UPLOAD.md

# Upload Guide

This release is ready up to the pre-upload stage. The local package is:

```text
/home/ps/sjj/project/Memory/dataset/general/general_science_release.tar.gz
```

The folder to upload is:

```text
/home/ps/sjj/project/Memory/dataset/general/general_science_release
```

## 1. Pre-upload Check

Run:

```bash
cd /home/ps/sjj/project/Memory/dataset/general/general_science_release
conda run -n memory python scripts/validate_general_science_release.py
```

Expected result:

```text
validation=ok
```

## 2. Hugging Face Upload

### Option A: Web Upload

1. Create a new dataset repository on Hugging Face.
2. Suggested repo name:

```text
gineven/GeneralScience-MLLM-22K
```

3. Upload the contents of `general_science_release/`.
4. Keep the folder structure unchanged:

```text
README.md
general_science_card.md
stats.json
train.jsonl
test.jsonl
images/scienceqa/*.png
scripts/*.py
```

### Option B: CLI Upload

Install or update the HF hub client if needed:

```bash
pip install -U huggingface_hub
```

Login:

```bash
hf auth login
```

Create the dataset repo:

```bash
# You already created this repository:
# https://huggingface.co/datasets/gineven/GeneralScience-MLLM-22K
```

Upload the folder:

```bash
cd /home/ps/sjj/project/Memory/dataset/general
hf upload gineven/GeneralScience-MLLM-22K general_science_release . \
  --repo-type dataset \
  --exclude "*/__pycache__/*" \
  --exclude "*.pyc"
```

If your installed CLI uses the older command name, use:

```bash
huggingface-cli login
# Repository already exists:
# https://huggingface.co/datasets/gineven/GeneralScience-MLLM-22K
```

Then upload with Python API as shown below.

### Option C: Python API Upload

```python
from huggingface_hub import HfApi

api = HfApi()
repo_id = "gineven/GeneralScience-MLLM-22K"

api.create_repo(repo_id=repo_id, repo_type="dataset", exist_ok=True)
api.upload_folder(
    repo_id=repo_id,
    repo_type="dataset",
    folder_path="/home/ps/sjj/project/Memory/dataset/general/general_science_release",
    path_in_repo=".",
    ignore_patterns=["*/__pycache__/*", "*.pyc"],
)
```

After upload, the expected download link is:

```text
https://huggingface.co/datasets/gineven/GeneralScience-MLLM-22K
```

## 3. ModelScope Upload

ModelScope upload is similar in spirit: create a dataset repository, then upload the same folder contents. Keep `README.md`, `train.jsonl`, `test.jsonl`, `stats.json`, and `images/scienceqa/` at the repository root.

If using `modelscope` CLI/API, first make sure your account is logged in and then upload the `general_science_release/` directory as a dataset repository.

## 4. Netdisk Fallback

If HF/ModelScope credentials are not available, upload:

```text
general_science_release.tar.gz
```

Then share the netdisk URL together with:

```text
general_science_release/general_science_card.md
general_science_release/stats.json
```

## 5. Important License Check

Before public upload, manually verify the upstream ScienceQA license because the local snapshot used for this build does not include license metadata. The current `README.md` marks the combined license as `other` and records the missing ScienceQA license note.