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gemma2-2b-swahili-preview

Text Generation
Transformers
Safetensors
Swahili
gemma2
text-2-text
llms
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metadata
license: apache-2.0
datasets:
  - Alfaxad/Inkuba-Mono-Swahili
language:
  - sw
pipeline_tag: text-generation
library_name: transformers
tags:
  - gemma2
  - text-2-text
  - text-generation
  - llms
base_model:
  - google/gemma-2-2b

Gemma2-2B-Swahili-Preview

Gemma2-2B-Swahili-Preview is a Swahili variation of the base language model Gemma2 2B fine-tuned on the Inkuba-Mono Swahili dataset, designed to enhance Swahili language understanding through monolingual training.

Model Details

  • Developer: Alfaxad Eyembe
  • Base Model: google/gemma-2-2b
  • Model Type: Decoder-only transformer
  • Language: Swahili
  • License: Apache 2.0
  • Fine-tuning Approach: Low-Rank Adaptation (LoRA)

Training Data

The model was fine-tuned on a focused subset of the Inkuba-Mono dataset:

  • 1,000,000 randomly selected examples
  • Total tokens: 60,831,073
  • Average text length: 101.33 characters
  • Diverse Swahili text sources including news, social media, and various domains

Training Details

  • Fine-tuning Method: LoRA
  • Training Steps: 2,500
  • Batch Size: 2
  • Gradient Accumulation Steps: 32
  • Learning Rate: 2e-4
  • Training Time: ~7.5 hours

image/png

Model Capabilities

This model is designed for:

  • Swahili text continuation
  • Natural language understanding
  • Contextual text generation
  • Base language modeling for Swahili

Usage 

from transformers import AutoTokenizer, AutoModelForCausalLM
import torch

tokenizer = AutoTokenizer.from_pretrained("alfaxadeyembe/gemma2-2b-swahili-preview")
model = AutoModelForCausalLM.from_pretrained(
    "alfaxadeyembe/gemma2-2b-swahili-preview",
    device_map="auto",
    torch_dtype=torch.bfloat16
)

# Set to evaluation mode
model.eval()

# Example usage
prompt = "Katika soko la Kariakoo, teknolojia mpya imewezesha"
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(
    **inputs,
    max_new_tokens=500,
    do_sample=True,
    temperature=0.7,
    top_p=0.95
)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(response)

Key Features

  • Natural Swahili text continuation
  • Strong cultural context understanding
  • Efficient parameter updates through LoRA
  • Diverse domain knowledge integration

Limitations

  • Not instruction-tuned
  • Base language modeling capabilities
  • Performance varies across different text domains

Citation 

@misc{gemma2-2b-swahili-preview,
  author = {Alfaxad Eyembe},
  title = {Gemma2-2B-Swahili-Preview: Swahili Variation of Gemma2 2B},
  year = {2025},
  publisher = {Hugging Face},
  journal = {Hugging Face Model Hub},
}

Contact

For questions or feedback, please reach out through: