model/data downloads and setup also take time.
Once that succeeds, run the full pilot in a new output directory:

```bash
python -m chatcode.train --data /checkpoints/chatcode-data --output /checkpoints/pilot
```

Resume an interrupted run with the same configuration and data:

```bash
python -m chatcode.train --data /checkpoints/chatcode-data --output /checkpoints/pilot --resume-from-checkpoint /checkpoints/pilot/checkpoint-N
```

The script explicitly refuses CPU training. It writes the selected adapter,
tokenizer, provenance, training report, and candidate model card under `adapter/`.
It does not upload automatically, launch cloud hardware, or change the existing Hub
repository. There is no new dataset or GPU training run in this delivery.

## Evaluate and release

Validation loss selects a checkpoint; it does not measure whether generated programs
work. Before releasing, compare the base and candidate on the untouched derived test
split and a separate set of conversation, instruction-following, debugging, and
coding tasks. Record every prompt, output, model revision, and generation setting.
Use tests for coding correctness and inspect chat quality for regressions.
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.

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.

For a regular cloud GPU demo, install the cloud dependencies, set
`POCKETCHAT_ADAPTER` to the compatible local adapter directory or Hub repository,
and run `python app.py`. Without an adapter it explicitly identifies itself as the
pretrained Qwen base. On Spaces set `SPACE_ID` normally via the platform.
ZeroGPU needs a separate startup/decorator integration and compatible PyTorch
environment; this ordinary GPU app is not yet a verified ZeroGPU deployment.

## Package

`python -m chatcode.package` creates `dist/pocketvision-chat-code-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.

Sources: [QLoRA with PEFT](https://huggingface.co/docs/peft/developer_guides/quantization),
[Hugging Face training Jobs](https://huggingface.co/docs/hub/jobs-training).

	# PocketVision Chat & Code — version 2 preparation

	This version accepts text and produces text. It focuses on ordinary conversation,
	explaining code, generating functions, and debugging. There is no image input,
	vision encoder, image processor, or ChartQA training in the new pipeline.

	Status: source and training recipe published privately at
	[gamedevelop/pocketvision-chat-code](https://huggingface.co/gamedevelop/pocketvision-chat-code);
	no new model trained or deployed. The original `gamedevelop/pocketvision` repository
	was deleted at the owner's request. Its local SmolVLM/ChartQA weights and ZIP backup
	remain available and are incompatible with this version.
	The local app shows an interface preview without downloading the new base weights.
	It does not silently run the old model under the new name.

	## Model and training data

	- Base: [Qwen/Qwen3-4B-Instruct-2507](https://huggingface.co/Qwen/Qwen3-4B-Instruct-2507),
	Apache-2.0, pinned to `cdbee75f17c01a7cc42f958dc650907174af0554`.
	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
	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.
	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.
	- 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
	5e-5, microbatch 1, accumulation 16. This is a starting recipe, not validated
	evidence of improved model quality or a guarantee that it fits every GPU.

	## Local interface preview

	Use the existing environment and run `python app.py`. With no CUDA GPU, it displays
	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.

	## Train on a remote Linux GPU

	Use a Colab GPU runtime or a Hugging Face Job. A 24 GB GPU is a reasonable starting
	target, subject to an actual memory smoke test. None has been rented or started.
	Run these commands on the cloud machine, from the unpacked source directory:

	```bash
	python -m pip install torch==2.9.1 --index-url https://download.pytorch.org/whl/cu128
	python -m pip install -r chatcode/requirements-cloud.txt
	python -m pip check
	python -m chatcode.data --output /checkpoints/chatcode-data
	python -m chatcode.train --data /checkpoints/chatcode-data --output /checkpoints/smoke --max-steps 25
	```

	`/checkpoints` must be your mounted persistent bucket/volume or Drive directory.
	Do not use a transient runtime directory as the only copy: Jobs discard local disk
	when they end, and notebook sessions can be terminated. Checkpoints include optimizer,
	scheduler, trainer and random state; they are saved every 25 steps.

	First inspect the smoke run's GPU memory and steps/second. Estimate cost and agree
	on a run limit before launching the full run. A 25-step smoke run is still paid
	compute when using a paid GPU. Model/data downloads and setup also take time.
	Once that succeeds, run the full pilot in a new output directory:

	```bash
	python -m chatcode.train --data /checkpoints/chatcode-data --output /checkpoints/pilot
	```

	Resume an interrupted run with the same configuration and data:

	```bash
	python -m chatcode.train --data /checkpoints/chatcode-data --output /checkpoints/pilot --resume-from-checkpoint /checkpoints/pilot/checkpoint-N
	```

	The script explicitly refuses CPU training. It writes the selected adapter,
	tokenizer, provenance, training report, and candidate model card under `adapter/`.
	It does not upload automatically, launch cloud hardware, or change the existing Hub
	repository. There is no new dataset or GPU training run in this delivery.

	## Evaluate and release

	Validation loss selects a checkpoint; it does not measure whether generated programs
	work. Before releasing, compare the base and candidate on the untouched derived test
	split and a separate set of conversation, instruction-following, debugging, and
	coding tasks. Record every prompt, output, model revision, and generation setting.
	Use tests for coding correctness and inspect chat quality for regressions.
	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.

	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.

	For a regular cloud GPU demo, install the cloud dependencies, set
	`POCKETCHAT_ADAPTER` to the compatible local adapter directory or Hub repository,
	and run `python app.py`. Without an adapter it explicitly identifies itself as the
	pretrained Qwen base. On Spaces set `SPACE_ID` normally via the platform.
	ZeroGPU needs a separate startup/decorator integration and compatible PyTorch
	environment; this ordinary GPU app is not yet a verified ZeroGPU deployment.

	## Package

	`python -m chatcode.package` creates `dist/pocketvision-chat-code-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.

	Sources: [QLoRA with PEFT](https://huggingface.co/docs/peft/developer_guides/quantization),
	[Hugging Face training Jobs](https://huggingface.co/docs/hub/jobs-training).