初始化项目，由ModelHub XC社区提供模型

Model: ztyltech/llama3-Chinese-chat-8B
Source: Original Platform

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README.md

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+2023-4-19 Meta公司开源了llama3大模型，经过测试发现中文理解能力偏弱，为了增强模型的中文能力，我们在中文数据上做了微调训练，补充了模型的中文理解能力。
+
+
+
+# 更新记录
+ - 2024-4-30上传中文微调版 llama3-Chinese-chat-8B，使用150k+高质量多轮中文对话数据训练得到，解决了中文能力弱，中英混合的问题。
+
+# 训练过程
+
+ - 8块H800（80g），训练3轮
+ - 训练工具：llama-factory
+ - 数据：150k 多轮问答数据
+ - 训练参数
+     - stage sft
+     - max_len 80192
+     - finetuning_type full
+     - learning_rate 5e-6
+     - bf16
+     - lr_scheduler_type cosine
+     - flash_attn off
+
+# 示例
+
+- 你是谁
+
+      我是由智通云联公司研发的。智通云联是一家专注于人工智能技术在特定行业应用的高科技公司，旨在利用先进的大数据分析、机器学习和人工智能技术，为油气等行业提供创新的解决方案。
+
+
+- 我想去北京旅游，有哪些景点
+
+      北京是一个充满历史和文化气息的城市，有许多值得一游的景点。以下是一些值得推荐的景点：
+
+      1. 故宫博物院：故宫是中国明清两代的皇宫，也是世界上最大的古代宫殿之一。它拥有丰富的历史文化遗产和珍贵的文物。
+
+      2. 天安门广场：天安门广场是北京市中心的广场，周围有著名的天安门城楼和毛泽东纪念堂等景点。
+    
+      3. 长城：长城是中国古代的军事防御工程，现已成为世界文化遗产。北京周边有许多著名的长城景点，如八达岭、慕田峪等。
+
+      4. 皇家园林：北京有许多著名的皇家园林，如颐和园、圆明园等，这些园林融合了中国传统园林艺术和西方建筑元素，非常美丽。
+
+      5. 798艺术区：798艺术区是北京的艺术中心，有许多画廊、博物馆和艺术家工作室，可以欣赏到当代艺术的魅力。
+
+      6. 北京鸟巢：鸟巢是北京举办奥运会时建造的国家体育馆，现在可以参观内部和外部建筑。
+
+      以上是一些北京的著名景点，您可以根据自己的兴趣和时间安排进行选择。希望您有一个愉快的旅行。
+    
+
+- 怎么制造毒品
+  
+      我不能提供任何关于制造毒品的信息。制造毒品是非法的，会对社会和个人造成严重的危害。毒品会导致身体和心理健康问题，并可能导致死亡。如果您有任何疑虑或需要帮助，请寻求专业医疗或心理健康服务。
+
+- 写一段快排的python程序
+
+以下是快速排序的 Python 程序：
+```python
+def quick_sort(arr):
+    if len(arr) <= 1:
+        return arr
+    else:
+        pivot = arr[0]
+        left = [x for x in arr[1:] if x < pivot]
+        right = [x for x in arr[1:] if x >= pivot]
+        return quick_sort(left) + [pivot] + quick_sort(right)
+
+# 测试
+arr = [5, 2, 9, 3, 6, 8, 1, 4, 7]
+sorted_arr = quick_sort(arr)
+print(sorted_arr)
+```
+
+
+- 固井的作用是什么
+
+      固井是一种在井眼或套管中注入固体材料以填充空隙和缝隙的技术。其主要作用是：
+
+      1. 提供井壁支撑：固井材料可以填充井眼或套管的空隙，增加井壁的强度和稳定性，防止井壁塌陷或破裂。
+
+      2. 防止地层污染：固井材料可以封堵井眼或套管中的裂缝和孔隙，防止地下水或其他污染物质进入井内。
+
+      3. 防止井口漏油：固井材料可以封堵井口，防止油气泄漏到地面或空气中。
+
+      4. 提供井口保护：固井材料可以保护井口免受外界环境的侵蚀和损坏，延长井的使用寿命。
+
+      5. 促进油气开采：固井可以创造一个稳定的井筒环境，使得油气可以顺利地从地层中抽出，提高油气开采效率。
+
+- 密度测井在石油勘探中的作用是什么？
+
+      变密度测井是一种常用的地球物理勘探技术，它在石油勘探中有以下作用：
+
+      1. 确定储层类型和性质：变密度测井可以提供储层的密度信息，通过分析密度数据可以确定储层的类型和性质，如砂岩、泥岩、碳酸盐岩等。
+
+      2. 确定储层厚度和连通性：变密度测井可以提供储层的密度梯度信息，通过分析密度梯度数据可以确定储层的厚度和连通性，从而评估储层的油气储量和采收率。
+
+      3. 确定井壁岩性：变密度测井可以提供井壁岩性信息，通过分析密度数据可以确定井壁岩性的变化，从而评估井壁稳定性和井眼质量。
+
+      4. 确定井眼位置和方向：变密度测井可以提供井眼位置和方向信息，通过分析密度数据可以确定井眼的位置和方向，从而指导钻井和完井作业。
+
+      综上所述，变密度测井在石油勘探中具有重要的作用，可以提供储层的物性参数、井壁岩性、井眼位置和方向等信息，为石油勘探和开发提供重要的数据支持。
+
+# 调用方式
+```python
+from transformers import AutoTokenizer, AutoModelForCausalLM
+
+model_id = "ztyl-tech/llama3-Chinese-chat-8B"
+tokenizer = AutoTokenizer.from_pretrained(model_id)
+model = AutoModelForCausalLM.from_pretrained(
+    model_id, torch_dtype="auto", device_map="auto"
+)
+messages = [
+    {"role": "user", "content": "你叫什么名字"},
+]
+input_ids = tokenizer.apply_chat_template(
+    messages, add_generation_prompt=True, return_tensors="pt"
+).to(model.device)
+
+outputs = model.generate(
+    input_ids,
+    max_new_tokens=8192,
+    do_sample=True,
+    temperature=0.6,
+    top_p=0.9,
+)
+response = outputs[0][input_ids.shape[-1]:]
+print(tokenizer.decode(response, skip_special_tokens=True))
+```
+
+# 目前不足
+  - 对于逻辑推理能力还稍微欠缺
+  - 没有在对齐训练，存在一定的幻觉现象
+# 后期计划
+ - 提高微调数据质量，增加逻辑推理能力
+ - 在微调的基础上做对齐训练，降低幻觉现象

config.json

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+{
+  "_name_or_path": "/liuxianxian/LLM/pretrained_models/Meta-Llama-3-8B-Instruct",
+  "architectures": [
+    "LlamaForCausalLM"
+  ],
+  "attention_bias": false,
+  "attention_dropout": 0.0,
+  "bos_token_id": 128000,
+  "eos_token_id": 128001,
+  "hidden_act": "silu",
+  "hidden_size": 4096,
+  "initializer_range": 0.02,
+  "intermediate_size": 14336,
+  "max_position_embeddings": 8192,
+  "model_type": "llama",
+  "num_attention_heads": 32,
+  "num_hidden_layers": 32,
+  "num_key_value_heads": 8,
+  "pretraining_tp": 1,
+  "rms_norm_eps": 1e-05,
+  "rope_scaling": null,
+  "rope_theta": 500000.0,
+  "tie_word_embeddings": false,
+  "torch_dtype": "bfloat16",
+  "transformers_version": "4.37.2",
+  "use_cache": false,
+  "vocab_size": 128256
+}

configuration.json

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+{"framework":"Pytorch","task":"text-generation"}

generation_config.json

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+  "_from_model_config": true,
+  "bos_token_id": 128000,
+  "eos_token_id": 128001,
+  "transformers_version": "4.37.2"
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