Update README.md

README.md

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----
-license: mit
----
+---
+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.
+```
+
+
+
+
+# Model Card for Model ID
+
+<!-- Provide a quick summary of what the model is/does. -->
+
+This modelcard aims to be a base template for new models. It has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/modelcard_template.md?plain=1).
+
+## Model Details
+
+### Model Description
+
+<!-- Provide a longer summary of what this model is. -->
+
+
+
+- **Developed by:** [More Information Needed]
+- **Funded by [optional]:** [More Information Needed]
+- **Shared by [optional]:** [More Information Needed]
+- **Model type:** [More Information Needed]
+- **Language(s) (NLP):** [More Information Needed]
+- **License:** [More Information Needed]
+- **Finetuned from model [optional]:** [More Information Needed]
+
+### Model Sources [optional]
+
+<!-- Provide the basic links for the model. -->
+
+- **Repository:** [More Information Needed]
+- **Paper [optional]:** [More Information Needed]
+- **Demo [optional]:** [More Information Needed]
+
+## Uses
+
+<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
+
+### Direct Use
+
+<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
+
+[More Information Needed]
+
+### Downstream Use [optional]
+
+<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
+
+[More Information Needed]
+
+### Out-of-Scope Use
+
+<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
+
+[More Information Needed]
+
+## Bias, Risks, and Limitations
+
+<!-- This section is meant to convey both technical and sociotechnical limitations. -->
+
+[More Information Needed]
+
+### Recommendations
+
+<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
+
+Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
+
+## How to Get Started with the Model
+
+Use the code below to get started with the model.
+
+[More Information Needed]
+
+## Training Details
+
+### Training Data
+
+<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
+
+[More Information Needed]
+
+### Training Procedure
+
+<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
+
+#### Preprocessing [optional]
+
+[More Information Needed]
+
+
+#### Training Hyperparameters
+
+- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
+
+#### Speeds, Sizes, Times [optional]
+
+<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
+
+[More Information Needed]
+
+## Evaluation
+
+<!-- This section describes the evaluation protocols and provides the results. -->
+
+### Testing Data, Factors & Metrics
+
+#### Testing Data
+
+<!-- This should link to a Dataset Card if possible. -->
+
+[More Information Needed]
+
+#### Factors
+
+<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
+
+[More Information Needed]
+
+#### Metrics
+
+<!-- These are the evaluation metrics being used, ideally with a description of why. -->
+
+[More Information Needed]
+
+### Results
+
+[More Information Needed]
+
+#### Summary
+
+
+
+## Model Examination [optional]
+
+<!-- Relevant interpretability work for the model goes here -->
+
+[More Information Needed]
+
+## Environmental Impact
+
+<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
+
+Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
+
+- **Hardware Type:** [More Information Needed]
+- **Hours used:** [More Information Needed]
+- **Cloud Provider:** [More Information Needed]
+- **Compute Region:** [More Information Needed]
+- **Carbon Emitted:** [More Information Needed]
+
+## Technical Specifications [optional]
+
+### Model Architecture and Objective
+
+[More Information Needed]
+
+### Compute Infrastructure
+
+[More Information Needed]
+
+#### Hardware
+
+[More Information Needed]
+
+#### Software
+
+[More Information Needed]
+
+## Citation [optional]
+
+<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
+
+**BibTeX:**
+
+[More Information Needed]
+
+**APA:**
+
+[More Information Needed]
+
+## Glossary [optional]
+
+<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
+
+[More Information Needed]
+
+## More Information [optional]
+
+[More Information Needed]
+
+## Model Card Authors [optional]
+
+[More Information Needed]
+
+## Model Card Contact
+
+[More Information Needed]