the resource page of the our resources collection on Huggingface, we highlight your currect position with a blue block. Dataset Dataset Link Annoy-PythonEdu-Rs Please also check the raw data after our processing
SAVRN Model Hub
AI Training Datasets
Each dataset with its card, its structure (every split, row count and column type), its files, its license, and the models that disclose training on it.
2,760Models
859Datasets
254Papers
1,692Publishers
5,040Sourced relationships
Updated 2026-09-18 · How the library is built
859 datasets, sorted by most downloaded.
the resource page of the our resources collection on Huggingface, we highlight your currect position with a blue block. Dataset Dataset Link Annoy-PythonEdu-Rs Please also check the raw data after our processing
the resource page of the our resources collection on Huggingface, we highlight your currect position with a blue block. Dataset Dataset Link Annoy-PythonEdu-Rs Please also check the raw data after our processing
This is the resource page of the our resources collection on Huggingface, we highlight your currect position with a blue block. Dataset Please also check the raw data after our processing if you are interested: saaffs454/Annoy-PyEdu-Rs-Raw. Models Introduction While having full executable code theoretically allows us to generate reliable execution trajectories as responses, two challenges arise: 1) Obtaining a deterministic reverse function for input prediction is impractical; 2) Automatically constructed trajectories are constrained by pre-designed templates and lack the expressiveness and generalizability of free-form natural language reasoning. Thus, we adopt a fully LLM-based approach…
the resource page of the our resources collection on Huggingface, we highlight your currect position with a blue block. Dataset Dataset Link Annoy-PythonEdu-Rs Please also check the raw data after our processing
the resource page of the our resources collection on Huggingface, we highlight your currect position with a blue block. Dataset Dataset Link Annoy-PythonEdu-Rs Please also check the raw data after our processing
the resource page of the our resources collection on Huggingface, we highlight your currect position with a blue block. Dataset Dataset Link Annoy-PythonEdu-Rs Please also check the raw data after our processing
This is the resource page of the our resources collection on Huggingface, we highlight your currect position with a blue block. Dataset Please also check the raw data after our processing if you are interested: aSsadASD1/Annoy-PyEdu-Rs-Raw. Models Introduction While having full executable code theoretically allows us to generate reliable execution trajectories as responses, two challenges arise: 1) Obtaining a deterministic reverse function for input prediction is impractical; 2) Automatically constructed trajectories are constrained by pre-designed templates and lack the expressiveness and generalizability of free-form natural language reasoning. Thus, we adopt a fully LLM-based approach…
the resource page of the our resources collection on Huggingface, we highlight your currect position with a blue block. Dataset Dataset Link Annoy-PythonEdu-Rs Please also check the raw data after our processing
This is the resource page of the our resources collection on Huggingface, we highlight your currect position with a blue block. Dataset Please also check the raw data after our processing if you are interested: sddsasd/Annoy-PyEdu-Rs-Raw. Models Introduction While having full executable code theoretically allows us to generate reliable execution trajectories as responses, two challenges arise: 1) Obtaining a deterministic reverse function for input prediction is impractical; 2) Automatically constructed trajectories are constrained by pre-designed templates and lack the expressiveness and generalizability of free-form natural language reasoning. Thus, we adopt a fully LLM-based approach for…
the resource page of the our resources collection on Huggingface, we highlight your currect position with a blue block. Dataset Dataset Link Annoy-PythonEdu-Rs Please also check the raw data after our processing
the resource page of the our resources collection on Huggingface, we highlight your currect position with a blue block. Dataset Dataset Link Annoy-PythonEdu-Rs Please also check the raw data after our processing
the resource page of the our resources collection on Huggingface, we highlight your currect position with a blue block. Dataset Dataset Link Annoy-PythonEdu-Rs Please also check the raw data after our processing
the resource page of the our resources collection on Huggingface, we highlight your currect position with a blue block. Dataset Dataset Link Annoy-PythonEdu-Rs Please also check the raw data after our processing
the resource page of the our resources collection on Huggingface, we highlight your currect position with a blue block. Dataset Dataset Link Annoy-PythonEdu-Rs Please also check the raw data after our processing
This is the resource page of the our resources collection on Huggingface, we highlight your currect position with a blue block. Dataset Please also check the raw data after our processing if you are interested: safaf3e23/Annoy-PyEdu-Rs-Raw. Models Introduction While having full executable code theoretically allows us to generate reliable execution trajectories as responses, two challenges arise: 1) Obtaining a deterministic reverse function for input prediction is impractical; 2) Automatically constructed trajectories are constrained by pre-designed templates and lack the expressiveness and generalizability of free-form natural language reasoning. Thus, we adopt a fully LLM-based approach…
Annoy: This should be a the raw data for our processed PythonEdu-Rs dataset, adopted from the original dataset from HuggingFaceTB team. The data format for each line in the 0368500filteredv2ds25.sced.jsonl is as follows: { "problemdescription"
Annoy: This should be a the raw data for our processed PythonEdu-Rs dataset, adopted from the original dataset from HuggingFaceTB team. The data format for each line in the 0368500filteredv2ds25.sced.jsonl is as follows: { "problemdescription"
Annoy: This should be a the raw data for our processed PythonEdu-Rs dataset, adopted from the original dataset from HuggingFaceTB team. The data format for each line in the 0368500filteredv2ds25.sced.jsonl is as follows: { "problemdescription"
Annoy: This should be a the raw data for our processed PythonEdu-Rs dataset, adopted from the original dataset from HuggingFaceTB team. The data format for each line in the 0368500filteredv2ds25.sced.jsonl is as follows: { "problemdescription"
Annoy: This should be a the raw data for our processed PythonEdu-Rs dataset, adopted from the original dataset from HuggingFaceTB team. The data format for each line in the 0368500filteredv2ds25.sced.jsonl is as follows: { "problemdescription"
Annoy: This should be a the raw data for our processed PythonEdu-Rs dataset, adopted from the original dataset from HuggingFaceTB team. The data format for each line in the 0368500filteredv2ds25.sced.jsonl is as follows: { "problemdescription"
Annoy: This should be a the raw data for our processed PythonEdu-Rs dataset, adopted from the original dataset from HuggingFaceTB team. The data format for each line in the 0368500filteredv2ds25.sced.jsonl is as follows: { "problemdescription"
We release the raw data for our processed PythonEdu-Rs dataset, adopted from the original dataset from HuggingFaceTB team. The data format for each line in the 0368500filteredv2ds25.sced.jsonl is as follows: Some of the ios are empty. The reason is that when executing the code, the input/output sizes are too large and exceed our required constraints. Thus, they are not stored or used later. Note: Due to imperfect LLM-based transformations, some problem descriptions do not contain enough information to describe the code. We leave this as future work to further enhance our data and update it to a better version.
Model Collections
Hand-picked starting points, each with the reason it exists.
Collection · 4 entries
Embedding models for retrieval
Sentence and document embedding models used to build retrieval systems. Dimension and sequence length matter more than size here, and both come from the publisher.
Collection · 4 entries
Models that fit on one accelerator
Models whose publisher-reported parameter count puts them within reach of a single accelerator at common precisions. Memory needed depends on precision and serving configuration, so treat the parameter count as the starting point, not the answer.
Collection · 6 entries
Open-weight text models worth knowing
Widely used open-weight language models, chosen because each one is a distinct family rather than a variant of the one above it. Selection, not a ranking.
Collection · 3 entries
Speech and audio models
Recognition and synthesis models, grouped so the two directions are easy to compare.
Related SAVRN Research
The hub sits beside SAVRN's market data and infrastructure research: what models cost to run, and what it takes to run them.
SAVRN Index
What open models cost to run
The same open-weight model priced by every host that serves it, per million tokens.
Research Hub
Data center trackers and maps
Moratoriums, permits, power, water and capital behind the facilities that run these models.
Method
How the Model Hub is built
Sources, evidence labels, refresh behaviour, and the limits of every comparison here.