---
license: mit
language:
- en
- fr
pipeline_tag: translation
tags:
- translation
- lora
- peft
- finetuning
- tinyllama
- opus-100
base_model:
- TinyLlama/TinyLlama-1.1B-intermediate-step-1431k-3T
datasets:
- Helsinki-NLP/opus-100
---

# EN–FR Translation LoRA on TinyLlama-1.1B

A lightweight LoRA adapter fine-tuned on the **TinyLlama-1.1B** base model for ** **English → French translation****  (binary classification: Positive or Negative).

Trained with **supervised fine-tuning (SFT)** on only **8,000 examples** from the Hugging Face **Helsinki-NLP/opus-100**  dataset, formatted as instruction prompts. Despite the limited data, it achieves solid performance with very low memory usage and fast inference.

## Model Details

- **Base Model**: `TinyLlama/TinyLlama-1.1B-intermediate-step-1431k-3T`
- **LoRA Configuration**:
  - Rank (`r`): 8
  - Scaling (`lora_alpha`): 16
  - Target modules: `["q_proj", "v_proj"]`
  - Dropout: 0.05
  - Bias: "none"
  - Task type: `CAUSAL_LM`
- **Training Data**: 8,000 labeled samples from **Helsinki-NLP/opus-100** (balanced)
- **Training Method**: Instruction-tuned SFT using PEFT + TRL

## Usage Example 



```python
from transformers import AutoTokenizer, AutoModelForCausalLM
from peft import PeftModel
import torch

# 定义基础模型和 LoRA 模型仓库
base_model_name = "TinyLlama/TinyLlama-1.1B-intermediate-step-1431k-3T"
repo_id_translation = "BEncoderRT/EN-FR-Translation-LoRA-TinyLlama-1.1B"  # 请确认此 repo 是否存在，若不存在请替换为正确 ID 或本地路径

# 加载 tokenizer
tokenizer = AutoTokenizer.from_pretrained(base_model_name)
if tokenizer.pad_token is None:
    tokenizer.pad_token = tokenizer.eos_token

# 加载基础模型（仅加载一次）
base_model = AutoModelForCausalLM.from_pretrained(
    base_model_name,
    device_map="auto",  # 自动分配到 GPU/CPU
    torch_dtype=torch.float16  # 推荐使用 half precision 节省显存
)

# 单独加载 translation LoRA adapter
translation_model = PeftModel.from_pretrained(
    base_model,
    repo_id_translation,
    adapter_name="translation"  # 可选，默认为 default
)

translation_model.eval()  # 设置为评估模式
print("Translation LoRA 模型加载完成。")

# 推理函数（仅针对 translation 任务：English to French）
def translation_inference(model, tokenizer, english_text, max_new_tokens=100):
    # 设置 adapter（如果有多个，这里确保使用 translation）
    if hasattr(model, "set_adapter"):
        model.set_adapter("translation")
    
    # 构造 prompt（根据原多任务代码的格式）
    formatted_prompt = (
        "### Task: Translation (English to French)\n"
        "### English:\n"
        f"{english_text}\n"
        "### French:\n"
    )
    
    inputs = tokenizer(formatted_prompt, return_tensors="pt", truncation=True, max_length=512).to(model.device)
    
    with torch.no_grad():
        outputs = model.generate(
            **inputs,
            max_new_tokens=max_new_tokens,
            do_sample=True,
            temperature=0.7,
            top_k=50,
            top_p=0.95,
            eos_token_id=tokenizer.eos_token_id,
            pad_token_id=tokenizer.pad_token_id
        )
    
    generated_text = tokenizer.decode(outputs[0], skip_special_tokens=True)
    
    # 提取翻译结果
    answer_start = generated_text.find("### French:\n")
    if answer_start != -1:
        extracted = generated_text[answer_start + len("### French:\n"):].strip()
        # 去除可能的后续内容（如另一个 ###）
        end_index = extracted.find("###")
        if end_index != -1:
            extracted = extracted[:end_index].strip()
        return extracted
    
    return generated_text  # Fallback：返回完整生成文本

# --- 测试用例 ---
print("\n测试 English to French Translation:")

english_1 = "The quick brown fox jumps over the lazy dog."
print(f"English: {english_1}")
print(f"French: {translation_inference(translation_model, tokenizer, english_1)}\n")

english_2 = "Life is beautiful."
print(f"English: {english_2}")
print(f"French: {translation_inference(translation_model, tokenizer, english_2)}\n")

english_3 = "Hello, how are you today? I hope everything is going well."
print(f"English: {english_3}")
print(f"French: {translation_inference(translation_model, tokenizer, english_3)}\n")

english_4 = "Machine learning is a subset of artificial intelligence that focuses on the development of algorithms capable of learning from data."
print(f"English: {english_4}")
print(f"French: {translation_inference(translation_model, tokenizer, english_4)}")
```



```
Translation LoRA 模型加载完成。

测试 English to French Translation:
English: The quick brown fox jumps over the lazy dog.
French: Le chien sauvage et le chat fastueux s'adressent à un autre chat, qui ne voit rien.

English: Life is beautiful.
French: La vie est belle.

English: Hello, how are you today? I hope everything is going well.
French: Bonjour, comment ça va aujourd'hui ?

English: Machine learning is a subset of artificial intelligence that focuses on the development of algorithms capable of learning from data.
French: Le machine learning est un sous-ensemble de l'intelligence artificielle qui s'intéresse au développement d'algorithmes capables de se former en apprenant les données.
```