Create Llama 3.1 8B fine-tuned chat space

README.md

@@ -1,12 +1,45 @@
 ---
-title: Robot Task Planning
-emoji: 🦀
-colorFrom: gray
-colorTo: indigo
+title: Robot Task Planning - Llama 3.1 8B
+emoji: 🤖
+colorFrom: blue
+colorTo: green
 sdk: gradio
-sdk_version: 5.34.2
+sdk_version: 4.44.0
 app_file: app.py
 pinned: false
+license: llama3.1
 ---
 
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+# Robot Task Planning - Llama 3.1 8B
+
+This Space demonstrates a fine-tuned version of Meta's Llama 3.1 8B model specialized for **robot task planning** using QLoRA technique.
+
+The model converts natural language commands into structured task sequences for construction robots like excavators and dump trucks.
+
+## Model
+
+The model is available at: [YongdongWang/llama-3.1-8b-dart-qlora](https://huggingface.co/YongdongWang/llama-3.1-8b-dart-qlora)
+
+## Features
+
+- **Robot Command Processing**: Convert natural language to structured robot tasks
+- **Multi-Robot Coordination**: Handle complex scenarios with multiple excavators and dump trucks  
+- **Task Dependencies**: Generate proper task sequences with dependencies
+- **Real-time Planning**: Instant task generation powered by Gradio
+
+## Usage
+
+Input natural language robot commands like "Deploy Excavator 1 to Soil Area 1" and the model will generate structured task sequences in JSON format for robot execution.
+
+## Technical Details
+
+- **Base Model**: meta-llama/Llama-3.1-8B
+- **Fine-tuning**: QLoRA (4-bit quantization + LoRA)
+- **Interface**: Gradio  
+- **Hosting**: HuggingFace Spaces
+- **Input**: Natural language robot commands
+- **Output**: Structured JSON task sequences
+
+## Performance
+
+⚠️ **Note**: Model loading may take 3-5 minutes on first startup due to the large model size and quantization process.

app.py

@@ -0,0 +1,218 @@
+import gradio as gr
+import torch
+from transformers import AutoTokenizer, AutoModelForCausalLM, BitsAndBytesConfig
+from peft import PeftModel
+import warnings
+warnings.filterwarnings("ignore")
+
+# 模型配置
+MODEL_NAME = "meta-llama/Llama-3.1-8B"
+LORA_MODEL = "YongdongWang/llama-3.1-8b-dart-qlora"
+
+def load_model():
+    """加载模型和分词器"""
+    print("🔄 Loading model...")
+    
+    try:
+        # 4位量化配置
+        bnb_config = BitsAndBytesConfig(
+            load_in_4bit=True,
+            bnb_4bit_quant_type="nf4",
+            bnb_4bit_compute_dtype=torch.float16,
+            bnb_4bit_use_double_quant=True,
+        )
+        
+        # 加载分词器
+        tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME, use_fast=False)
+        if tokenizer.pad_token is None:
+            tokenizer.pad_token = tokenizer.eos_token
+        
+        # 加载基础模型
+        base_model = AutoModelForCausalLM.from_pretrained(
+            MODEL_NAME,
+            quantization_config=bnb_config,
+            device_map="auto",
+            torch_dtype=torch.float16,
+            trust_remote_code=True
+        )
+        
+        # 加载 LoRA 适配器
+        model = PeftModel.from_pretrained(base_model, LORA_MODEL)
+        model.eval()
+        
+        print("✅ Model loaded successfully!")
+        return model, tokenizer
+        
+    except Exception as load_error:
+        print(f"❌ Model loading failed: {load_error}")
+        return None, None
+
+# 全局变量存储模型
+model = None
+tokenizer = None
+
+def initialize_model():
+    """初始化模型 - 延迟加载"""
+    global model, tokenizer
+    if model is None or tokenizer is None:
+        model, tokenizer = load_model()
+    return model is not None and tokenizer is not None
+
+def generate_response(prompt, max_tokens=200, temperature=0.7, top_p=0.9):
+    """生成回复"""
+    if not initialize_model():
+        return "❌ Model not loaded. Please check the logs or try again."
+    
+    try:
+        # 格式化输入 - 移除多余的字符串插值
+        formatted_prompt = prompt.strip()
+        
+        # 编码输入
+        inputs = tokenizer(formatted_prompt, return_tensors="pt").to(model.device)
+        
+        # 生成回复
+        with torch.no_grad():
+            outputs = model.generate(
+                **inputs,
+                max_new_tokens=max_tokens,
+                do_sample=True,
+                temperature=temperature,
+                top_p=top_p,
+                pad_token_id=tokenizer.pad_token_id,
+                eos_token_id=tokenizer.eos_token_id,
+                repetition_penalty=1.1,
+                early_stopping=True,
+            )
+        
+        # 解码输出
+        response = tokenizer.decode(outputs[0], skip_special_tokens=True)
+        
+        # 移除原始输入，只保留生成的部分
+        if len(response) > len(formatted_prompt):
+            response = response[len(formatted_prompt):].strip()
+        
+        # 如果回复包含特殊标记，进行清理
+        if "Assistant:" in response:
+            response = response.split("Assistant:")[-1].strip()
+        
+        return response if response else "❌ No response generated. Please try again."
+    
+    except Exception as generation_error:
+        return f"❌ Generation Error: {str(generation_error)}"
+
+def chat_interface(message, history, max_tokens, temperature, top_p):
+    """聊天界面"""
+    if not message.strip():
+        return history, ""
+    
+    try:
+        response = generate_response(message, max_tokens, temperature, top_p)
+        history.append((message, response))
+        return history, ""
+    except Exception as chat_error:
+        error_msg = f"❌ Chat Error: {str(chat_error)}"
+        history.append((message, error_msg))
+        return history, ""
+
+# 创建 Gradio 应用
+with gr.Blocks(title="Robot Task Planning - Llama 3.1 8B", theme=gr.themes.Soft()) as demo:
+    gr.Markdown("""
+    # 🤖 Llama 3.1 8B - Robot Task Planning
+    
+    This is a fine-tuned version of Meta's Llama 3.1 8B model specialized for **robot task planning** using QLoRA technique.
+    
+    **Capabilities**: Convert natural language robot commands into structured task sequences for excavators, dump trucks, and other construction robots.
+    
+    **Model**: [YongdongWang/llama-3.1-8b-dart-qlora](https://huggingface.co/YongdongWang/llama-3.1-8b-dart-qlora)
+    
+    ⚠️ **Note**: Model loading may take a few minutes on first startup.
+    """)
+    
+    with gr.Row():
+        with gr.Column(scale=3):
+            chatbot = gr.Chatbot(
+                label="Task Planning Results",
+                height=400,
+                show_label=True,
+                container=True,
+                bubble_full_width=False
+            )
+            
+            msg = gr.Textbox(
+                label="Robot Command",
+                placeholder="Enter robot task command (e.g., 'Deploy Excavator 1 to Soil Area 1')...",
+                lines=2,
+                max_lines=5,
+                show_label=True,
+                container=True
+            )
+            
+            with gr.Row():
+                send_btn = gr.Button("Generate Tasks", variant="primary", size="sm")
+                clear_btn = gr.Button("Clear", variant="secondary", size="sm")
+        
+        with gr.Column(scale=1):
+            gr.Markdown("### ⚙️ Generation Settings")
+            
+            max_tokens = gr.Slider(
+                minimum=50,
+                maximum=500,
+                value=200,
+                step=10,
+                label="Max Tokens",
+                info="Maximum number of tokens to generate"
+            )
+            
+            temperature = gr.Slider(
+                minimum=0.1,
+                maximum=2.0,
+                value=0.7,
+                step=0.1,
+                label="Temperature",
+                info="Controls randomness (lower = more focused)"
+            )
+            
+            top_p = gr.Slider(
+                minimum=0.1,
+                maximum=1.0,
+                value=0.9,
+                step=0.05,
+                label="Top-p",
+                info="Nucleus sampling threshold"
+            )
+    
+    # 示例对话
+    gr.Examples(
+        examples=[
+            ["Deploy Excavator 1 to Soil Area 1 for excavation."],
+            ["Send Dump Truck 1 to collect material, then unload at storage area."],
+            ["Move all robots to avoid Puddle 1 after inspection."],
+            ["Deploy multiple excavators to different soil areas simultaneously."],
+            ["Coordinate dump trucks to transport materials from excavation site to storage."],
+            ["Send robot to inspect rock area, then avoid with all other robots."],
+            ["Return all robots to start position after completing tasks."],
+        ],
+        inputs=msg,
+        label="💡 Example Robot Commands"
+    )
+    
+    # 事件处理
+    msg.submit(
+        chat_interface,
+        inputs=[msg, chatbot, max_tokens, temperature, top_p],
+        outputs=[chatbot, msg]
+    )
+    
+    send_btn.click(
+        chat_interface,
+        inputs=[msg, chatbot, max_tokens, temperature, top_p],
+        outputs=[chatbot, msg]
+    )
+    
+    clear_btn.click(
+        lambda: ([], ""),
+        outputs=[chatbot, msg]
+    )
+
+if __name__ == "__main__":
+    demo.launch()

requirements.txt

@@ -0,0 +1,7 @@
+gradio==4.44.0
+transformers==4.44.2
+torch==2.1.0
+peft==0.7.1
+bitsandbytes==0.41.3
+accelerate==0.24.1
+scipy==1.11.4