c-elo/kikuyu_translategemma_4b_v7_highrank_rslora

Uploaded finetuned model

• —Developed by: gateremark
• —License: apache-2.0
• —Finetuned from model: google/translategemma-4b-it

This Gemma3 / TranslateGemma model was trained with Unsloth and Hugging Face's TRL library.

<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>

C-elo Labs Organization Release

This repository is the C-elo Labs organization release of the Kikuyu TranslateGemma-4B V7 model, originally developed by Mark Gatere under `gateremark/kikuyu_translategemma_4b_v7_highrank_rslora`.

This is the current preferred C-elo Labs English → Kikuyu translation model for production-facing use. The original gateremark repository remains available as the development-history copy, while this c-elo repository is maintained for C-elo Labs research, product, and public release references.

Kikuyu TranslateGemma-4B V7

Fine-tuned English -> Kikuyu translation model based on Google's TranslateGemma-4B-it.

This is the current fast production model behind C-elo Translate. It was trained as a smaller, faster alternative to the earlier 12B model while improving automatic evaluation scores and manual translation quality.

Live demo: c-elo.com/c-elo-ai

Previous 12B model: gateremark/kikuyu_translategemma_12b_merged_V2

Model Details

Why This Model

The earlier 12B Kikuyu TranslateGemma model reached 19.61 BLEU, but it was large and slower to cold-start in production. This V7 4B-family model is smaller, faster to load, and evaluated better on the same held-out split:

Usage

Recommended: Unsloth / Gemma3Processor Path

This model uses the TranslateGemma/Gemma3 chat template. For reliable generation, use the processor for apply_chat_template() and the underlying text tokenizer for tokenization/decoding.

import torch
from unsloth import FastLanguageModel

model_id = "gateremark/kikuyu_translategemma_4b_v7_highrank_rslora"

model, processor = FastLanguageModel.from_pretrained(
    model_name=model_id,
    max_seq_length=4096,
    dtype=None,
    load_in_4bit=False,  # Set True if you need lower VRAM and accept possible quality changes.
)

text_tokenizer = (
    getattr(processor, "tokenizer", None)
    or getattr(processor, "text_tokenizer", None)
    or processor
)

if text_tokenizer.pad_token_id is None:
    text_tokenizer.pad_token = text_tokenizer.eos_token

model.config.pad_token_id = text_tokenizer.pad_token_id
text_tokenizer.padding_side = "left"
FastLanguageModel.for_inference(model)

terminators = []
for token_id in [
    text_tokenizer.eos_token_id,
    text_tokenizer.convert_tokens_to_ids("<end_of_turn>"),
    text_tokenizer.convert_tokens_to_ids("<eos>"),
]:
    if (
        isinstance(token_id, int)
        and token_id >= 0
        and token_id != getattr(text_tokenizer, "unk_token_id", None)
        and token_id not in terminators
    ):
        terminators.append(token_id)


def translate_to_kikuyu(text: str) -> str:
    messages = [
        {
            "role": "user",
            "content": [
                {
                    "type": "text",
                    "source_lang_code": "en",
                    "target_lang_code": "ki",
                    "text": text,
                }
            ],
        }
    ]

    formatted_text = processor.apply_chat_template(
        messages,
        tokenize=False,
        add_generation_prompt=True,
    )

    inputs = text_tokenizer(
        [formatted_text],
        return_tensors="pt",
        padding=True,
    )
    inputs = {key: value.to(model.device) for key, value in inputs.items()}

    with torch.no_grad():
        outputs = model.generate(
            **inputs,
            max_new_tokens=128,
            do_sample=False,
            eos_token_id=terminators,
            pad_token_id=text_tokenizer.pad_token_id,
        )

    input_len = inputs["input_ids"].shape[1]
    response = text_tokenizer.decode(
        outputs[0][input_len:],
        skip_special_tokens=True,
    )
    return response.strip()


print(translate_to_kikuyu("Hello, how are you?"))
# Example output: Ndũmĩrĩrie, ũraigua atĩa?

Minimal Inference Notes

• —Use target_lang_code="ki" for Kikuyu.
• —Use left padding for batched generation with decoder-only models.
• —Deterministic decoding (do_sample=False) is recommended for translation.
• —The model is trained for English -> Kikuyu. Reverse Kikuyu -> English was not part of this run.

Training Details

Dataset

• —Dataset: gateremark/english-kikuyu-translations
• —Size: 30,430 parallel English-Kikuyu sentence pairs
• —Split: 95% train / 5% eval
• —Train examples: 28,908
• —Eval examples: 1,522

V7 Hyperparameters

Target Modules

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

Evaluation

Evaluation was run on the held-out 5% split from the same dataset using BLEU and chrF++.

Automatic metrics are useful for regression testing, but Kikuyu quality should also be checked with native-speaker review because morphology, idiom, tone, and dialect variation are not fully captured by BLEU.

Sample Translations

Intended Use

• —English -> Kikuyu translation tools
• —Kikuyu language learning applications
• —Low-resource African language NLP research
• —Cultural and linguistic preservation projects
• —Prototyping multilingual AI interfaces for Kikuyu speakers

Limitations

• —Direction: English -> Kikuyu only. Kikuyu -> English was not trained in this run.
• —Language coverage: Optimized for Kikuyu (ki), not other Gikuyu-related dialects or neighboring Bantu languages.
• —Domain: Best for general text. Technical, legal, medical, poetic, or highly idiomatic content may need human review.
• —Evaluation: BLEU and chrF++ do not fully measure naturalness, dialect fit, or cultural nuance.
• —Production use: Review outputs before high-stakes use.

Citation

@misc{gatere2026kikuyutranslategemma4bv7,
  author = {Mark Gatere},
  title = {Kikuyu TranslateGemma-4B V7: rsLoRA Fine-tuning for English to Kikuyu Translation},
  year = {2026},
  publisher = {Hugging Face},
  howpublished = {\url{https://huggingface.co/gateremark/kikuyu_translategemma_4b_v7_highrank_rslora}}
}

Acknowledgments

• —Google for TranslateGemma-4B-it
• —Unsloth for efficient fine-tuning
• —Hugging Face for model and dataset hosting
• —Modal for GPU training and deployment infrastructure
• —Kikuyu speakers and reviewers supporting C-elo's translation work