Upload README.md

README.md

@@ -1,3 +1,146 @@
 ---
 license: llama2
+inference:
+  parameters:
+    do_sample: false
+    max_length: 200
+widget:
+- text: "### Instruction:\nYour task is to generate valid duckdb SQL to answer the following question.\n\n### Input:\n\n### Question:\ncreate a new table called tmp from test.csv\n\n### Response (use duckdb shorthand if possible):"
+  example_title: "read test.csv"
+- text: "### Instruction:\nYour task is to generate valid duckdb SQL to answer the following question.\n\n### Input:\n\n### Question:\ncreate a new table called tmp from test.csv\n\n### Response (use duckdb shorthand if possible):"
+  example_title: "get _amount columns"
+- text: "### Instruction:\nYour task is to generate valid duckdb SQL to answer the following question, given a duckdb database schema.\n\n### Input:\nHere is the database schema that the SQL query will run on:\nCREATE TABLE rideshare (\n    hvfhs_license_num varchar,\n    dispatching_base_num varchar,\n    originating_base_num varchar,\n    request_datetime timestamp,\n    on_scene_datetime timestamp,\n    pickup_datetime timestamp,\n    dropoff_datetime timestamp,\n    trip_miles double,\n    trip_time bigint,\n\n);\n\n### Question:\nget longest trip in december 2022\n\n### Response (use duckdb shorthand if possible):"
+  example_title: "taxi trips"
 ---
+
+# DuckDB-NSQL-7B
+
+## Model Description
+
+NSQL is a family of autoregressive open-source large foundation models (FMs) designed specifically for SQL generation tasks.
+
+In this repository we are introducing a new member of NSQL, DuckDB-NSQL. It's based on Meta's original [Llama-2 7B model](https://huggingface.co/meta-llama/Llama-2-7b) and further pre-trained on a dataset of general SQL queries and then fine-tuned on a dataset composed of DuckDB text-to-SQL pairs.
+
+## Training Data
+
+The general SQL queries are the SQL subset from [The Stack](https://huggingface.co/datasets/bigcode/the-stack), containing 1M training samples. The samples we transpiled to DuckDB SQL, using [sqlglot](https://github.com/tobymao/sqlglot). The labeled text-to-SQL pairs come [NSText2SQL](https://huggingface.co/datasets/NumbersStation/NSText2SQL) that were also transpiled to DuckDB SQL, and 200k synthetically generated DuckDB SQL queries, based on the DuckDB v.0.9.2 documentation. 
+
+## Evaluation Data
+
+We evaluate our models on a DuckDB-specific benchmark that contains 75 text-to-SQL pairs. The benchmark is available [here](https://github.com/NumbersStationAI/DuckDB-NSQL/).
+
+## Training Procedure
+
+DuckDB-NSQL was trained using cross-entropy loss to maximize the likelihood of sequential inputs. For finetuning on text-to-SQL pairs, we only compute the loss over the SQL portion of the pair. The model is trained using 80GB A100s, leveraging data and model parallelism. We pre-trained for 3 epochs and fine-tuned for 10 epochs.
+
+## Intended Use and Limitations
+
+The model was designed for text-to-SQL generation tasks from given table schema and natural language prompts. The model works best with the prompt format defined below and outputs.
+In contrast to existing text-to-SQL models, the SQL generation is not contrained to `SELECT` statements, but can generate any valid DuckDB SQL statement, including statements for official DuckDB extensions.
+
+## How to Use
+
+Example 1:
+
+```python
+import torch
+from transformers import AutoTokenizer, AutoModelForCausalLM
+tokenizer = AutoTokenizer.from_pretrained("motherduckdb/nsql-duckdb-7B")
+model = AutoModelForCausalLM.from_pretrained("motherduckdb/nsql-duckdb-7B", torch_dtype=torch.bfloat16)
+
+text = """### Instruction:
+Your task is to generate valid duckdb SQL to answer the following question.
+
+### Input:
+
+### Question:
+create a new table called tmp from test.csv
+
+### Response (use duckdb shorthand if possible):
+"""
+
+input_ids = tokenizer(text, return_tensors="pt").input_ids
+
+generated_ids = model.generate(input_ids, max_length=500)
+print(tokenizer.decode(generated_ids[0], skip_special_tokens=True))
+```
+
+Example 2:
+
+```python
+import torch
+from transformers import AutoTokenizer, AutoModelForCausalLM
+tokenizer = AutoTokenizer.from_pretrained("motherduckdb/nsql-duckdb-7B")
+model = AutoModelForCausalLM.from_pretrained("motherduckdb/nsql-duckdb-7B", torch_dtype=torch.bfloat16)
+
+text = """### Instruction:
+Your task is to generate valid duckdb SQL to answer the following question, given a duckdb database schema.
+
+### Input:
+Here is the database schema that the SQL query will run on:
+CREATE TABLE taxi (
+    VendorID bigint,
+    tpep_pickup_datetime timestamp,
+    tpep_dropoff_datetime timestamp,
+    passenger_count double,
+    trip_distance double,
+    fare_amount double,
+    extra double,
+    tip_amount double,
+    tolls_amount double,
+    improvement_surcharge double,
+    total_amount double,
+);
+
+### Question:
+get all columns ending with _amount from taxi table
+
+### Response (use duckdb shorthand if possible):"""
+
+input_ids = tokenizer(text, return_tensors="pt").input_ids
+
+generated_ids = model.generate(input_ids, max_length=500)
+print(tokenizer.decode(generated_ids[0], skip_special_tokens=True))
+```
+
+Example 3:
+
+```python
+import torch
+from transformers import AutoTokenizer, AutoModelForCausalLM
+tokenizer = AutoTokenizer.from_pretrained("motherduckdb/nsql-duckdb-7B")
+model = AutoModelForCausalLM.from_pretrained("motherduckdb/nsql-duckdb-7B", torch_dtype=torch.bfloat16)
+
+text = """### Instruction:
+Your task is to generate valid duckdb SQL to answer the following question, given a duckdb database schema.
+
+### Input:
+Here is the database schema that the SQL query will run on:
+CREATE TABLE rideshare (
+    hvfhs_license_num varchar,
+    dispatching_base_num varchar,
+    originating_base_num varchar,
+    request_datetime timestamp,
+    on_scene_datetime timestamp,
+    pickup_datetime timestamp,
+    dropoff_datetime timestamp,
+    trip_miles double,
+    trip_time bigint,
+
+);
+
+### Question:
+get longest trip in december 2022
+
+### Response (use duckdb shorthand if possible):
+"""
+
+input_ids = tokenizer(text, return_tensors="pt").input_ids
+
+generated_ids = model.generate(input_ids, max_length=500)
+print(tokenizer.decode(generated_ids[0], skip_special_tokens=True))
+```
+
+
+
+For more information (e.g., run with your local database), please find examples in [this repository](https://github.com/NumbersStationAI/DuckDB-NSQL).