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README.md

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----
-license: apache-2.0
----
+# Qwen2.5-Coder-7B-LoRA-Law
+
+This repository contains trained weights for a fine-tuned version of the Qwen2.5-Coder-7B-Instruct model, specialized for answering legal questions in Chinese.  The model is fine-tuned using LoRA on the DISC-Law-SFT dataset.
+
+## Model Description
+
+This model is intended to provide informative and comprehensive answers to legal questions posed in Chinese.  
+
+## Training Details
+
+* **Base Model:** Qwen/Qwen2.5-Coder-7B-Instruct
+* **Dataset:** [DISC-Law-SFT](https://huggingface.co/datasets/ShengbinYue/DISC-Law-SFT), specifically the `DISC-Law-SFT-Pair-QA-released.jsonl` subset.
+* **Fine-tuning Method:** LoRA
+* **LoRA Parameters:**
+    * `r`: 64
+    * `lora_alpha`: 16
+    * `lora_dropout`: 0.1
+* **Training Hyperparameters:**
+    * `per_device_train_batch_size`: 4
+    * `gradient_accumulation_steps`: 8
+    * `learning_rate`: 2e-5
+    * `num_train_epochs`: 2
+* **System Prompt:** 你是一个法律专家，请根据用户的问题给出专业的回答
+
+
+## How to Merge Lora weights
+
+
+```python
+from peft import PeftModel
+from transformers import AutoModelForCausalLM
+import torch
+import os
+from modelscope import AutoTokenizer
+import shutil
+
+# 保证原始模型的各个文件不遗漏保存到merge_path中
+def copy_files_not_in_B(A_path, B_path):
+    """
+    Copies files from directory A to directory B if they exist in A but not in B.
+
+    :param A_path: Path to the source directory (A).
+    :param B_path: Path to the destination directory (B).
+    """
+    # 保证路径存在
+    if not os.path.exists(A_path):
+        raise FileNotFoundError(f"The directory {A_path} does not exist.")
+    if not os.path.exists(B_path):
+        os.makedirs(B_path)
+
+    # 获取路径A中所有非权重文件
+    files_in_A = os.listdir(A_path)
+    files_in_A = set([file for file in files_in_A if not (".bin" in file or "safetensors" in file)])
+    # List all files in directory B
+    files_in_B = set(os.listdir(B_path))
+
+    # 找到所有A中存在但B中不存在的文件
+    files_to_copy = files_in_A - files_in_B
+
+    # 将文件或文件夹复制到B路径下
+    for file in files_to_copy:
+        src_path = os.path.join(A_path, file)
+        dst_path = os.path.join(B_path, file)
+
+        if os.path.isdir(src_path):
+            # 复制目录及其内容
+            shutil.copytree(src_path, dst_path)
+        else:
+            # 复制文件
+            shutil.copy2(src_path, dst_path)
+
+def merge_lora_to_base_model():
+    model_name_or_path = '...'  # 原模型地址
+    adapter_name_or_path = '...'  # 微调后模型的保存地址
+    save_path = '...'
+
+    # 如果文件夹不存在，就创建
+    if not os.path.exists(save_path):
+        os.makedirs(save_path)
+    tokenizer = AutoTokenizer.from_pretrained(model_name_or_path,trust_remote_code=True,)
+
+    model = AutoModelForCausalLM.from_pretrained(
+        model_name_or_path,
+        trust_remote_code=True,
+        low_cpu_mem_usage=True,
+        torch_dtype=torch.float16,
+        device_map="auto"
+    )
+    # 加载保存的 Adapter
+    model = PeftModel.from_pretrained(model, adapter_name_or_path, device_map="auto",trust_remote_code=True)
+    # 将 Adapter 合并到基础模型中
+    merged_model = model.merge_and_unload()  # PEFT 的方法将 Adapter 权重合并到基础模型
+    # 保存合并后的模型
+    tokenizer.save_pretrained(save_path)
+    merged_model.save_pretrained(save_path, safe_serialization=False)
+    copy_files_not_in_B(model_name_or_path, save_path)
+    print(f"合并后的模型已保存至: {save_path}")
+
+if __name__ == '__main__':
+    merge_lora_to_base_model()

adapter_config.json

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+{
+  "alpha_pattern": {},
+  "auto_mapping": null,
+  "base_model_name_or_path": "/data4/zxf_2/large_model/Qwen/Qwen2.5-Coder-7B-Instruct",
+  "bias": "none",
+  "fan_in_fan_out": false,
+  "inference_mode": true,
+  "init_lora_weights": true,
+  "layer_replication": null,
+  "layers_pattern": null,
+  "layers_to_transform": null,
+  "loftq_config": {},
+  "lora_alpha": 16,
+  "lora_dropout": 0.1,
+  "megatron_config": null,
+  "megatron_core": "megatron.core",
+  "modules_to_save": null,
+  "peft_type": "LORA",
+  "r": 64,
+  "rank_pattern": {},
+  "revision": null,
+  "target_modules": [
+    "k_proj",
+    "o_proj",
+    "q_proj",
+    "up_proj",
+    "v_proj",
+    "gate_proj",
+    "down_proj"
+  ],
+  "task_type": "CAUSAL_LM",
+  "use_dora": false,
+  "use_rslora": false
+}

rng_state.pth

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+size 14244

scheduler.pt

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+oid sha256:f39804fe11ca958234bcad7fd5559b97aa33843cccfb2c28d5eafcc996c5d612
+size 1064

training_args.bin

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+size 5112