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Parameter descriptions:

Base Model Data
Are datasources for training the base model comprehensively documented and made available? In case a distinction between base (foundation) and end (user) model is not applicable, this mirrors the end model data entries.
End User Model Data
Are datasources for training the model that the end user interacts with comprehensively documented and made available?
Base Model Weights
Are the weights of the base models made freely available? In case a distinction between base (foundation) and end (user) model is not applicable, this mirrors the end model data entries.
End User Model Weights
Are the weights of the model that the end user interacts with made freely available?
Training Code
Is the source code of dataset processing, model training and tuning comprehensively made available?
Code Documentation
Is the source code of datasource processing, model training and tuning comprehensively documented?
Hardware Architecture
Is the hardware architecture used for datasource processing and model training comprehensively documented?
Preprint
Are archived preprint(s) are available that detail all major parts of the system including datasource processing, model training and tuning steps?
Paper
Are peer-reviewed scientific publications available that detail all major parts of the system including datasource processing, model training and tuning steps?
Modelcard
Is a model card available in standardized format that provides comprehensive insight on model architecture, training, fine-tuning, and evaluation?
Datasheet
Is a datasheet as defined in "Datasheets for Datasets" (Gebru et al. 2021) available?
Package
Is a packaged release of the model available on a software repository (e.g. a Python Package Index, Homebrew)?
API and Meta Prompts
Is an API available that provides unrestricted access to the model (other than security and CDN restrictions)? If applicable, this entry also collects information on the use and availability of meta prompts.
Licenses
Is the project fully covered by Open Source Initiative (OSI)-approved licenses, including all data sources and training pipeline code?

33x-Coder

by Common Sense

Pre-CodeLlama coder model
Code
Limited
https://huggingface.co/senseable/33x-coder
Llama-2-34B
33x-Coder
Apache-2.0
Common Sense, a collective of LLM developers
https://huggingface.co/senseable
January 2024
Availability
Base Model Data
Data nowhere disclosed or documented, and described only in the vaguest terms in a corporate preprint released by Meta
https://ai.meta.com/research/publications/llama-2-open-foundation-and-fine-tuned-chat-models/
End User Model Data
Two data sources linked, however unclear if they are the only ones used.
https://huggingface.co/datasets/andersonbcdefg/synthetic_retrieval_taskshttps://huggingface.co/datasets/ise-uiuc/Magicoder-Evol-Instruct-110K
Base Model Weights
Download only after requesting access; requires signing a consent form
https://ai.meta.com/resources/models-and-libraries/llama-downloads/
End User Model Weights
Weights made available on HuggingFace.
https://huggingface.co/senseable/33x-coder
Training Code
No repo containing training code found.
Documentation
Code Documentation
No training code, so undocumented.
Hardware Architecture
No hardware architecture documented.
Preprint
No preprint found.
Paper
No peer-reviewed paper found.
Modelcard
Model card provides only surface-level detail.
https://huggingface.co/senseable/33x-coder
Datasheet
No datasheet found.
Access
Package
No package found.
API and Meta Prompts
HuggingFace space returns error.
https://huggingface.co/spaces/senseable/senseable-33x-coder
Licenses
Apache-2.0, an OSI-approved license.
https://huggingface.co/senseable/33x-coder
Last updated 17 Jun 2025
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