chatdb
/

natural-sql-7b

@@ -1,201 +1,158 @@
 ---
 library_name: transformers
-tags: []
 ---
-# Model Card for Model ID
-<!-- Provide a quick summary of what the model is/does. -->
-## Model Details
-### Model Description
-<!-- Provide a longer summary of what this model is. -->
-This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
-- **Developed by:** [More Information Needed]
-- **Funded by [optional]:** [More Information Needed]
-- **Shared by [optional]:** [More Information Needed]
-- **Model type:** [More Information Needed]
-- **Language(s) (NLP):** [More Information Needed]
-- **License:** [More Information Needed]
-- **Finetuned from model [optional]:** [More Information Needed]
-### Model Sources [optional]
-<!-- Provide the basic links for the model. -->
-- **Repository:** [More Information Needed]
-- **Paper [optional]:** [More Information Needed]
-- **Demo [optional]:** [More Information Needed]
-## Uses
-<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
-### Direct Use
-<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
-[More Information Needed]
-### Downstream Use [optional]
-<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
-[More Information Needed]
-### Out-of-Scope Use
-<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
-[More Information Needed]
-## Bias, Risks, and Limitations
-<!-- This section is meant to convey both technical and sociotechnical limitations. -->
-[More Information Needed]
-### Recommendations
-<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
-Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
-## How to Get Started with the Model
-Use the code below to get started with the model.
-[More Information Needed]
-## Training Details
-### Training Data
-<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
-[More Information Needed]
-### Training Procedure
-<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
-#### Preprocessing [optional]
-[More Information Needed]
-#### Training Hyperparameters
-- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
-#### Speeds, Sizes, Times [optional]
-<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
-[More Information Needed]
-## Evaluation
-<!-- This section describes the evaluation protocols and provides the results. -->
-### Testing Data, Factors & Metrics
-#### Testing Data
-<!-- This should link to a Dataset Card if possible. -->
-[More Information Needed]
-#### Factors
-<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
-[More Information Needed]
-#### Metrics
-<!-- These are the evaluation metrics being used, ideally with a description of why. -->
-[More Information Needed]
-### Results
-[More Information Needed]
-#### Summary
-## Model Examination [optional]
-<!-- Relevant interpretability work for the model goes here -->
-[More Information Needed]
-## Environmental Impact
-<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
-Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
-- **Hardware Type:** [More Information Needed]
-- **Hours used:** [More Information Needed]
-- **Cloud Provider:** [More Information Needed]
-- **Compute Region:** [More Information Needed]
-- **Carbon Emitted:** [More Information Needed]
-## Technical Specifications [optional]
-### Model Architecture and Objective
-[More Information Needed]
-### Compute Infrastructure
-[More Information Needed]
-#### Hardware
-[More Information Needed]
-#### Software
-[More Information Needed]
-## Citation [optional]
-<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
-**BibTeX:**
-[More Information Needed]
-**APA:**
-[More Information Needed]
-## Glossary [optional]
-<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
-[More Information Needed]
-## More Information [optional]
-[More Information Needed]
-## Model Card Authors [optional]
-[More Information Needed]
-## Model Card Contact
-[More Information Needed]

 ---
+base_model: deepseek-ai/deepseek-coder-6.7b-instruct
+tags:
+  - instruct
+  - finetune
 library_name: transformers
+license: cc-by-sa-4.0
+pipeline_tag: text-generation
 ---
+# **Natural-SQL-7B by ChatDB**
+## Natural-SQL-7B is a model with very strong performance in Text-to-SQL instructions, has an excellent understanding of complex questions, and outperforms models of the same size in its space.
+<img src="https://cdn-uploads.huggingface.co/production/uploads/648a374f00f7a3374ee64b99/hafdsfrFCqrVbATIzV_EN.png" width="600">
+[ChatDB.ai](https://chatdb.ai) | [Notebook](https://github.com/cfahlgren1/natural-sql/blob/main/natural-sql-7b.ipynb) | [Twitter](https://twitter.com/calebfahlgren)
+# **Benchmarks**
+### *Results on Novel Datasets not trained on via SQL-Eval*
+<img src="https://cdn-uploads.huggingface.co/production/uploads/648a374f00f7a3374ee64b99/5ynfoKPzI3_-WasQQt7qR.png" width="800">
+<em>Big thanks to the [defog](https://huggingface.co/defog) team for open sourcing [sql-eval](https://github.com/defog-ai/sql-eval)</em>👏
+Natural-SQL also can handle complex, compound questions that other models typically struggle with. There is a more detailed writeup Here is a write up, small test done [here](https://chatdb.ai/post/naturalsql-vs-sqlcoder-for-text-to-sql).
+# Usage
+Make sure you have the correct version of the transformers library installed:
+```sh
+pip install transformers==4.35.2
+```
+### Loading the Model
+Use the following Python code to load the model:
+```python
+import torch
+from transformers import AutoModelForCausalLM, AutoTokenizer
+tokenizer = AutoTokenizer.from_pretrained("chatdb/natural-sql-7b")
+model = AutoModelForCausalLM.from_pretrained(
+    "chatdb/natural-sql-7b",
+    device_map="auto",
+    torch_dtype=torch.float16,
+)
+```
+### **License**
+The model weights are licensed under `CC BY-SA 4.0`, with extra guidelines for responsible use.
+Essentially, you're free to use and adapt the model, even commercially.
+If you alter the weights, such as through fine-tuning, you must publicly share your changes under the same `CC BY-SA 4.0` license.
+### Generating SQL
+```python
+inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
+generated_ids = model.generate(
+    **inputs,
+    num_return_sequences=1,
+    eos_token_id=100001,
+    pad_token_id=100001,
+    max_new_tokens=400,
+    do_sample=False,
+    num_beams=1,
+)
+outputs = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)
+print(outputs[0].split("```sql")[-1])
+```
+# Prompt Template
+```
+# Task
+Generate a SQL query to answer the following question: `{natural language question}`
+### PostgreSQL Database Schema
+The query will run on a database with the following schema:
+<SQL Table DDL Statements>
+# SQL
+Here is the SQL query that answers the question: `{natural language question}`
+'''sql
+```
+# Example SQL Output
+### Example Schemas
+```sql
+CREATE TABLE users (
+        user_id SERIAL PRIMARY KEY,
+        username VARCHAR(50) NOT NULL,
+        email VARCHAR(100) NOT NULL,
+        password_hash TEXT NOT NULL,
+        created_at TIMESTAMP NOT NULL DEFAULT CURRENT_TIMESTAMP
+    );
+CREATE TABLE projects (
+    project_id SERIAL PRIMARY KEY,
+    project_name VARCHAR(100) NOT NULL,
+    description TEXT,
+    start_date DATE,
+    end_date DATE,
+    owner_id INTEGER REFERENCES users(user_id)
+);
+CREATE TABLE tasks (
+    task_id SERIAL PRIMARY KEY,
+    task_name VARCHAR(100) NOT NULL,
+    description TEXT,
+    due_date DATE,
+    status VARCHAR(50),
+    project_id INTEGER REFERENCES projects(project_id)
+);
+CREATE TABLE taskassignments (
+    assignment_id SERIAL PRIMARY KEY,
+    task_id INTEGER REFERENCES tasks(task_id),
+    user_id INTEGER REFERENCES users(user_id),
+    assigned_date DATE NOT NULL DEFAULT CURRENT_TIMESTAMP
+);
+CREATE TABLE comments (
+    comment_id SERIAL PRIMARY KEY,
+    content TEXT NOT NULL,
+    created_at TIMESTAMP NOT NULL DEFAULT CURRENT_TIMESTAMP,
+    task_id INTEGER REFERENCES tasks(task_id),
+    user_id INTEGER REFERENCES users(user_id)
+);
+```
+### Example SQL Outputs
+**Question**: **Show me the day with the most users joining**
+```sql
+SELECT created_at::DATE AS day, COUNT(*) AS user_count
+FROM users
+GROUP BY day
+ORDER BY user_count DESC
+LIMIT 1;
+```
+**Question**: **Show me the project that has a task with the most comments**
+```sql
+SELECT p.project_name, t.task_name, COUNT(c.comment_id) AS comment_count
+FROM projects p
+JOIN tasks t ON p.project_id = t.project_id
+JOIN comments c ON t.task_id = c.task_id
+GROUP BY p.project_name, t.task_name
+ORDER BY comment_count DESC
+LIMIT 1;
+```
+**Question**: **What is the ratio of users with gmail addresses vs without?**
+```sql
+SELECT
+    SUM(CASE WHEN email ILIKE '%@gmail.com%' THEN 1 ELSE 0 END)::FLOAT / NULLIF(SUM(CASE WHEN email NOT ILIKE '%@gmail.com%' THEN 1 ELSE 0 END), 0) AS gmail_ratio
+FROM
+    users;
+```