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README.md

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 ---
-license: apache-2.0
+tasks:
+- text2sql
+model-type:
+- glm
+domain:
+- nlp
+frameworks:
+- pytorch
+backbone:
+- transformer
+containers:
+- registry-vpc.cn-shanghai.aliyuncs.com/cloud-dsw/pytorch:1.8-cpu-py36-ubuntu18.04
+- registry-vpc.cn-shanghai.aliyuncs.com/cloud-dsw/tensorflow:1.12-cpu-py36-ubuntu18.04
+customized-quickstart: False
+finetune-support: False
+license: Apache License 2.0
+language: 
+- en
+- fr
+- cn
+tags:
+- transformer
+pre-train: False
+datasets:
+train:
+- spider dataset
+- bird dataset
 ---
+# sqlgpt 介绍
+SQLGPT 是一个用于 text2SQL 的模型。
+## 模型底座
+它是基于 deepseek 模型构建的，该模型通过使用qlora进行训练。
+## 训练数据
+训练数据包含了spider 数据集。这个模型的目标是将自然语言查询转换为SQL查询。
+## 功能
+Text to SQL( 以下简称Text2SQL)，是将自然语言文本（Text）转换成结构化查询语言SQL的过程，属于自然语言处理-语义分析（Semantic Parsing）领域中的子任务。
+它的目的可以简单概括为：“打破人与结构化数据之间的壁垒”，即普通用户可以通过自然语言描述完成复杂数据库的查询工作，得到想要的结果。
+它通过学习语法、语义和查询意图来理解用户的问题，并根据对应的数据库结构生成相应的SQL查询语句。SQL-Codellama的训练过程经过了大量的数据预处理、特征提取和模型训练，以提高其准确性和性能。
+它可以应用于各种领域，如数据分析、数据库查询优化等。SQL-Codellama的设计和训练过程是为了使其能够处理复杂的查询，并产生高质量的SQL查询结果。它的目标是为用户提供准确、高效的文本到SQL转换，从而帮助用户更轻松地进行数据库查询和数据分析。
+
+```bash
+ git clone https://www.modelscope.cn/tomatoModelScope/sql_codellama.git
+```

config.json

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+{
+  "_name_or_path": "/mnt/user/294761/finetune/deepseek-models/deepseek-coder-33b-base",
+  "architectures": [
+    "LlamaForCausalLM"
+  ],
+  "attention_bias": false,
+  "attention_dropout": 0.0,
+  "bos_token_id": 32013,
+  "eos_token": "<\uff5cend\u2581of\u2581sentence\uff5c>",
+  "eos_token_id": 32014,
+  "hidden_act": "silu",
+  "hidden_size": 7168,
+  "initializer_range": 0.02,
+  "intermediate_size": 19200,
+  "max_position_embeddings": 16384,
+  "model_type": "llama",
+  "num_attention_heads": 56,
+  "num_hidden_layers": 62,
+  "num_key_value_heads": 8,
+  "pad_token": "<\uff5cend\u2581of\u2581sentence\uff5c>",
+  "pad_token_id": 32014,
+  "pretraining_tp": 1,
+  "rms_norm_eps": 1e-06,
+  "rope_scaling": {
+    "factor": 4.0,
+    "type": "linear"
+  },
+  "rope_theta": 100000,
+  "tie_word_embeddings": false,
+  "torch_dtype": "bfloat16",
+  "transformers_version": "4.36.0",
+  "use_cache": true,
+  "vocab_size": 32256
+}

generation_config.json

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+  "_from_model_config": true,
+  "bos_token_id": 32013,
+  "eos_token_id": 32014,
+  "transformers_version": "4.36.0"
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+    "model.layers.9.self_attn.q_proj.weight": "model-00003-of-00014.safetensors",
+    "model.layers.9.self_attn.v_proj.weight": "model-00003-of-00014.safetensors",
+    "model.norm.weight": "model-00014-of-00014.safetensors"
+  }
+}

tokenizer_config.json

@@ -0,0 +1,34 @@
+{
+  "add_bos_token": true,
+  "add_eos_token": false,
+  "bos_token": {
+    "__type": "AddedToken",
+    "content": "<｜begin▁of▁sentence｜>",
+    "lstrip": false,
+    "normalized": true,
+    "rstrip": false,
+    "single_word": false
+  },
+  "clean_up_tokenization_spaces": false,
+  "eos_token": {
+    "__type": "AddedToken",
+    "content": "<｜end▁of▁sentence｜>",
+    "lstrip": false,
+    "normalized": true,
+    "rstrip": false,
+    "single_word": false
+  },
+  "legacy": true,
+  "model_max_length": 16384,
+  "pad_token": {
+    "__type": "AddedToken",
+    "content": "<｜end▁of▁sentence｜>",
+    "lstrip": false,
+    "normalized": true,
+    "rstrip": false,
+    "single_word": false
+  },
+  "sp_model_kwargs": {},
+  "unk_token": null,
+  "tokenizer_class": "LlamaTokenizerFast"
+}

upload.py

@@ -0,0 +1,10 @@
+from modelscope.hub.api import HubApi
+
+YOUR_ACCESS_TOKEN = '请从ModelScope个人中心->访问令牌获取'
+
+api = HubApi()
+api.login(YOUR_ACCESS_TOKEN)
+api.push_model(
+    model_id="yourname/your_model_id", 
+    model_dir="my_model_dir" # 本地模型目录，要求目录中必须包含configuration.json
+)