Instructions to use gateremark/kikuyu_translategemma_12b_merged_V2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use gateremark/kikuyu_translategemma_12b_merged_V2 with Transformers:

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

pipe = pipeline("text-generation", model="gateremark/kikuyu_translategemma_12b_merged_V2")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
pipe(text=messages)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoProcessor, AutoModelForMultimodalLM

processor = AutoProcessor.from_pretrained("gateremark/kikuyu_translategemma_12b_merged_V2")
model = AutoModelForMultimodalLM.from_pretrained("gateremark/kikuyu_translategemma_12b_merged_V2", device_map="auto")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
inputs = processor.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=256)
print(processor.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use gateremark/kikuyu_translategemma_12b_merged_V2 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "gateremark/kikuyu_translategemma_12b_merged_V2"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "gateremark/kikuyu_translategemma_12b_merged_V2",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/gateremark/kikuyu_translategemma_12b_merged_V2

SGLang

How to use gateremark/kikuyu_translategemma_12b_merged_V2 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 "gateremark/kikuyu_translategemma_12b_merged_V2" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "gateremark/kikuyu_translategemma_12b_merged_V2",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

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 "gateremark/kikuyu_translategemma_12b_merged_V2" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "gateremark/kikuyu_translategemma_12b_merged_V2",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Unsloth Desktop
Docker Model Runner
How to use gateremark/kikuyu_translategemma_12b_merged_V2 with Docker Model Runner:
```
docker model run hf.co/gateremark/kikuyu_translategemma_12b_merged_V2
```

Uploaded finetuned model

Developed by: gateremark
License: apache-2.0
Finetuned from model : google/translategemma-12b-it

This gemma3 model was trained 2x faster with Unsloth and Huggingface's TRL library.

Kikuyu TranslateGemma-12B

Fine-tuned English → Kikuyu translation model based on Google's TranslateGemma-12B-it.

🌍 Live Demo: c-elo.com/c-elo-ai (Talk to our AI)

Learn about our training process and challenges faced

Model Details

Attribute	Value
Base Model	google/translategemma-12b-it
Parameters	12.4B
Fine-tuning Method	LoRA (r=128, alpha=256)
Training Data	30,430 English-Kikuyu pairs
BLEU Score	19.61
Framework	Unsloth + TRL

Usage

Quick Start

from transformers import AutoTokenizer, AutoModelForCausalLM
import torch

model_id = "gateremark/kikuyu_translategemma_12b_merged_V2"

tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype=torch.bfloat16,
    device_map="auto",
)

def translate_to_kikuyu(text: str) -> str:
    messages = [
        {
            "role": "user",
            "content": [{
                "type": "text",
                "source_lang_code": "en",
                "target_lang_code": "ki",
                "text": text
            }]
        }
    ]
    
    input_ids = tokenizer.apply_chat_template(
        messages,
        tokenize=True,
        add_generation_prompt=True,
        return_tensors="pt"
    ).to(model.device)
    
    terminators = [
        tokenizer.eos_token_id,
        tokenizer.convert_tokens_to_ids("<end_of_turn>"),
    ]
    
    with torch.no_grad():
        outputs = model.generate(
            input_ids=input_ids,
            max_new_tokens=256,
            temperature=0.3,
            do_sample=True,
            eos_token_id=terminators,
        )
    
    response = tokenizer.decode(
        outputs[0][input_ids.shape[1]:],
        skip_special_tokens=True
    )
    return response.strip()

# Example
print(translate_to_kikuyu("Hello, how are you?"))
# Output: Hihi, ũrĩ atĩa?

Using with Unsloth (Faster Inference)

from unsloth import FastLanguageModel

model, tokenizer = FastLanguageModel.from_pretrained(
    model_name="gateremark/kikuyu_translategemma_12b_merged_V2",
    max_seq_length=2048,
    dtype=None,
    load_in_4bit=True,  # Optional: reduce VRAM to ~6GB
)

Training Details

Dataset

Source: gateremark/english-kikuyu-translations
Size: 30,430 parallel sentences
Split: 95% train (28,908) / 5% eval (1,522)

Hyperparameters

Parameter	Value
LoRA rank (r)	128
LoRA alpha	256
LoRA dropout	0
Learning rate	2e-4
Batch size	64 (16 × 4 accum)
Epochs	2
Optimizer	AdamW 8-bit
NEFTune α	5
Weight decay	0.01

Target Modules

["q_proj", "k_proj", "v_proj", "o_proj", "gate_proj", "up_proj", "down_proj"]

Hardware

GPU: NVIDIA H200 (139GB VRAM)
Platform: Lightning.ai
Training Time: ~90 minutes

Evaluation

Metric	Score
BLEU	19.61
Eval Loss	0.578

Sample Translations

English	Kikuyu
Hello, how are you?	Hihi, ũrĩ atĩa?
The weather is beautiful today.	Rĩera nĩ rĩega mũno ũmũthĩ
I love learning new languages.	Nĩ ngenagĩra gũthoma thiomi njerũ

Limitations

Direction: English → Kikuyu only (reverse not trained)
Domain: General text; may struggle with technical/specialized content
Dialects: Trained on standard Kikuyu; dialect variations not covered
Evaluation: BLEU-based; human evaluation in progress

Intended Use

Translation tools for Kikuyu speakers
Language learning applications
Research on low-resource African language NLP
Cultural preservation initiatives

Citation

@misc{gatere2026kikuyutranslategemma,
  author = {Mark Gatere},
  title = {Kikuyu TranslateGemma-12B: Fine-tuning TranslateGemma for Low-Resource Bantu Language Translation},
  year = {2026},
  publisher = {Hugging Face},
  howpublished = {\url{https://huggingface.co/gateremark/kikuyu_translategemma_12b_merged_V2}}
}

Acknowledgments

Google for the TranslateGemma base model
Unsloth for 2x faster fine-tuning
Lightning.ai for GPU compute

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Safetensors

Model size

13B params

Tensor type

BF16

Model tree for gateremark/kikuyu_translategemma_12b_merged_V2

Base model

google/translategemma-12b-it

Adapter

(5)

this model