Instructions to use Alfaxad/gemma2-2b-swahili-preview with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Alfaxad/gemma2-2b-swahili-preview with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="Alfaxad/gemma2-2b-swahili-preview")

# Load model directly
from transformers import AutoTokenizer, AutoModelForMultimodalLM

tokenizer = AutoTokenizer.from_pretrained("Alfaxad/gemma2-2b-swahili-preview")
model = AutoModelForMultimodalLM.from_pretrained("Alfaxad/gemma2-2b-swahili-preview")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Alfaxad/gemma2-2b-swahili-preview with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Alfaxad/gemma2-2b-swahili-preview"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Alfaxad/gemma2-2b-swahili-preview",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/Alfaxad/gemma2-2b-swahili-preview

SGLang

How to use Alfaxad/gemma2-2b-swahili-preview with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "Alfaxad/gemma2-2b-swahili-preview" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Alfaxad/gemma2-2b-swahili-preview",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "Alfaxad/gemma2-2b-swahili-preview" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Alfaxad/gemma2-2b-swahili-preview",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use Alfaxad/gemma2-2b-swahili-preview with Docker Model Runner:
```
docker model run hf.co/Alfaxad/gemma2-2b-swahili-preview
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

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

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},
}