初始化项目，由ModelHub XC社区提供模型

Model: OneGate/OG-SQL-7B
Source: Original Platform

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README.md

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+---
+license: cc-by-4.0
+language:
+- en
+tags:
+- Text-to-sql
+---
+# OGSQL-7B
+
+![image/png](https://cdn-uploads.huggingface.co/production/uploads/657ad4e2583493c1d1efb05b/RFfCWir0Jzhm-biGceU0c.png)
+
+### Model Description
+OGSQL-7B  was fine-tuned for the task of converting natural language text into SQL queries.
+
+- **Model type**: Transformer
+- **Language(s) (NLP)**: SQL (target language for generation)
+- **Finetuned from model**: codellama 7b
+
+## Use Case
+OGSQL-7B is designed to facilitate the conversion of natural language queries into structured SQL commands, aiding in database querying without the need for manual SQL knowledge.
+
+## How to Get Started with the Model
+```python
+# Example code to load and use the model
+from transformers import AutoModelForSeq2SeqLM, AutoTokenizer
+
+model_name = "OGSQL-7B"
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForSeq2SeqLM.from_pretrained(model_name)
+
+def generate_sql(query):
+    inputs = tokenizer.encode(query, return_tensors="pt")
+    outputs = model.generate(inputs)
+    return tokenizer.decode(outputs[0], skip_special_tokens=True)
+
+# Example use
+query = """
+using this context:
+-- Create Customers Table
+CREATE TABLE Customers (
+    customer_id INTEGER PRIMARY KEY,
+    name TEXT NOT NULL,
+    email TEXT,
+    join_date DATE
+);
+
+-- Create Products Table
+CREATE TABLE Products (
+    product_id INTEGER PRIMARY KEY,
+    name TEXT NOT NULL,
+    price DECIMAL(10, 2)
+);
+
+-- Create Orders Table
+CREATE TABLE Orders (
+    order_id INTEGER PRIMARY KEY,
+    customer_id INTEGER,
+    product_id INTEGER,
+    order_date DATE,
+    quantity INTEGER,
+    total_price DECIMAL(10, 2),
+    FOREIGN KEY (customer_id) REFERENCES Customers(customer_id),
+    FOREIGN KEY (product_id) REFERENCES Products(product_id)
+);
+
+show me all the orders from last month , sort by date
+
+
+"""
+print(generate_sql(query))
+
+```
+
+
+## alternatively  you can use this notebook:  
+[![Colab notebook](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1zfuzV3R1GQflHV_va03WArb8vhwPh_2T)
+

config.json

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+{
+  "_name_or_path": "defog/sqlcoder-7b-2",
+  "architectures": [
+    "LlamaForCausalLM"
+  ],
+  "attention_bias": false,
+  "attention_dropout": 0.0,
+  "bos_token_id": 1,
+  "eos_token_id": 2,
+  "hidden_act": "silu",
+  "hidden_size": 4096,
+  "initializer_range": 0.02,
+  "intermediate_size": 11008,
+  "max_position_embeddings": 16384,
+  "model_type": "llama",
+  "num_attention_heads": 32,
+  "num_hidden_layers": 32,
+  "num_key_value_heads": 32,
+  "pretraining_tp": 1,
+  "rms_norm_eps": 1e-05,
+  "rope_scaling": null,
+  "rope_theta": 1000000,
+  "tie_word_embeddings": false,
+  "torch_dtype": "float16",
+  "transformers_version": "4.39.3",
+  "use_cache": true,
+  "vocab_size": 32016
+}

generation_config.json

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+{
+  "_from_model_config": true,
+  "bos_token_id": 1,
+  "eos_token_id": 2,
+  "transformers_version": "4.39.3"
+}

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+    "▁<EOT>",
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+    "▁<EOT>"
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+      "content": "▁<EOT>",
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+    "▁<EOT>",
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+    "▁<EOT>"
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+  "eot_token": "▁<EOT>",
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