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AfriScience-MT / llama_3.1_8b_instruct-lora-r64-amh-eng
Model's Last Updated: April 10 2026
translation
Introduction of llama_3.1_8b_instruct-lora-r64-amh-eng
Model Details of llama_3.1_8b_instruct-lora-r64-amh-eng
llama_3.1_8b_instruct-lora-r64-amh-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 | meta-llama/Llama-3.1-8B-Instruct |
| Translation Direction | Amharic → English |
| LoRA Rank (r) | 64 |
| LoRA Alpha | 128 |
| 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 ~32.0M parameters to the base model while achieving strong translation performance.
Evaluation Results
Performance on the AfriScience-MT test set:
| Split | BLEU | chrF | SSA-COMET |
|---|---|---|---|
| Test | - | - | - |
Metrics explanation:
- BLEU : Measures n-gram overlap with reference translations (0-100, higher is better)
- chrF : Character-level F-score, robust for morphologically rich languages (0-100, higher is better)
- SSA-COMET : Neural metric trained for Sub-Saharan African languages, shown as percentage (0-100, higher is better) ( McGill-NLP/ssa-comet-stl )
Usage
Quick Start
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(
"meta-llama/Llama-3.1-8B-Instruct",
quantization_config=bnb_config,
device_map="auto",
torch_dtype=torch.bfloat16,
)
tokenizer = AutoTokenizer.from_pretrained("meta-llama/Llama-3.1-8B-Instruct")
# Load LoRA adapter
adapter_name = "AfriScience-MT/llama_3.1_8b_instruct-lora-r64-amh-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 Amharic 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)
# For GPUs with sufficient memory (>24GB for larger models)
base_model = AutoModelForCausalLM.from_pretrained(
"meta-llama/Llama-3.1-8B-Instruct",
device_map="auto",
torch_dtype=torch.bfloat16,
)
model = PeftModel.from_pretrained(base_model, "AfriScience-MT/llama_3.1_8b_instruct-lora-r64-amh-eng")
Training Details
Hyperparameters
| Parameter | Value |
|---|---|
| LoRA Rank (r) | 64 |
| LoRA Alpha | 128 |
| LoRA Dropout | 0.05 |
| Target Modules | q_proj, k_proj, v_proj, o_proj, gate_proj, up_proj, down_proj |
| Epochs | 3 |
| Batch Size | 2 |
| Learning Rate | 2e-04 |
| Max Sequence Length | 512 |
| Gradient Accumulation | 4 |
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:
# 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 amh \
--target_lang eng \
--model_name meta-llama/Llama-3.1-8B-Instruct \
--model_type llama \
--lora_rank 64 \
--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 Amharic → English translation.
- Base Model Required : Must be used with the meta-llama/Llama-3.1-8B-Instruct 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:
@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 .
Acknowledgments
- Base model: meta-llama/Llama-3.1-8B-Instruct
- LoRA implementation: PEFT
- Evaluation: SSA-COMET for African language assessment
Runs of AfriScience-MT llama_3.1_8b_instruct-lora-r64-amh-eng on huggingface.co
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Other API from AfriScience-MT
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