DeepSeek-R1-GGUF
unsloth · Kit
GGUF copies of the 671B DeepSeek-R1 reasoning model, with 1.58-bit and 2-bit dynamic quants, for running it in llama.cpp on a local machine with enough memory.
Ranks AI systems that turn a text or image description of a mechanical part into a valid 3D CAD model, and scores uploaded STEP or BREP files. For CAD generation researchers and engineers.
CADGenBench Leaderboard 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 HuggingAI4Engineering/CADGenBench --repo-type space --local-dir CADGenBench cd CADGenBench
CADGenBench Leaderboard is the results page for a benchmark of AI-driven CAD generation. It measures whether a system can read a written or pictured description of a machine part and return a 3D model that is valid and geometrically right.
The Space has three tabs. Leaderboard shows ranked submissions, Submit scores your own files, and About explains the scoring axes.
Researchers and engineers who build or compare systems that generate CAD models.
| Industry | Who uses it | How they use it | Example |
|---|---|---|---|
| Mechanical engineering | CAD engineers | See how generation systems rank | Open the Leaderboard tab and read the rows |
| Research | AI researchers | Score their own model on the benchmark | Upload STEP files on the Submit tab |
| Software | Tool builders | Compare approaches to CAD generation | Read a submission's report |
You can skip it if you do not work with CAD generation, since the Space only covers this benchmark.
| Use case | Who | What they get back |
|---|---|---|
| Browse rankings | Researchers | A ranked list of submissions |
| Submit candidates | Model builders | A scored row with a per-submission report |
| Read the scoring | Anyone | An explanation of the scoring axes |
pip install -U huggingface_hub hf download HuggingAI4Engineering/CADGenBench --repo-type space --local-dir CADGenBench cd CADGenBench docker build -t cadgenbench . docker run -it -p 7860:7860 cadgenbench
Open the Space's page in your browser. Read the Leaderboard tab for rankings. To submit, go to the Submit tab, upload your STEP or BREP candidate files and wait for the Space to run the evaluation.
| Tool | What it does |
|---|---|
| Leaderboard tab | Shows ranked submissions |
| Submit tab | Takes STEP or BREP files and runs the evaluation |
| About tab | Explains the scoring axes and where the benchmark code lives |
You open the Submit tab and upload STEP files your system produced. The Space runs the evaluation and adds a row for your submission, with a report you can open.
If you are new to CAD generation, start on the Leaderboard tab and read the top rows. Then open the About tab to see how scoring works.
If you know the field, the Space is a Docker app that serves several pages, including leaderboard, submit, gallery, metrics and admin views. It names two datasets, one for submissions and one for benchmark data. The repository also holds a tests folder and a tools folder.
Ranks AI systems that turn a text or image description of a mechanical part into a valid 3D CAD model, and scores uploaded STEP or BREP files. For CAD generation researchers and engineers.
Made by HuggingAI4Engineering 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: CADGenBench Leaderboard is the results page for a benchmark of AI-driven CAD generation. It measures whether a system can read a written or pictured description of a machine part and return a 3D model that is valid and geometrically right.
The Space has three tabs. Leaderboard shows ranked submissions, Submit scores your own files, and About explains the scoring axes.
Who it suits: Researchers and engineers who build or compare systems that generate CAD models.
Industry | Who uses it | How they use it | Example
Mechanical engineering | CAD engineers | See how generation systems rank | Open the Leaderboard tab and read the rows
Research | AI researchers | Score their own model on the benchmark | Upload STEP files on the Submit tab
Software | Tool builders | Compare approaches to CAD generation | Read a submission's report
You can skip it if you do not work with CAD generation, since the Space only covers this benchmark.
Where to use it:
Use case | Who | What they get back
Browse rankings | Researchers | A ranked list of submissions
Submit candidates | Model builders | A scored row with a per-submission report
Read the scoring | Anyone | An explanation of the scoring axes
How to set it up:
pip install -U huggingface_hub
hf download HuggingAI4Engineering/CADGenBench --repo-type space --local-dir CADGenBench
cd CADGenBench
docker build -t cadgenbench .
docker run -it -p 7860:7860 cadgenbench
How to use it: Open the Space's page in your browser. Read the Leaderboard tab for rankings. To submit, go to the Submit tab, upload your STEP or BREP candidate files and wait for the Space to run the evaluation.
Tool | What it does
Leaderboard tab | Shows ranked submissions
Submit tab | Takes STEP or BREP files and runs the evaluation
About tab | Explains the scoring axes and where the benchmark code lives
Example: You open the Submit tab and upload STEP files your system produced. The Space runs the evaluation and adds a row for your submission, with a report you can open.
For newcomers and experts: If you are new to CAD generation, start on the Leaderboard tab and read the top rows. Then open the About tab to see how scoring works.
If you know the field, the Space is a Docker app that serves several pages, including leaderboard, submit, gallery, metrics and admin views. It names two datasets, one for submissions and one for benchmark data. The repository also holds a tests folder and a tools folder.
You upload STEP or BREP files
The Space runs the evaluation
A row is published with a report
The row joins the leaderboard
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 |
|---|---|---|---|---|
| CADGenBench Leaderboard This page | 90 | docker | apache-2.0 | |
| Open LLM LeaderboardShares docker · leaderboard | 14,156 | docker | apache-2.0 | |
| MTEB LeaderboardShares docker · leaderboard | 7,773 | docker | mit | |
| UGI LeaderboardShares docker · leaderboard | 2,199 | docker | apache-2.0 | |
| Microduck SandboxShares docker | 804 | docker | apache-2.0 | |
| Visualize Dataset (v2.0+ latest dataset format)Shares docker | 754 | docker | apache-2.0 |
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.
unsloth · Kit
GGUF copies of the 671B DeepSeek-R1 reasoning model, with 1.58-bit and 2-bit dynamic quants, for running it in llama.cpp on a local machine with enough memory.
prithivMLmods · Kit
Provides GGUF files of Axion-1.0-35B, a 35B mixture-of-experts model with about 3B active parameters aimed at scientific reasoning, information seeking and too…
Phips · Kit
Upscales low-resolution images with a super-resolution network that trains on one consumer GPU and exports to ONNX. For people who train or run image upscalers.
allenai · Kit
The low-level action model of allenai's ToyBlocks block-stacking project, trained on simulated Franka arm data to pick, place and rotate three blocks on a tabl…
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…