Instructions to use Washedashore/Berbble with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- fastText
How to use Washedashore/Berbble with fastText:
from huggingface_hub import hf_hub_download import fasttext model = fasttext.load_model(hf_hub_download("Washedashore/Berbble", "model.bin")) - Notebooks
- Google Colab
- Kaggle
- Model Card for Model ID
- Model Details
- Uses
- Bias, Risks, and Limitations
- How to Get Started with the Model
- Training Details
- Evaluation
- Model Examination [optional]
- Environmental Impact
- Technical Specifications [optional]
- Citation [optional]
- Glossary [optional]
- More Information [optional]
- Model Card Authors [optional]
- Model Card Contact
- [More---
model-index:
- name: Yi-34B
results:
- task:
type: text-generation
dataset:
name: ai2_arc
type: ai2_arc
metrics:
- name: AI2 Reasoning Challenge (25-Shot)
type: AI2 Reasoning Challenge (25-Shot)
value: 64.59
source:
name: Open LLM Leaderboard
url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard
Model Card for Model ID
This modelcard aims to be a base template for new models. It has been generated using this raw template.
Model Details
Model Description
- Developed by: [Washed Ashore Relics]
- Funded by [optional]: [Privately]
- Shared by [optional]: [More Information Needed]
- Model type: [Herbal Chatbox]
- Language(s) (NLP): [More Information Needed]
- License: [Apache 2.0]
- Finetuned from model [optional]: [n/a]
Model Sources [optional]
- Repository: [More Information Needed]
- Paper [optional]: [More Information Needed]
- Demo [optional]: [More Information Needed]
Uses
Direct Use
[More Information Needed]
Downstream Use [optional]
[More Information Needed]
Out-of-Scope Use
[More Information Needed]
Bias, Risks, and Limitations
[More Information Needed]
Recommendations
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
Training Details
Training Data
[More Information Needed]
Training Procedure
Preprocessing [optional]
[More Information Needed]
Training Hyperparameters
- Training regime: [More Information Needed]
Speeds, Sizes, Times [optional]
[More Information Needed]
Evaluation
Testing Data, Factors & Metrics
Testing Data
[More Information Needed]
Factors
[More Information Needed]
Metrics
[More Information Needed]
Results
[More Information Needed]
Summary
Model Examination [optional]
[More Information Needed]
Environmental Impact
Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).
- 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]
Technical Specifications [optional]
Model Architecture and Objective
[More Information Needed]
Compute Infrastructure
[More Information Needed]
Hardware
[More Information Needed]
Software
[More Information Needed]
Citation [optional]
BibTeX:
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APA:
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Glossary [optional]
[More Information Needed]
More Information [optional]
[More Information Needed]
Model Card Authors [optional]
[More Information Needed]
Model Card Contact
[More--- model-index: - name: Yi-34B results: - task: type: text-generation dataset: name: ai2_arc type: ai2_arc metrics: - name: AI2 Reasoning Challenge (25-Shot) type: AI2 Reasoning Challenge (25-Shot) value: 64.59 source: name: Open LLM Leaderboard url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard
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