Bonsai-27B-GGUF
lmstudio-community · Kit
A GGUF conversion of the 27B Bonsai model for LM Studio, with a 3.80 GB Q1_0 language file and a 0.93 GB BF16 vision projector. Tested for text, vision and par…
A static dashboard of the CausalArena benchmark for learning causal structure, with a leaderboard, real-data checks, case views, a gallery of released graphs and a guide to submitting results.
CausalArena 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 LAMDA-Tabular/CausalArena-leaderboard --repo-type space --local-dir CausalArena-leaderboard cd CausalArena-leaderboard
CausalArena Leaderboard is a static dashboard for the public release of CausalArena, a benchmark for learning causal structure from data. It collects the main leaderboard, real-data checks, scaling and runtime views, taxonomy summaries, case inspection, and a gallery of released graphs. You open it in a browser and install nothing.
Researchers who work on learning causal structure and want to compare methods, and contributors who want to add their own results or new causal models.
| Industry | Who uses it | How they use it | Example |
|---|---|---|---|
| Research | Causal structure researchers | Compare methods on the main leaderboard | Open the leaderboard page and read the rankings |
| Data science | Practitioners | Check how methods hold up on real data | Read the real-data checks |
| Education | Students | Browse released causal graphs | Open the SCM Gallery page |
| Benchmark contributors | Method authors | Submit results or propose a new causal model | Follow the Submit page steps |
Anyone who is not working on causal structure learning can skip it.
It runs in these places.
| Use case | Who | What they get back |
|---|---|---|
| Compare methods | Researchers | The main leaderboard and the scaling and runtime views |
| Inspect cases | Researchers | Semantic and Formula case views |
| Browse released graphs | Students and researchers | The SCM Gallery page of grounded graphs |
Using the Space online needs only a browser. Getting its files onto your own computer needs the following.
pip install -U huggingface_hub hf download LAMDA-Tabular/CausalArena-leaderboard --repo-type space --local-dir CausalArena-leaderboard cd CausalArena-leaderboard
Open the page and move between its views. The table lists what each part shows. To contribute, read the Submit page.
| Tool | What it does |
|---|---|
| Main leaderboard | Ranks methods on the benchmark |
| Real-data checks | Shows how methods do on real data |
| Scaling and runtime views | Show how results change with size and how long runs take |
| Taxonomy summaries | Group the results by category |
| Case inspection | Lets you look at Semantic and Formula cases |
| SCM Gallery | Lets you browse the released grounded graphs |
| Submit page | Describes the audited steps for contributing |
A researcher with a new causal structure method reads the Submit page. They download the result template from the companion dataset repository, validate their package on their own machine, and submit through the companion GitHub repository. Maintainers refresh the dashboard after checking it.
Learning causal structure means working out which things cause which from data. The first small thing to try is the main leaderboard, then the SCM Gallery to see example graphs.
The page is static, so nothing computes on request and results appear only when maintainers update it. The contribution path is audited, with local validation of packages before submission. Its citation frames it as a benchmark for causal structure learning in the age of foundation models.
Submissions do not happen on the page itself, and the dashboard only updates after maintainers validate them.
A static dashboard of the CausalArena benchmark for learning causal structure, with a leaderboard, real-data checks, case views, a gallery of released graphs and a guide to submitting results.
Made by LAMDA-Tabular 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: CausalArena Leaderboard is a static dashboard for the public release of CausalArena, a benchmark for learning causal structure from data. It collects the main leaderboard, real-data checks, scaling and runtime views, taxonomy summaries, case inspection, and a gallery of released graphs. You open it in a browser and install nothing.
Who it suits: Researchers who work on learning causal structure and want to compare methods, and contributors who want to add their own results or new causal models.
Industry | Who uses it | How they use it | Example
Research | Causal structure researchers | Compare methods on the main leaderboard | Open the leaderboard page and read the rankings
Data science | Practitioners | Check how methods hold up on real data | Read the real-data checks
Education | Students | Browse released causal graphs | Open the SCM Gallery page
Benchmark contributors | Method authors | Submit results or propose a new causal model | Follow the Submit page steps
Anyone who is not working on causal structure learning can skip it.
Where to use it: It runs in these places.
Use case | Who | What they get back
Compare methods | Researchers | The main leaderboard and the scaling and runtime views
Inspect cases | Researchers | Semantic and Formula case views
Browse released graphs | Students and researchers | The SCM Gallery page of grounded graphs
How to set it up: Using the Space online needs only a browser. Getting its files onto your own computer needs the following.
pip install -U huggingface_hub
hf download LAMDA-Tabular/CausalArena-leaderboard --repo-type space --local-dir CausalArena-leaderboard
cd CausalArena-leaderboard
How to use it: Open the page and move between its views. The table lists what each part shows. To contribute, read the Submit page.
Tool | What it does
Main leaderboard | Ranks methods on the benchmark
Real-data checks | Shows how methods do on real data
Scaling and runtime views | Show how results change with size and how long runs take
Taxonomy summaries | Group the results by category
Case inspection | Lets you look at Semantic and Formula cases
SCM Gallery | Lets you browse the released grounded graphs
Submit page | Describes the audited steps for contributing
Example: A researcher with a new causal structure method reads the Submit page. They download the result template from the companion dataset repository, validate their package on their own machine, and submit through the companion GitHub repository. Maintainers refresh the dashboard after checking it.
For newcomers and experts: Learning causal structure means working out which things cause which from data. The first small thing to try is the main leaderboard, then the SCM Gallery to see example graphs.
The page is static, so nothing computes on request and results appear only when maintainers update it. The contribution path is audited, with local validation of packages before submission. Its citation frames it as a benchmark for causal structure learning in the age of foundation models.
You download the result or SCM template
You validate the package locally
You submit through the companion GitHub repository
Maintainers refresh the static dashboard
Good to know: Submissions do not happen on the page itself, and the dashboard only updates after maintainers validate them.
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 5 KB
Listed Hugging Face Spaces in the same category that share the most tags with this one.
| Product | Likes | Built with | License | Last change |
|---|---|---|---|---|
| CausalArena Leaderboard This page | 2 | static | cc-by-4.0 | |
| Arena LeaderboardShares static | 5,039 | static | apache-2.0 | |
| BrowserShares static | 533 | static | cc0-1.0 | |
| Model PulseShares static | 214 | static | apache-2.0 | |
| MTEB-BR LeaderboardShares static | 126 | static | apache-2.0 | |
| Hub StatsShares static | 65 | static | apache-2.0 |
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