288 lines
8.6 KiB
Markdown
288 lines
8.6 KiB
Markdown
---
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base_model: google/translategemma-4b-it
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pipeline_tag: text-generation
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library_name: transformers
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tags:
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- text-generation-inference
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- transformers
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- unsloth
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- gemma4
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- translation
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- translategemma
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- kikuyu
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- african-languages
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- low-resource
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- lora
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- rslora
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license: apache-2.0
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language:
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- en
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- ki
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model-index:
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- name: kikuyu_translategemma_4b_v7_highrank_rslora
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results:
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- task:
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type: translation
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name: English to Kikuyu Translation
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dataset:
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name: gateremark/english-kikuyu-translations
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type: gateremark/english-kikuyu-translations
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split: eval
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metrics:
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- type: bleu
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value: 21.93
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name: BLEU
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- type: chrf
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value: 42.87
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name: chrF++
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---
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# Uploaded finetuned model
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- **Developed by:** gateremark
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- **License:** apache-2.0
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- **Finetuned from model:** google/translategemma-4b-it
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This Gemma3 / TranslateGemma model was trained with [Unsloth](https://github.com/unslothai/unsloth) and Hugging Face's TRL library.
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[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
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# Kikuyu TranslateGemma-4B V7
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Fine-tuned **English -> Kikuyu** translation model based on Google's [TranslateGemma-4B-it](https://huggingface.co/google/translategemma-4b-it).
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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.
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**Live demo:** [c-elo.com/c-elo-ai](https://c-elo.com/c-elo-ai)
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**Previous 12B model:** [gateremark/kikuyu_translategemma_12b_merged_V2](https://huggingface.co/gateremark/kikuyu_translategemma_12b_merged_V2)
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## Model Details
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| Attribute | Value |
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|-----------|-------|
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| Base model | google/translategemma-4b-it |
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| Model family | TranslateGemma / Gemma3 |
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| Hub size | ~5B parameters, BF16 safetensors |
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| Fine-tuning method | rsLoRA, high-rank LoRA |
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| LoRA rank / alpha | r=256, alpha=256 |
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| Training data | 30,430 English-Kikuyu pairs |
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| Direction | English -> Kikuyu |
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| BLEU | **21.93** |
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| chrF++ | **42.87** |
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| Eval loss | 0.7518 |
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| Framework | Unsloth + TRL + Transformers |
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| Training platform | Modal, NVIDIA H100 |
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## Why This Model
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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:
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| Model | BLEU | chrF++ | Notes |
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|-------|------|--------|-------|
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| 12B LoRA V2 | 19.61 | - | Earlier production model |
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| 4B V2 LoRA r256 | 17.76 | 38.31 | Strong first 4B run |
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| 4B V3 DoRA r128 | 15.81 | 35.88 | DoRA did not improve this setup |
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| 4B V6 LoRA r256, 4 epochs | 17.67 | 38.28 | Longer training did not improve V2 |
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| **4B V7 rsLoRA r256** | **21.93** | **42.87** | Current champion |
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## Usage
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### Recommended: Unsloth / Gemma3Processor Path
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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.
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```python
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import torch
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from unsloth import FastLanguageModel
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model_id = "gateremark/kikuyu_translategemma_4b_v7_highrank_rslora"
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model, processor = FastLanguageModel.from_pretrained(
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model_name=model_id,
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max_seq_length=4096,
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dtype=None,
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load_in_4bit=False, # Set True if you need lower VRAM and accept possible quality changes.
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)
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text_tokenizer = (
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getattr(processor, "tokenizer", None)
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or getattr(processor, "text_tokenizer", None)
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or processor
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)
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if text_tokenizer.pad_token_id is None:
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text_tokenizer.pad_token = text_tokenizer.eos_token
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model.config.pad_token_id = text_tokenizer.pad_token_id
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text_tokenizer.padding_side = "left"
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FastLanguageModel.for_inference(model)
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terminators = []
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for token_id in [
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text_tokenizer.eos_token_id,
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text_tokenizer.convert_tokens_to_ids("<end_of_turn>"),
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text_tokenizer.convert_tokens_to_ids("<eos>"),
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]:
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if (
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isinstance(token_id, int)
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and token_id >= 0
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and token_id != getattr(text_tokenizer, "unk_token_id", None)
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and token_id not in terminators
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):
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terminators.append(token_id)
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def translate_to_kikuyu(text: str) -> str:
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messages = [
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{
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"role": "user",
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"content": [
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{
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"type": "text",
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"source_lang_code": "en",
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"target_lang_code": "ki",
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"text": text,
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}
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],
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}
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]
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formatted_text = processor.apply_chat_template(
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messages,
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tokenize=False,
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add_generation_prompt=True,
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)
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inputs = text_tokenizer(
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[formatted_text],
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return_tensors="pt",
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padding=True,
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)
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inputs = {key: value.to(model.device) for key, value in inputs.items()}
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with torch.no_grad():
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outputs = model.generate(
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**inputs,
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max_new_tokens=128,
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do_sample=False,
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eos_token_id=terminators,
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pad_token_id=text_tokenizer.pad_token_id,
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)
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input_len = inputs["input_ids"].shape[1]
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response = text_tokenizer.decode(
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outputs[0][input_len:],
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skip_special_tokens=True,
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)
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return response.strip()
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print(translate_to_kikuyu("Hello, how are you?"))
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# Example output: Ndũmĩrĩrie, ũraigua atĩa?
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```
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### Minimal Inference Notes
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- Use `target_lang_code="ki"` for Kikuyu.
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- Use left padding for batched generation with decoder-only models.
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- Deterministic decoding (`do_sample=False`) is recommended for translation.
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- The model is trained for English -> Kikuyu. Reverse Kikuyu -> English was not part of this run.
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## Training Details
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### Dataset
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- **Dataset:** [gateremark/english-kikuyu-translations](https://huggingface.co/datasets/gateremark/english-kikuyu-translations)
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- **Size:** 30,430 parallel English-Kikuyu sentence pairs
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- **Split:** 95% train / 5% eval
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- **Train examples:** 28,908
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- **Eval examples:** 1,522
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### V7 Hyperparameters
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| Parameter | Value |
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|-----------|-------|
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| Method | rsLoRA |
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| LoRA rank | 256 |
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| LoRA alpha | 256 |
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| LoRA dropout | 0 |
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| DoRA | False |
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| Epochs | 3 |
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| Learning rate | 1e-4 |
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| Batch size | 32 effective batch |
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| Optimizer | AdamW 8-bit |
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| Weight decay | 0.01 |
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| Precision | BF16 |
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| Max sequence length | 4096 |
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### Target Modules
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```python
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[
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"q_proj",
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"k_proj",
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"v_proj",
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"o_proj",
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"gate_proj",
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"up_proj",
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"down_proj",
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]
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```
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## Evaluation
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Evaluation was run on the held-out 5% split from the same dataset using BLEU and chrF++.
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| Metric | Score |
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|--------|-------|
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| BLEU | **21.93** |
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| chrF++ | **42.87** |
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| Eval loss | 0.7518 |
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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.
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## Sample Translations
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| English | Kikuyu |
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|---------|--------|
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| Hello, how are you? | Hihi, ũrĩ atĩa? |
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| The weather is beautiful today. | Rĩera nĩ rĩega mũno ũmũthĩ |
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| I love learning new languages. | Nĩ nyendete kwĩruta thiomi njerũ |
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## Intended Use
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- English -> Kikuyu translation tools
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- Kikuyu language learning applications
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- Low-resource African language NLP research
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- Cultural and linguistic preservation projects
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- Prototyping multilingual AI interfaces for Kikuyu speakers
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## Limitations
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- **Direction:** English -> Kikuyu only. Kikuyu -> English was not trained in this run.
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- **Language coverage:** Optimized for Kikuyu (`ki`), not other Gikuyu-related dialects or neighboring Bantu languages.
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- **Domain:** Best for general text. Technical, legal, medical, poetic, or highly idiomatic content may need human review.
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- **Evaluation:** BLEU and chrF++ do not fully measure naturalness, dialect fit, or cultural nuance.
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- **Production use:** Review outputs before high-stakes use.
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## Citation
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```bibtex
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@misc{gatere2026kikuyutranslategemma4bv7,
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author = {Mark Gatere},
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title = {Kikuyu TranslateGemma-4B V7: rsLoRA Fine-tuning for English to Kikuyu Translation},
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year = {2026},
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publisher = {Hugging Face},
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howpublished = {\url{https://huggingface.co/gateremark/kikuyu_translategemma_4b_v7_highrank_rslora}}
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}
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```
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## Acknowledgments
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- Google for [TranslateGemma-4B-it](https://huggingface.co/google/translategemma-4b-it)
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- [Unsloth](https://github.com/unslothai/unsloth) for efficient fine-tuning
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- Hugging Face for model and dataset hosting
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- Modal for GPU training and deployment infrastructure
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- Kikuyu speakers and reviewers supporting C-elo's translation work
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