kikuyu_translategemma_4b_v7_highrank_rslora/README.md

---
base_model: google/translategemma-4b-it
pipeline_tag: text-generation
library_name: transformers
tags:
- text-generation-inference
- transformers
- unsloth
- gemma4
- translation
- translategemma
- kikuyu
- african-languages
- low-resource
- lora
- rslora
license: apache-2.0
language:
- en
- ki
model-index:
- name: kikuyu_translategemma_4b_v7_highrank_rslora
  results:
  - task:
      type: translation
      name: English to Kikuyu Translation
    dataset:
      name: gateremark/english-kikuyu-translations
      type: gateremark/english-kikuyu-translations
      split: eval
    metrics:
    - type: bleu
      value: 21.93
      name: BLEU
    - type: chrf
      value: 42.87
      name: chrF++
---

# 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](https://github.com/unslothai/unsloth) and Hugging Face's TRL library.

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

# Kikuyu TranslateGemma-4B V7

Fine-tuned **English -> Kikuyu** translation model based on Google's [TranslateGemma-4B-it](https://huggingface.co/google/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](https://c-elo.com/c-elo-ai)

**Previous 12B model:** [gateremark/kikuyu_translategemma_12b_merged_V2](https://huggingface.co/gateremark/kikuyu_translategemma_12b_merged_V2)

## Model Details

| Attribute | Value |
|-----------|-------|
| Base model | google/translategemma-4b-it |
| Model family | TranslateGemma / Gemma3 |
| Hub size | ~5B parameters, BF16 safetensors |
| Fine-tuning method | rsLoRA, high-rank LoRA |
| LoRA rank / alpha | r=256, alpha=256 |
| Training data | 30,430 English-Kikuyu pairs |
| Direction | English -> Kikuyu |
| BLEU | **21.93** |
| chrF++ | **42.87** |
| Eval loss | 0.7518 |
| Framework | Unsloth + TRL + Transformers |
| Training platform | Modal, NVIDIA H100 |

## 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:

| Model | BLEU | chrF++ | Notes |
|-------|------|--------|-------|
| 12B LoRA V2 | 19.61 | - | Earlier production model |
| 4B V2 LoRA r256 | 17.76 | 38.31 | Strong first 4B run |
| 4B V3 DoRA r128 | 15.81 | 35.88 | DoRA did not improve this setup |
| 4B V6 LoRA r256, 4 epochs | 17.67 | 38.28 | Longer training did not improve V2 |
| **4B V7 rsLoRA r256** | **21.93** | **42.87** | Current champion |

## 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.

```python
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](https://huggingface.co/datasets/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

| Parameter | Value |
|-----------|-------|
| Method | rsLoRA |
| LoRA rank | 256 |
| LoRA alpha | 256 |
| LoRA dropout | 0 |
| DoRA | False |
| Epochs | 3 |
| Learning rate | 1e-4 |
| Batch size | 32 effective batch |
| Optimizer | AdamW 8-bit |
| Weight decay | 0.01 |
| Precision | BF16 |
| Max sequence length | 4096 |

### Target Modules

```python
[
    "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++.

| Metric | Score |
|--------|-------|
| BLEU | **21.93** |
| chrF++ | **42.87** |
| Eval loss | 0.7518 |

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

| 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 nyendete kwĩruta thiomi njerũ |

## 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

```bibtex
@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](https://huggingface.co/google/translategemma-4b-it)
- [Unsloth](https://github.com/unslothai/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