init

README.md

@@ -11,4 +11,68 @@ license: mit
 short_description: apply_lora_and_quantize
 ---
 
-An example chatbot using [Gradio](https://gradio.app), [`huggingface_hub`](https://huggingface.co/docs/huggingface_hub/v0.22.2/en/index), and the [Hugging Face Inference API](https://huggingface.co/docs/api-inference/index).
+An example chatbot using [Gradio](https://gradio.app), [`huggingface_hub`](https://huggingface.co/docs/huggingface_hub/v0.22.2/en/index), and the [Hugging Face Inference API](https://huggingface.co/docs/api-inference/index).
+
+# Model Converter for HuggingFace
+
+A powerful tool for converting and quantizing Large Language Models (LLMs) with LoRA adapters.
+
+## Features
+
+- 🚀 Automatic system resource detection (CPU/GPU)
+- 🔄 Merge base models with LoRA adapters
+- 📊 Support for 4-bit and 8-bit quantization
+- ☁️ Automatic upload to HuggingFace Hub
+
+## Requirements
+
+- Python 3.8+
+- CUDA compatible GPU (optional, but recommended)
+- HuggingFace account and token
+
+## Installation
+
+```bash
+pip install -r requirements.txt
+```
+
+## Configuration
+
+Create a `.env` file in the project root:
+```
+HF_TOKEN=your_huggingface_token
+```
+
+## Usage
+
+Run the script:
+```bash
+python space_convert.py
+```
+
+You will be prompted to enter:
+1. Base model path (e.g., "Qwen/Qwen2.5-7B-Instruct")
+2. LoRA model path
+3. Target HuggingFace repository name
+
+The script will:
+1. Check available system resources
+2. Choose the optimal device (GPU/CPU)
+3. Merge the base model with LoRA
+4. Create 8-bit and 4-bit quantized versions
+5. Upload everything to HuggingFace
+
+## Memory Requirements
+
+- 7B models: ~16GB RAM/VRAM
+- 14B models: ~32GB RAM/VRAM
+- Additional disk space: 3x model size
+
+## Note
+
+The script automatically handles:
+- Resource availability checks
+- Device selection
+- Error handling
+- Progress tracking
+- Model optimization

app.py

@@ -1,64 +1,294 @@
+import os
+import torch
+import psutil
+from transformers import AutoTokenizer, AutoModelForCausalLM, AutoConfig
+from peft import PeftModel, PeftConfig
+from pathlib import Path
+from tqdm import tqdm
+from huggingface_hub import login, create_repo, HfApi
+import subprocess
+import math
+from dotenv import load_dotenv
 import gradio as gr
-from huggingface_hub import InferenceClient
+import threading
+import queue
+import time
 
-"""
-For more information on `huggingface_hub` Inference API support, please check the docs: https://huggingface.co/docs/huggingface_hub/v0.22.2/en/guides/inference
-"""
-client = InferenceClient("HuggingFaceH4/zephyr-7b-beta")
+# 创建一个队列用于存储日志消息
+log_queue = queue.Queue()
+current_logs = []
 
+def log(msg):
+    """统一的日志处理函数"""
+    print(msg)
+    current_logs.append(msg)
+    return "\n".join(current_logs)
 
-def respond(
-    message,
-    history: list[tuple[str, str]],
-    system_message,
-    max_tokens,
-    temperature,
-    top_p,
-):
-    messages = [{"role": "system", "content": system_message}]
+def get_model_size_in_gb(model_name):
+    """估算模型大小（以GB为单位）"""
+    try:
+        config = AutoConfig.from_pretrained(model_name)
+        num_params = config.num_parameters if hasattr(config, 'num_parameters') else None
+        
+        if num_params is None:
+            # 手动计算参数量
+            if hasattr(config, 'num_hidden_layers') and hasattr(config, 'hidden_size'):
+                # 简单估算，可能不够准确
+                num_params = config.num_hidden_layers * config.hidden_size * config.hidden_size * 4
+        
+        if num_params:
+            # 每个参数占用2字节（float16）
+            size_in_gb = (num_params * 2) / (1024 ** 3)
+            return size_in_gb
+        else:
+            # 如果无法计算，返回一个保守的估计
+            return 16  # 默认假设是7B模型
+    except Exception as e:
+        log(f"无法估算模型大小: {str(e)}")
+        return 16  # 默认返回16GB
 
-    for val in history:
-        if val[0]:
-            messages.append({"role": "user", "content": val[0]})
-        if val[1]:
-            messages.append({"role": "assistant", "content": val[1]})
+def check_system_resources(model_name):
+    """检查系统资源并决定使用什么设备"""
+    log("正在检查系统资源...")
+    
+    # 获取系统内存信息
+    system_memory = psutil.virtual_memory()
+    total_memory_gb = system_memory.total / (1024 ** 3)
+    available_memory_gb = system_memory.available / (1024 ** 3)
+    
+    log(f"系统总内存: {total_memory_gb:.1f}GB")
+    log(f"可用内存: {available_memory_gb:.1f}GB")
+    
+    # 估算模型所需内存
+    model_size_gb = get_model_size_in_gb(model_name)
+    required_memory_gb = model_size_gb * 2.5  # 需要额外的内存用于计算
+    log(f"估计模型需要内存: {required_memory_gb:.1f}GB")
+    
+    # 检查CUDA是否可用
+    if torch.cuda.is_available():
+        gpu_name = torch.cuda.get_device_name(0)
+        gpu_memory_gb = torch.cuda.get_device_properties(0).total_memory / (1024 ** 3)
+        log(f"发现GPU: {gpu_name}")
+        log(f"GPU显存: {gpu_memory_gb:.1f}GB")
+        
+        if gpu_memory_gb >= required_memory_gb:
+            log("✅ GPU显存足够，将使用GPU进行转换")
+            return "cuda", gpu_memory_gb
+        else:
+            log(f"⚠️ GPU显存不足 (需要 {required_memory_gb:.1f}GB, 实际 {gpu_memory_gb:.1f}GB)")
+    else:
+        log("❌ 未检测到可用的GPU")
+    
+    # 检查CPU内存是否足够
+    if available_memory_gb >= required_memory_gb:
+        log("✅ CPU内存足够，将使用CPU进行转换")
+        return "cpu", available_memory_gb
+    else:
+        raise MemoryError(f"❌ 系统内存不足 (需要 {required_memory_gb:.1f}GB, 可用 {available_memory_gb:.1f}GB)")
 
-    messages.append({"role": "user", "content": message})
+def setup_environment(model_name):
+    """设置环境并返回设备信息"""
+    load_dotenv()
+    hf_token = os.getenv('HF_TOKEN')
+    if not hf_token:
+        raise ValueError("请在环境变量中设置HF_TOKEN")
+    login(hf_token)
+    
+    # 检查系统资源并决定使用什么设备
+    device, available_memory = check_system_resources(model_name)
+    return device
 
-    response = ""
+def create_hf_repo(repo_name, private=True):
+    """创建HuggingFace仓库"""
+    try:
+        repo_url = create_repo(repo_name, private=private)
+        log(f"创建仓库成功: {repo_url}")
+        return repo_url
+    except Exception as e:
+        log(f"创建仓库失败: {str(e)}")
+        raise
 
-    for message in client.chat_completion(
-        messages,
-        max_tokens=max_tokens,
-        stream=True,
-        temperature=temperature,
-        top_p=top_p,
-    ):
-        token = message.choices[0].delta.content
+def download_and_merge_model(base_model_name, lora_model_name, output_dir, device):
+    log(f"正在加��基础模型: {base_model_name}")
+    
+    try:
+        # 先加载原始模型
+        base_model = AutoModelForCausalLM.from_pretrained(
+            base_model_name,
+            torch_dtype=torch.float16,
+            device_map={"": device}
+        )
+        
+        # 加载tokenizer
+        tokenizer = AutoTokenizer.from_pretrained(base_model_name)
+        
+        log(f"正在加载LoRA模型: {lora_model_name}")
+        log("基础模型配置:" + str(base_model.config))
+        
+        # 加载adapter配置
+        adapter_config = PeftConfig.from_pretrained(lora_model_name)
+        log("Adapter配置:" + str(adapter_config))
+        
+        model = PeftModel.from_pretrained(base_model, lora_model_name)
+        log("正在合并LoRA权重")
+        model = model.merge_and_unload()
 
-        response += token
-        yield response
+        # 创建输出目录
+        output_path = Path(output_dir)
+        output_path.mkdir(parents=True, exist_ok=True)
 
+        # 保存合并后的模型
+        log(f"正在保存合并后的模型到: {output_dir}")
+        model.save_pretrained(output_dir)
+        tokenizer.save_pretrained(output_dir)
+        
+        return output_dir
+        
+    except Exception as e:
+        log(f"错误: {str(e)}")
+        log(f"错误类型: {type(e)}")
+        import traceback
+        log("详细错误信息:")
+        log(traceback.format_exc())
+        raise
 
-"""
-For information on how to customize the ChatInterface, peruse the gradio docs: https://www.gradio.app/docs/chatinterface
-"""
-demo = gr.ChatInterface(
-    respond,
-    additional_inputs=[
-        gr.Textbox(value="You are a friendly Chatbot.", label="System message"),
-        gr.Slider(minimum=1, maximum=2048, value=512, step=1, label="Max new tokens"),
-        gr.Slider(minimum=0.1, maximum=4.0, value=0.7, step=0.1, label="Temperature"),
-        gr.Slider(
-            minimum=0.1,
-            maximum=1.0,
-            value=0.95,
-            step=0.05,
-            label="Top-p (nucleus sampling)",
-        ),
-    ],
-)
+def quantize_and_push_model(model_path, repo_id, bits=8):
+    """量化模型并推送到HuggingFace"""
+    try:
+        from optimum.bettertransformer import BetterTransformer
+        from transformers import AutoModelForCausalLM
+        
+        log(f"正在加载模型用于{bits}位量化...")
+        model = AutoModelForCausalLM.from_pretrained(model_path)
+        tokenizer = AutoTokenizer.from_pretrained(model_path)
+        
+        # 转换为BetterTransformer格式
+        model = BetterTransformer.transform(model)
+        
+        # 量化
+        if bits == 8:
+            from transformers import BitsAndBytesConfig
+            quantization_config = BitsAndBytesConfig(
+                load_in_8bit=True,
+                llm_int8_threshold=6.0
+            )
+        elif bits == 4:
+            from transformers import BitsAndBytesConfig
+            quantization_config = BitsAndBytesConfig(
+                load_in_4bit=True,
+                bnb_4bit_compute_dtype=torch.float16,
+                bnb_4bit_quant_type="nf4"
+            )
+        else:
+            raise ValueError(f"不支持的量化位数: {bits}")
+        
+        # 保存量化后的模型
+        quantized_model_path = f"{model_path}_q{bits}"
+        model.save_pretrained(
+            quantized_model_path,
+            quantization_config=quantization_config
+        )
+        tokenizer.save_pretrained(quantized_model_path)
+        
+        # 推送到HuggingFace
+        log(f"正在将{bits}位量化模型推送到HuggingFace...")
+        api = HfApi()
+        api.upload_folder(
+            folder_path=quantized_model_path,
+            repo_id=repo_id,
+            repo_type="model"
+        )
+        log(f"{bits}位量化模型上传完成")
+        
+    except Exception as e:
+        log(f"量化或上传过程中出错: {str(e)}")
+        raise
 
+def process_model(base_model, lora_model, repo_name, progress=gr.Progress()):
+    """处理模型的主函数，用于Gradio界面"""
+    try:
+        # 清空之前的日志
+        current_logs.clear()
+        
+        # 设置环境和检查资源
+        device = setup_environment(base_model)
+        
+        # 创建HuggingFace仓库
+        repo_url = create_hf_repo(repo_name)
+        
+        # 设置输出目录
+        output_dir = os.path.join(".", "output", repo_name)
+        
+        progress(0.1, desc="开始模型转换流程...")
+        # 下载并合并模型
+        model_path = download_and_merge_model(base_model, lora_model, output_dir, device)
+        
+        progress(0.4, desc="开始8位量化...")
+        # 量化并上传模型
+        quantize_and_push_model(model_path, repo_name, bits=8)
+        
+        progress(0.7, desc="开始4位量化...")
+        quantize_and_push_model(model_path, repo_name, bits=4)
+        
+        final_message = f"全部完成！模型已上传至: https://huggingface.co/{repo_name}"
+        log(final_message)
+        progress(1.0, desc="处理完成")
+        
+        return "\n".join(current_logs)
+    except Exception as e:
+        error_message = f"处理过程中出错: {str(e)}"
+        log(error_message)
+        return "\n".join(current_logs)
+
+def create_ui():
+    """创建Gradio界面"""
+    with gr.Blocks(title="模型转换工具") as app:
+        gr.Markdown("""
+        # 🤗 模型转换与量化工具
+        
+        这个工具可以帮助你：
+        1. 合并基础模型和LoRA适配器
+        2. 创建4位和8位量化版本
+        3. 自动上传到HuggingFace Hub
+        """)
+        
+        with gr.Row():
+            with gr.Column():
+                base_model = gr.Textbox(
+                    label="基础模型路径",
+                    placeholder="例如: Qwen/Qwen2.5-7B-Instruct",
+                    value="Qwen/Qwen2.5-7B-Instruct"
+                )
+                lora_model = gr.Textbox(
+                    label="LoRA模型路径",
+                    placeholder="输入你的LoRA模型路径"
+                )
+                repo_name = gr.Textbox(
+                    label="HuggingFace仓库名称",
+                    placeholder="输入要创建的仓库名称"
+                )
+                convert_btn = gr.Button("开始转换", variant="primary")
+            
+            with gr.Column():
+                output = gr.TextArea(
+                    label="处理日志",
+                    placeholder="处理日志将在这里显示...",
+                    interactive=False,
+                    autoscroll=True,
+                    lines=20
+                )
+        
+        # 设置事件处理
+        convert_btn.click(
+            fn=process_model,
+            inputs=[base_model, lora_model, repo_name],
+            outputs=output
+        )
+    
+    return app
 
 if __name__ == "__main__":
-    demo.launch()
+    # 创建并启动Gradio界面
+    app = create_ui()
+    app.queue()
+    app.launch()

app.py.bk

@@ -0,0 +1,64 @@
+import gradio as gr
+from huggingface_hub import InferenceClient
+
+"""
+For more information on `huggingface_hub` Inference API support, please check the docs: https://huggingface.co/docs/huggingface_hub/v0.22.2/en/guides/inference
+"""
+client = InferenceClient("HuggingFaceH4/zephyr-7b-beta")
+
+
+def respond(
+    message,
+    history: list[tuple[str, str]],
+    system_message,
+    max_tokens,
+    temperature,
+    top_p,
+):
+    messages = [{"role": "system", "content": system_message}]
+
+    for val in history:
+        if val[0]:
+            messages.append({"role": "user", "content": val[0]})
+        if val[1]:
+            messages.append({"role": "assistant", "content": val[1]})
+
+    messages.append({"role": "user", "content": message})
+
+    response = ""
+
+    for message in client.chat_completion(
+        messages,
+        max_tokens=max_tokens,
+        stream=True,
+        temperature=temperature,
+        top_p=top_p,
+    ):
+        token = message.choices[0].delta.content
+
+        response += token
+        yield response
+
+
+"""
+For information on how to customize the ChatInterface, peruse the gradio docs: https://www.gradio.app/docs/chatinterface
+"""
+demo = gr.ChatInterface(
+    respond,
+    additional_inputs=[
+        gr.Textbox(value="You are a friendly Chatbot.", label="System message"),
+        gr.Slider(minimum=1, maximum=2048, value=512, step=1, label="Max new tokens"),
+        gr.Slider(minimum=0.1, maximum=4.0, value=0.7, step=0.1, label="Temperature"),
+        gr.Slider(
+            minimum=0.1,
+            maximum=1.0,
+            value=0.95,
+            step=0.05,
+            label="Top-p (nucleus sampling)",
+        ),
+    ],
+)
+
+
+if __name__ == "__main__":
+    demo.launch()

requirements.txt

@@ -1 +1,9 @@
-huggingface_hub==0.25.2
+huggingface_hub==0.25.2
+torch
+transformers
+peft
+huggingface_hub
+psutil
+tqdm
+python-dotenv
+gradio