SadikAI Medium — release
AI made by Sadik Nawaz, 11 years old, 6th class.
A 7.9M-parameter transformer trained from scratch on English, Hindi, Hinglish, code, maths and world knowledge, plus the complete local chat app it runs in. "Medium" is the project name: the weights are intentionally small so the whole thing runs offline on a home PC with no GPU.
| field | value |
|---|---|
| creator | Sadik Nawaz (11, 6th class) |
| parameters | 7,866,048 (1,574,592 non-embedding) |
| training | 63.9 M tokens · step 4249 |
| validation loss / perplexity | 2.9338 / 18.8 |
model/ inference-only checkpoint + architecture JSON
tokenizer/ byte-level BPE tokenizer (32,768 vocab)
generate.py standalone text sampler (torch only)
app/ full chat app: memory, web, maths, code, media, lessons
MODEL_CARD.md what this is / what it can't do
DATA_SOURCES.md training sources + licenses
PUBLISH.md how to publish this folder (HF / GitHub)
SHA256SUMS checksum for every file in this release
requirements.txt
LICENSE.txt
Quick start
pip install -r requirements.txt
python generate.py --say "hello" --max-new-tokens 80
Run the chat app
From the extracted folder root:
python app/chat_server.py --checkpoint model/weights.pt --tokenizer tokenizer/tokenizer.json --port 8000 --open
The app answers maths and common coding asks from its curated layer, keeps memory across sessions, looks facts up on the web, and can generate images, speech audio, and PDF/Word/Excel/CSV files. It also gives offline lessons on teamwork, world religions, respect, kindness, study skills, health, first aid, online safety, science, space, geography, India, coding, computer basics, money skills and more, and writes short stories or poems from a curated library.
Try asking
who made you?— the creator creditteach me about teamworktell me about world religions/learn Hinduismhow do I study better?what should I do for a small cut?write a story about a dogwrite a poem about the moon/topics— every offline lessonwhat is 12 x 13?code for fibonacci in pythondraw a red fox in the snow
About the weights
The language model itself is a base next-token model: it continues text.
Stories and poems come from a small curated library, with the model as a
fallback. Facts, maths, coding answers and school lessons come from the app
layer, which is why the release is useful offline even though the network is
small. See MODEL_CARD.md for honest limits.
Publish to the Hugging Face Hub (optional)
pip install huggingface_hub
hf auth login # older: huggingface-cli login
hf upload SadikNawazAI/sadik-ai-medium-multilingual . --repo-type model
# older: huggingface-cli upload SadikNawazAI/sadik-ai-medium-multilingual . --repo-type model
Everything here was produced by export_release.py; rebuild after retraining:
python export_release.py --checkpoint runs/medium/best.pt