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recipe-gpt-bloom-7b1
KVM1L/recipe-gpt-bloom-7b1
recipe-gpt-bloom-7b1 is a machine learning model from KVM1L. 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 transformers.
This model generates detailed step-by-step recipes based on provided ingredients.
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Public
Updated Jul 11, 2024
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31.5 MB
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At a glance
- Library
- transformers
- Access
- Public
- Created
- Jul 9, 2024
- Updated
- Jul 11, 2024
- SHA
- c8c41ee3
Model card
From the Hugging Face model README
Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->This model generates detailed step-by-step recipes based on provided ingredients.
Model Details
Model Description
<!-- Provide a longer summary of what this model is. -->This model is a text generation model developed using 🤗 transformers library. It specializes in creating detailed cooking recipes from a list of ingredients. The model utilizes quantization techniques for efficient deployment and performance.
- Developed by: KVM1L
- Model type: Text Generation (Causal Language Model)
- Language(s) (NLP): English
- License: [More Information Needed]
- Finetuned from model: bigscience/bloom-7b1
Model Sources [optional]
<!-- Provide the basic links for the model. -->- Repository: Colab Notebook: https://github.com/KVM1L03/RecipeGPT/blob/main/Python/recipeGPT.ipynb
Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->[More Information Needed]
Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->[More Information Needed]
Out-of-Scope Use
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Bias, Risks, and Limitations
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Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->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
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Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->Preprocessing [optional]
[More Information Needed]
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]
<!-- Relevant interpretability work for the model goes here -->[More Information Needed]
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).
- 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
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Compute Infrastructure
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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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More Information [optional]
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Model Card Authors [optional]
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Model Card Contact
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- Library
- transformers
- Created
- Jul 9, 2024
- Updated
- Jul 11, 2024