---
base_model: unsloth/llama-3-8b-bnb-4bit
library_name: peft
license: llama3
datasets:
- scaleszw/scales_shona_ai
language:
- sn
---

# 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]

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- **Repository:** [scales_ai]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]

## Uses

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### Direct Use

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### Downstream Use [optional]

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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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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

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#### Preprocessing [optional]

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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, Factors & Metrics

#### 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

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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).

- **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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**BibTeX:**

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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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### Framework versions

- PEFT 0.11.1