| | --- |
| | license: other |
| | tags: |
| | - text-generation |
| | --- |
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| | # Model Card for bt-opt-350m |
| | |
| | # Model Details |
| | |
| | ## Model Description |
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| | More information needed |
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| | - **Developed by:** Opentensor |
| | - **Shared by [Optional]:** Opentensor |
| | - **Model type:** Text Generation |
| | - **Language(s) (NLP):** More information needed |
| | - **License:** Other |
| | - **Parent Model:** OPT |
| | - **Resources for more information:** More information needed |
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| | # Uses |
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| | ## Direct Use |
| | This model can be used for the task of Text Generation |
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| | ## Downstream Use [Optional] |
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| | More information needed. |
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| | ## Out-of-Scope Use |
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| | The model should not be used to intentionally create hostile or alienating environments for people. |
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| | # Bias, Risks, and Limitations |
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| | Significant research has explored bias and fairness issues with language models (see, e.g., [Sheng et al. (2021)](https://aclanthology.org/2021.acl-long.330.pdf) and [Bender et al. (2021)](https://dl.acm.org/doi/pdf/10.1145/3442188.3445922)). Predictions generated by the model may include disturbing and harmful stereotypes across protected classes; identity characteristics; and sensitive, social, and occupational groups. |
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| | ## Recommendations |
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| | Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. |
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| | # Training Details |
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| | ## Training Data |
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| | More information needed |
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| | ## Training Procedure |
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| | ### Preprocessing |
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| | More information needed |
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| | ### Speeds, Sizes, Times |
| | More information needed |
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| | # Evaluation |
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| | ## Testing Data, Factors & Metrics |
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| | ### Testing Data |
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| | More information needed |
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| | ### Factors |
| | More information needed |
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| | ### Metrics |
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| | More information needed |
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| | ## Results |
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| | More information needed |
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| | # Model Examination |
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| | More information needed |
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| | # Environmental Impact |
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| | Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). |
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| | - **Hardware Type:** More information needed |
| | - **Hours used:** More information needed |
| | - **Cloud Provider:** More information needed |
| | - **Compute Region:** More information needed |
| | - **Carbon Emitted:** More information needed |
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| | # Technical Specifications [optional] |
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| | ## Model Architecture and Objective |
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| | More information needed |
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| | ## Compute Infrastructure |
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| | More information needed |
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| | ### Hardware |
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| | More information needed |
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| | ### Software |
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| | More information needed. |
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| | # Citation |
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| | **BibTeX:** |
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| | More information needed. |
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| | # Glossary [optional] |
| | More information needed |
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| | # More Information [optional] |
| | More information needed |
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| | # Model Card Authors [optional] |
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| | Opentensor in collaboration with Ezi Ozoani and the Hugging Face team |
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| | # Model Card Contact |
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| | More information needed |
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| | # How to Get Started with the Model |
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| | Use the code below to get started with the model. |
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| | <details> |
| | <summary> Click to expand </summary> |
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| | ```python |
| | from transformers import AutoTokenizer, AutoModelForCausalLM |
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| | tokenizer = AutoTokenizer.from_pretrained("opentensor/bt-opt-350m") |
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| | model = AutoModelForCausalLM.from_pretrained("opentensor/bt-opt-350m") |
| | ``` |
| | </details> |
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