Update README.md

.gitattributes

@@ -35,3 +35,7 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
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 *.ggml filter=lfs diff=lfs merge=lfs -text
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.ipynb_checkpoints/README-checkpoint.md

@@ -0,0 +1,77 @@
+---
+frameworks:
+- Pytorch
+license: Apache License 2.0
+tasks:
+- text-classification
+
+#model-type:
+##如 gpt、phi、llama、chatglm、baichuan 等
+- llama
+
+#domain:
+##如 nlp、cv、audio、multi-modal
+- nlp
+
+#language:
+##语言代码列表 https://help.aliyun.com/document_detail/215387.html?spm=a2c4g.11186623.0.0.9f8d7467kni6Aa
+- cn 
+- zh
+
+#metrics:
+##如 CIDEr、Blue、ROUGE 等
+#- CIDEr
+
+#tags:
+##各种自定义，包括 pretrained、fine-tuned、instruction-tuned、RL-tuned 等训练方法和其他
+- fine-tuned
+
+#tools:
+##如 vllm、fastchat、llamacpp、AdaSeq 等
+- vllm
+
+---
+# Unichat-llama3-Chinese-8B
+
+
+## 介绍
+* 中国联通AI创新中心发布llama3.1中文指令微调模型，全参数微调
+* 基础模型 [**Meta-Llama-3.1-8B-Instruct**](https://huggingface.co/meta-llama/Meta-Llama-3.1-8B-Instruct)
+
+
+### 📊 数据
+- 高质量指令数据，覆盖多个领域和行业，为模型训练提供充足的数据支持
+- 微调指令数据经过严格的人工筛查，保证优质的指令数据用于模型微调
+
+
+```python
+import transformers
+import torch
+
+model_id = "UnicomAI/Unichat-llama3.1-Chinese-8B"
+
+pipeline = transformers.pipeline(
+    "text-generation",
+    model=model_id,
+    model_kwargs={"torch_dtype": torch.bfloat16},
+    device_map="auto",
+)
+
+messages = [
+    {"role": "system", "content": "You are a helpful assistant"},
+    {"role": "user", "content": "你是谁?"},
+]
+
+outputs = pipeline(
+    messages,
+    max_new_tokens=1024,
+    do_sample=False,
+    repetition_penalty=1.1,
+)
+print(outputs[0]["generated_text"][-1])
+```
+
+## 资源
+更多模型,数据集和训练相关细节请参考:
+* Github：[**Unichat-llama3-Chinese**](https://github.com/UnicomAI/Unichat-llama3-Chinese)
+ 

README.md

@@ -7,15 +7,16 @@ tasks:
 
 #model-type:
 ##如 gpt、phi、llama、chatglm、baichuan 等
-#- gpt
+- llama
 
 #domain:
 ##如 nlp、cv、audio、multi-modal
-#- nlp
+- nlp
 
 #language:
 ##语言代码列表 https://help.aliyun.com/document_detail/215387.html?spm=a2c4g.11186623.0.0.9f8d7467kni6Aa
-#- cn 
+- cn 
+- zh
 
 #metrics:
 ##如 CIDEr、Blue、ROUGE 等
@@ -23,29 +24,71 @@ tasks:
 
 #tags:
 ##各种自定义，包括 pretrained、fine-tuned、instruction-tuned、RL-tuned 等训练方法和其他
-#- pretrained
+- fine-tuned
 
 #tools:
 ##如 vllm、fastchat、llamacpp、AdaSeq 等
-#- vllm
+- vllm
+
 ---
-### 当前模型的贡献者未提供更加详细的模型介绍。模型文件和权重，可浏览“模型文件”页面获取。
-#### 您可以通过如下git clone命令，或者ModelScope SDK来下载模型
+# Unichat-llama3.1-Chinese-8B
 
-SDK下载
-```bash
-#安装ModelScope
-pip install modelscope
-```
+
+## 介绍
+* 中国联通AI创新中心发布llama3.1中文指令微调模型（全参数微调）
+* 使用llama3官方对话模板
+* 中文环境下安全和价值观改造
+* 模型中文问题回答风格优化
+* 基础模型 [**Meta-Llama-3.1-8B-Instruct**](https://huggingface.co/meta-llama/Meta-Llama-3.1-8B-Instruct)
+
+
+### 📊 数据
+- 高质量指令数据，覆盖多个领域和行业，为模型训练提供充足的数据支持
+- 微调指令数据经过严格的人工筛查，保证优质的指令数据用于模型微调
+
+## 问答示例
+
+- 问：曼谷的大皇宫与卧佛寺，哪个玩起来更有意思？
+
+- 答：在探讨曼谷的大皇宫与卧佛寺哪个更有趣时，我们可以从多个维度进行分析。
+
+首先，大皇宫（Grand Palace）是曼谷最著名的景点之一，以其宏伟的建筑和丰富的历史文化价值而闻名。游客可以在这里参观到许多珍贵的艺术品和文物，同时了解泰国的历史和传统。此外，大皇宫还举办了许多重要的活动，如王室婚礼等，这为游客提供了独特的视觉体验。
+
+另一方面，卧佛寺（Wat Pho）则以其庞大的金色佛像而受到游客的喜爱。这座寺庙不仅是一个宗教场所，也是一个休闲娱乐的地方。游客可以在这里欣赏美丽的佛像、享受按摩服务，还可以购买一些特色纪念品。同时，卧佛寺周边还有许多小吃摊位，供游客品尝当地美食。
+
+总的来说，大皇宫和卧佛寺各有千秋。大皇宫更注重历史文化的展示和教育，而卧佛寺则更侧重于宗教信仰和休闲娱乐。如果你对泰国的历史和文化感兴趣，那么大皇宫可能会让你更加满意；如果你希望在旅行中放松身心，并体验一下当地的宗教氛围，那么卧佛寺可能会是你的首选。
+
+因此，选择哪个更有意思取决于个人兴趣和偏好。如果时间允许，建议两者都去一趟，以便全面领略曼谷的魅力。
+
+## 运行代码
 ```python
-#SDK模型下载
-from modelscope import snapshot_download
-model_dir = snapshot_download('UnicomAI/Unichat-llama3.1-Chinese-8B')
-```
-Git下载
-```
-#Git模型下载
-git clone https://www.modelscope.cn/UnicomAI/Unichat-llama3.1-Chinese-8B.git
+import transformers
+import torch
+
+model_id = "UnicomAI/Unichat-llama3.1-Chinese-8B"
+
+pipeline = transformers.pipeline(
+    "text-generation",
+    model=model_id,
+    model_kwargs={"torch_dtype": torch.bfloat16},
+    device_map="auto",
+)
+
+messages = [
+    {"role": "system", "content": "You are a helpful assistant"},
+    {"role": "user", "content": "你是谁?"},
+]
+
+outputs = pipeline(
+    messages,
+    max_new_tokens=1024,
+    do_sample=False,
+    repetition_penalty=1.1,
+)
+print(outputs[0]["generated_text"][-1])
 ```
 
-<p style="color: lightgrey;">如果您是本模型的贡献者，我们邀请您根据<a href="https://modelscope.cn/docs/ModelScope%E6%A8%A1%E5%9E%8B%E6%8E%A5%E5%85%A5%E6%B5%81%E7%A8%8B%E6%A6%82%E8%A7%88" style="color: lightgrey; text-decoration: underline;">模型贡献文档</a>，及时完善模型卡片内容。</p>
+## 资源
+更多模型,数据集和训练相关细节请参考:
+* Github：[**Unichat-llama3-Chinese**](https://github.com/UnicomAI/Unichat-llama3-Chinese)
+ 

config.json

@@ -0,0 +1,39 @@
+{
+  "_name_or_path": "/home/jovyan/LLM/lir/model/meta-llama/Meta-Llama-3.1-8B-Instruct",
+  "architectures": [
+    "LlamaForCausalLM"
+  ],
+  "attention_bias": false,
+  "attention_dropout": 0.0,
+  "bos_token_id": 128000,
+  "eos_token_id": [
+    128001,
+    128008,
+    128009
+  ],
+  "hidden_act": "silu",
+  "hidden_size": 4096,
+  "initializer_range": 0.02,
+  "intermediate_size": 14336,
+  "max_position_embeddings": 131072,
+  "mlp_bias": false,
+  "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": {
+    "factor": 8.0,
+    "high_freq_factor": 4.0,
+    "low_freq_factor": 1.0,
+    "original_max_position_embeddings": 8192,
+    "rope_type": "llama3"
+  },
+  "rope_theta": 500000.0,
+  "tie_word_embeddings": false,
+  "torch_dtype": "bfloat16",
+  "transformers_version": "4.43.1",
+  "use_cache": false,
+  "vocab_size": 128256
+}

configuration.json

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

generation_config.json

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+{
+  "do_sample": true,
+  "eos_token_id": 128009,
+  "max_new_tokens": 2048,
+  "pad_token_id": 128009,
+  "temperature": 0.3,
+  "top_k": 20,
+  "top_p": 0.7,
+  "transformers_version": "4.43.1"
+}

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