scaleszw/scales_ai

Model Card for Model ID

1. Natural Language Understanding and Generation: Scales AI excels in understanding and generating human-like text based on user input, utilizing the latest advancements in natural language processing.

2. Information Retrieval: Scales AI is capable of performing web searches to fetch information, utilizing the Google Custom Search API to provide users with up-to-date and relevant information from the web.

3. Entity Recognition and Tracking: Scales AI can identify and keep track of key entities mentioned during conversations, allowing for context-aware responses.

4. Memory of Conversation History: Scales AI can maintain a history of the ongoing conversation to ensure continuity and relevance in responses.

5. Error Handling and Robustness: Scales AI is designed to handle errors gracefully, providing meaningful feedback to users in case of issues and continuing the conversation without interruptions.

6. Shona speaking: Scales AI able to have conversations in the Shona language and also take an input in Shona language and perform a web search to provide the users with accurate, relevant and insightful responses.

Model Details

Model Description

Scales AI is a large language model that understands shona language better than other models

• Developed by: [Ronald Bvirinyangwe]
• Funded by [optional]: [More Information Needed]
• Shared by [optional]: [Ronald Bvirinyangwe]
• Model type: [Text-generation]
• Language(s) (NLP): [English,Shona]
• License: [llama3]
• Finetuned from model [optional]: [llama-3-8b-bnb-4bit]

Model Sources [optional]

• Repository: [scales_ai]
• 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:

[More Information Needed]

APA:

[More Information Needed]

Glossary [optional]

[More Information Needed]

More Information [optional]

[More Information Needed]

Model Card Authors [optional]

[More Information Needed]

Model Card Contact

[More Information Needed]

Framework versions

• PEFT 0.11.1