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Test-sagemaker-metrics-Llama2-13b-Multilanguage-3.0.3
Weni/Test-sagemaker-metrics-Llama2-13b-Multilanguage-3.0.3
Test-sagemaker-metrics-Llama2-13b-Multilanguage-3.0.3 is a machine learning model from Weni. Use it for the machine learning task on the model card, and read the license before you ship it in a product. It is set up for pytorch. The card lists the license as mit.
plano de treinamento zeroshot 2.2.1 -------------------------- zeroshot 2.0.1 Llama13-b multilanguage 3.0.3 zeroshot 3.0.1 mistral-7b multilanguage 3.0.3
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Updated Dec 5, 2023
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At a glance
- Library
- pytorch
- License
- mit
- Access
- Public
- Created
- Dec 5, 2023
- Updated
- Dec 5, 2023
- SHA
- bf46ade9
Model card
From the Hugging Face model README
Model Card for Weni/Test-sagemaker-metrics-Llama2-13b-Multilanguage-3.0.3
<!-- Provide a quick summary of what the model is/does. -->Model Details
Model Description
<!-- Provide a longer summary of what this model is. -->plano de treinamento zeroshot 2.2.1
zeroshot 2.0.1 Llama13-b multilanguage 3.0.3 zeroshot 3.0.1 mistral-7b multilanguage 3.0.3
- Developed by: Weni
- Shared by [optional]: [More Information Needed]
- Model type: [More Information Needed]
- Language(s) (NLP): ['pt', 'en', 'es']
- License: mit
- Finetuned from model [optional]: [More Information Needed]
Model Sources [optional]
<!-- Provide the basic links for the model. -->- Repository: https://huggingface.co/Weni/Test-sagemaker-metrics-Llama2-13b-Multilanguage-3.0.3/tree/main
- Paper [optional]: [More Information Needed]
- Demo [optional]: [More Information Needed]
Uses
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Out-of-Scope Use
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Bias, Risks, and Limitations
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Recommendations
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How to Get Started with the Model
Use the code below to get started with the model.
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Training Details
Training Data
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Training Procedure
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Training Hyperparameters
- Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
Speeds, Sizes, Times [optional]
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Evaluation
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Testing Data
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Factors
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Metrics
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Results
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Summary
Model Examination [optional]
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Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).
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Technical Specifications [optional]
Model Architecture and Objective
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Hardware
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Software
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Citation [optional]
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APA:
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Glossary [optional]
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- Library
- pytorch
- License
- mit
- Languages
- pt, en, es
- Created
- Dec 5, 2023
- Updated
- Dec 5, 2023