Update README.md

README.md

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----
-license: mit
----
+---
+license: mit
+language:
+- en
+base_model:
+- TinyLlama/TinyLlama-1.1B-intermediate-step-1431k-3T
+pipeline_tag: text-classification
+tags:
+- LORA
+- IMDB
+- Sentiment
+---
+# IMDB-Sentiment-LoRA-TinyLlama-1.1B
+
+A lightweight LoRA adapter fine-tuned on the **TinyLlama-1.1B** base model for **IMDB movie review sentiment analysis** (binary classification: Positive or Negative).
+
+Trained with **supervised fine-tuning (SFT)** on only **8,000 examples** from the Hugging Face `imdb` 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 IMDB (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_sentiment = "BEncoderRT/IMDB-Sentiment-LoRA-TinyLlama-1.1B"  # 请确认此 repo 是否存在，若不存在请替换为正确 ID
+
+
+tokenizer = AutoTokenizer.from_pretrained(base_model_name)
+if tokenizer.pad_token is None:
+    tokenizer.pad_token = tokenizer.eos_token
+
+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 sentiment LoRA adapter
+sentiment_model = PeftModel.from_pretrained(
+    base_model,
+    repo_id_sentiment,
+    adapter_name="sentiment"  # 可选，默认为 default
+)
+
+sentiment_model.eval()  # 设置为评估模式
+print("Sentiment LoRA 模型加载完成。")
+
+# 推理函数（仅针对 sentiment 任务）
+def sentiment_inference(model, tokenizer, review_text, max_new_tokens=50):
+    # 设置 adapter（如果有多个，这里确保使用 sentiment）
+    if hasattr(model, "set_adapter"):
+        model.set_adapter("sentiment")
+    
+    # 构造 prompt
+    formatted_prompt = (
+        "### Task: Sentiment Analysis\n"
+        "### Review:\n"
+        f"{review_text}\n"
+        "### Answer:\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)
+    
+    # 提取答案
+    answer_start = generated_text.find("### Answer:\n")
+    if answer_start != -1:
+        extracted = generated_text[answer_start + len("### Answer:\n"):].strip()
+        # 简单判断 positive/negative
+        if 'positive' in extracted.lower():
+            return 'positive'
+        elif 'negative' in extracted.lower():
+            return 'negative'
+        return extracted.split('\n')[0].strip()
+    
+    return generated_text
+
+# --- 测试用例 ---
+print("\n测试 Sentiment Analysis:")
+
+positive_review = "This movie was absolutely fantastic! The acting was superb and the story was captivating."
+print(f"Review: {positive_review}")
+print(f"Sentiment: {sentiment_inference(sentiment_model, tokenizer, positive_review)}\n")
+
+negative_review = "I found this film to be incredibly boring and predictable. A complete waste of time."
+print(f"Review: {negative_review}")
+print(f"Sentiment: {sentiment_inference(sentiment_model, tokenizer, negative_review)}\n")
+
+another_review = "An okay movie, nothing special but not bad either."
+print(f"Review: {another_review}")
+print(f"Sentiment: {sentiment_inference(sentiment_model, tokenizer, another_review)}")
+```
+
+```
+Sentiment LoRA 模型加载完成。
+
+测试 Sentiment Analysis:
+Review: This movie was absolutely fantastic! The acting was superb and the story was captivating.
+Sentiment: positive
+
+Review: I found this film to be incredibly boring and predictable. A complete waste of time.
+Sentiment: negative
+
+Review: An okay movie, nothing special but not bad either.
+Sentiment: positive
+```
+