anaksor-ocr
Sokheng · Kit
Reads Khmer and English text from documents, receipts and photos of signs, using a detector for text lines and a recogniser for each line. Suits developers who…
A Gradio demo that compares GPT-2 models trained to multiply large numbers, including variants named for no reasoning steps and for reasoning steps hidden or written out. For AI researchers and curious learners.
Gpt2 Multiplication Predictor is a kit in the Research category, listed by Mindket. It is free. This is version 1, last updated on . Download it on this page, a zip of its files. Nothing in it runs by itself.
pip install -U huggingface_hub hf download yuntian-deng/gpt2-multiplication --repo-type space --local-dir gpt2-multiplication cd gpt2-multiplication
This Space is a demo of GPT-2 models that predict the result of multiplying two numbers. It names three models. One uses no chain of thought, one uses an implicit chain of thought for 20 digits, and one uses an explicit chain of thought for 20 digits. The README is only a configuration pointer, so the descriptions here come from the model names.
AI researchers, students and curious people who want to see how small language models handle arithmetic with and without written-out steps.
| Industry | Who uses it | How they use it | Example |
|---|---|---|---|
| AI research | Researchers | Compare reasoning styles on long multiplication | Run the same product through each model |
| Education | Students | See where a small model fails at arithmetic | Try a large multiplication and compare answers |
| AI development | Developers | Study a small Gradio demo with three models | Read app.py and the tests folder |
People who need a dependable calculator can skip it, since the point is to study the models.
| Use case | Who | What they get back |
|---|---|---|
| Compare models | Researchers | Predicted products from the three GPT-2 variants |
| Test limits | Students | A look at where each variant gets a product wrong |
| Read the code | Developers | A short Gradio app with a tests folder |
pip install -U huggingface_hub hf download yuntian-deng/gpt2-multiplication --repo-type space --local-dir gpt2-multiplication cd gpt2-multiplication pip install gradio==6.30.0 pip install -r requirements.txt python app.py
Open the Space page in a browser, or start your own copy with the command below. The README gives no steps, so the controls on the Gradio page are your guide.
python app.py
You enter two numbers to multiply on the page. Going by the model names, the app shows what each GPT-2 variant predicts for their product, so you can compare them with the true answer.
If you are new, open the Space and try a small multiplication first, then a large one. Checking each answer yourself shows how the variants differ.
For experts, the three models are yuntian-deng/gpt2-no-cot-multiplication, gpt2-implicit-cot-multiplication-20-digits and gpt2-explicit-cot-multiplication-20-digits. The Space runs on zero-a10g hardware with Gradio 6.30.0.
A Gradio demo that compares GPT-2 models trained to multiply large numbers, including variants named for no reasoning steps and for reasoning steps hidden or written out. For AI researchers and curious learners.
Made by yuntian-deng on Hugging Face. These notes are Mindket's, written from its Space page and README; the app's own files and license are on Hugging Face.
What it is: This Space is a demo of GPT-2 models that predict the result of multiplying two numbers. It names three models. One uses no chain of thought, one uses an implicit chain of thought for 20 digits, and one uses an explicit chain of thought for 20 digits. The README is only a configuration pointer, so the descriptions here come from the model names.
Who it suits: AI researchers, students and curious people who want to see how small language models handle arithmetic with and without written-out steps.
Industry | Who uses it | How they use it | Example
AI research | Researchers | Compare reasoning styles on long multiplication | Run the same product through each model
Education | Students | See where a small model fails at arithmetic | Try a large multiplication and compare answers
AI development | Developers | Study a small Gradio demo with three models | Read app.py and the tests folder
People who need a dependable calculator can skip it, since the point is to study the models.
Where to use it:
Use case | Who | What they get back
Compare models | Researchers | Predicted products from the three GPT-2 variants
Test limits | Students | A look at where each variant gets a product wrong
Read the code | Developers | A short Gradio app with a tests folder
How to set it up:
pip install -U huggingface_hub
hf download yuntian-deng/gpt2-multiplication --repo-type space --local-dir gpt2-multiplication
cd gpt2-multiplication
pip install gradio==6.30.0
pip install -r requirements.txt
python app.py
How to use it: Open the Space page in a browser, or start your own copy with the command below. The README gives no steps, so the controls on the Gradio page are your guide.
python app.py
Example: You enter two numbers to multiply on the page. Going by the model names, the app shows what each GPT-2 variant predicts for their product, so you can compare them with the true answer.
For newcomers and experts: If you are new, open the Space and try a small multiplication first, then a large one. Checking each answer yourself shows how the variants differ.
For experts, the three models are yuntian-deng/gpt2-no-cot-multiplication, gpt2-implicit-cot-multiplication-20-digits and gpt2-explicit-cot-multiplication-20-digits. The Space runs on zero-a10g hardware with Gradio 6.30.0.
You enter two numbers
The app passes them to the GPT-2 models
Each model predicts the product
You compare the answers
Good to know:
Open the Space in your browser, or copy it to your own Hugging Face account from its page. Nothing in this kit runs by itself.
README.md 4 KB
Listed Hugging Face Spaces in the same category that share the most tags with this one.
| Product | Likes | Built with | License | Last change |
|---|---|---|---|---|
| Gpt2 Multiplication Predictor This page | 32 | gradio | mit | |
| Open VLM LeaderboardShares gradio | 1,034 | gradio | apache-2.0 | |
| Face SwapShares gradio | 808 | gradio | cc-by-nc-sa-4.0 | |
| TTS Spaces ArenaShares gradio | 493 | gradio | zlib | |
| Depth Anything 3Shares gradio | 490 | gradio | cc-by-nc-4.0 | |
| BLIPShares gradio | 431 | gradio | bsd-3-clause |
Posts and comments that name this project on Hacker News, Bluesky, X, Reddit, Facebook and YouTube, found by a weekly search and on Trendshift. Mindket doesn’t write or check them. Open one to read it where it was posted.
Sokheng · Kit
Reads Khmer and English text from documents, receipts and photos of signs, using a detector for text lines and a recogniser for each line. Suits developers who…
Qwen · Kit
Qwen2.5-7B-Instruct as GGUF files in eight quantization sizes, to chat with locally through llama.cpp. For people who want a capable 7.6B model on their own co…
ACE-Step · Kit
Generates music from text with a 4B diffusion decoder, 50 steps and guidance control, aiming for higher audio quality than the base. For creators with a GPU of…
AbdomenAtlas · Kit
Detects and outlines tumors in 9 organs on CT scans, trained with the R-Super method that learns from radiology reports. A research model for medical imaging g…
dBenf · Kit
Answers typed questions about satellite image patches from all 12 Sentinel-2 bands, returning a probability for each option without writing text. A Qwen3.5-0.8…
zimmerk4 · Kit
Plays the full game Getting Over It from the bottom to the end with a small trained policy network that learned inside a JAX physics simulator. For reinforceme…