---
library_name: transformers
datasets:
- Svngoku/xP3x-Kongo
language:
- kg
metrics:
- bleu
pipeline_tag: text-generation
tags:
- africa
- languages
---

# Kongo Llama Experiment

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## Model Details

### Model Description

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This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.

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## Uses

```py
# Load the model
model = LlamaForCausalLM.from_pretrained('/content/kongo-llama/checkpoint-9000')

# Prepare input text
text = "Nzambi "
inputs = wrapped_tokenizer(text, return_tensors="pt")

# Generate text
generated_ids = model.generate(
    max_length=150,  # Increased length
    num_beams=5,      # Use beam search
    temperature=0.7,  # Adjust temperature for creativity
    do_sample=True,
    top_k=50,         # Limit vocabulary for next token
    top_p=0.95        # Nucleus sampling
)

# Decode and print the generated text
generated_text = wrapped_tokenizer.batch_decode(generated_ids, skip_special_tokens=True)
print(generated_text)
```

### Direct Use

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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

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## Training Details

### Training Data

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### Training Procedure

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#### Preprocessing [optional]

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#### Training Hyperparameters

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## Evaluation

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### Testing Data, Factors & Metrics

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#### Factors

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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).

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## Technical Specifications [optional]

### Model Architecture and Objective

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## Citation [optional]

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## Glossary [optional]

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