File size: 4,912 Bytes
e7f82a3 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 | ---
library_name: peft
base_model: afriquellama_8b
language:
- ha
- en
tags:
- translation
- african-languages
- scientific-translation
- afriscience-mt
- lora
- peft
- llama
license: apache-2.0
pipeline_tag: translation
---
# afriquellama_8b-lora-r4-hau-eng
[](https://huggingface.co/AfriScience-MT/afriquellama_8b-lora-r4-hau-eng)
This is a **LoRA adapter** for the AfriScience-MT project, enabling efficient scientific machine translation for African languages.
## Adapter Description
| Property | Value |
|----------|-------|
| **Base Model** | [afriquellama_8b](https://huggingface.co/afriquellama_8b) |
| **Translation Direction** | Hausa → English |
| **LoRA Rank (r)** | 4 |
| **LoRA Alpha** | 8 |
| **Training Method** | QLoRA (4-bit quantization) |
| **Domain** | Scientific/Academic texts |
### Why LoRA?
LoRA (Low-Rank Adaptation) enables efficient fine-tuning by training only a small number of additional parameters. This adapter adds only **~2.0M parameters** to the base model while achieving strong translation performance.
## Usage
### Quick Start
```python
from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig
from peft import PeftModel
import torch
# Configure 4-bit quantization (recommended for memory efficiency)
bnb_config = BitsAndBytesConfig(
load_in_4bit=True,
bnb_4bit_compute_dtype=torch.bfloat16,
bnb_4bit_quant_type="nf4",
bnb_4bit_use_double_quant=True,
)
# Load base model
base_model = AutoModelForCausalLM.from_pretrained(
"afriquellama_8b",
quantization_config=bnb_config,
device_map="auto",
torch_dtype=torch.bfloat16,
)
tokenizer = AutoTokenizer.from_pretrained("afriquellama_8b")
# Load LoRA adapter
adapter_name = "AfriScience-MT/afriquellama_8b-lora-r4-hau-eng"
model = PeftModel.from_pretrained(base_model, adapter_name)
model.eval()
# Prepare translation prompt
source_text = "Climate change significantly impacts agricultural productivity in sub-Saharan Africa."
instruction = "Translate the following Hausa scientific text to English."
# Format prompt
prompt = f"""### Instruction:
{instruction}
### Input:
{source_text}
### Response:
"""
# Generate translation
inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
with torch.no_grad():
outputs = model.generate(
**inputs,
max_new_tokens=256,
num_beams=5,
early_stopping=True,
pad_token_id=tokenizer.pad_token_id,
)
# Decode only the generated part
generated = outputs[0][inputs["input_ids"].shape[1]:]
translation = tokenizer.decode(generated, skip_special_tokens=True)
print(translation)
```
### Without Quantization (Full Precision)
```python
# For GPUs with sufficient memory (>24GB for larger models)
base_model = AutoModelForCausalLM.from_pretrained(
"afriquellama_8b",
device_map="auto",
torch_dtype=torch.bfloat16,
)
model = PeftModel.from_pretrained(base_model, "AfriScience-MT/afriquellama_8b-lora-r4-hau-eng")
```
### Hardware Requirements
| Configuration | VRAM Required |
|---------------|---------------|
| 4-bit (QLoRA) | ~8-12 GB |
| 8-bit | ~16-20 GB |
| Full precision | ~24-40 GB |
## Reproducibility
To reproduce this adapter:
```bash
# Clone the AfriScience-MT repository
git clone https://github.com/afriscience-mt/afriscience-mt.git
cd afriscience-mt
# Install dependencies
pip install -r requirements.txt
# Run LoRA training
python -m afriscience_mt.scripts.run_lora_training \
--data_dir ./data \
--source_lang hau \
--target_lang eng \
--model_name afriquellama_8b \
--model_type llama \
--lora_rank 4 \
--output_dir ./output \
--num_epochs 3 \
--batch_size 4 \
--load_in_4bit
```
## Limitations
- **Domain Specificity**: Optimized for scientific/academic texts; may underperform on casual or colloquial language.
- **Language Direction**: Only supports Hausa → English translation.
- **Base Model Required**: Must be used with the [afriquellama_8b](https://huggingface.co/afriquellama_8b) base model.
- **Context Length**: Maximum context is model-dependent; longer texts should be chunked.
## Citation
If you use this adapter, please cite the AfriScience-MT project:
```bibtex
@inproceedings{afriscience-mt-2025,
title={AfriScience-MT: Machine Translation for African Scientific Literature},
author={AfriScience-MT Team},
year={2025},
url={https://github.com/afriscience-mt/afriscience-mt}
}
```
## License
This adapter is released under the [Apache 2.0 License](https://www.apache.org/licenses/LICENSE-2.0).
## Acknowledgments
- Base model: [afriquellama_8b](https://huggingface.co/afriquellama_8b)
- LoRA implementation: [PEFT](https://github.com/huggingface/peft)
- Evaluation: [SSA-COMET](https://huggingface.co/McGill-NLP/ssa-comet-stl) for African language assessment
|