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Model: motherduckdb/DuckDB-NSQL-7B-v0.1 Source: Original Platform
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README.md
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---
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license: llama2
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inference:
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parameters:
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do_sample: false
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max_length: 200
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base_model: meta-llama/Llama-2-7b
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widget:
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- 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):"
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example_title: "read test.csv"
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- 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):"
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example_title: "get _amount columns"
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- 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):"
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example_title: "taxi trips"
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---
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# DuckDB-NSQL-7B
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## Model Description
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NSQL is a family of autoregressive open-source large foundation models (FMs) designed specifically for SQL generation tasks.
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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.
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## Training Data
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200k DuckDB text-to-SQL pairs, synthetically generated using [Mixtral-8x7B-Instruct-v0.1](https://huggingface.co/mistralai/Mixtral-8x7B-Instruct-v0.1), guided by the DuckDB v0.9.2 documentation. And text-to-SQL pairs from [NSText2SQL](https://huggingface.co/datasets/NumbersStation/NSText2SQL) that were transpiled to DuckDB SQL using [sqlglot](https://github.com/tobymao/sqlglot).
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## Evaluation Data
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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/).
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## Training Procedure
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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 fine-tuned for 10 epochs.
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## Intended Use and Limitations
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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.
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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.
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## How to Use
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Example 1:
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```python
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import torch
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from transformers import AutoTokenizer, AutoModelForCausalLM
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tokenizer = AutoTokenizer.from_pretrained("motherduckdb/DuckDB-NSQL-7B-v0.1")
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model = AutoModelForCausalLM.from_pretrained("motherduckdb/DuckDB-NSQL-7B-v0.1", torch_dtype=torch.bfloat16)
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text = """### Instruction:
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Your task is to generate valid duckdb SQL to answer the following question.
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### Input:
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### Question:
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create a new table called tmp from test.csv
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### Response (use duckdb shorthand if possible):
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"""
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input_ids = tokenizer(text, return_tensors="pt").input_ids
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generated_ids = model.generate(input_ids, max_length=500)
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print(tokenizer.decode(generated_ids[0], skip_special_tokens=True))
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```
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Example 2:
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```python
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import torch
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from transformers import AutoTokenizer, AutoModelForCausalLM
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tokenizer = AutoTokenizer.from_pretrained("motherduckdb/DuckDB-NSQL-7B-v0.1")
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model = AutoModelForCausalLM.from_pretrained("motherduckdb/DuckDB-NSQL-7B-v0.1", torch_dtype=torch.bfloat16)
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text = """### Instruction:
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Your task is to generate valid duckdb SQL to answer the following question, given a duckdb database schema.
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### Input:
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Here is the database schema that the SQL query will run on:
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CREATE TABLE taxi (
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VendorID bigint,
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tpep_pickup_datetime timestamp,
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tpep_dropoff_datetime timestamp,
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passenger_count double,
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trip_distance double,
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fare_amount double,
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extra double,
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tip_amount double,
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tolls_amount double,
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improvement_surcharge double,
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total_amount double,
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);
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### Question:
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get all columns ending with _amount from taxi table
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### Response (use duckdb shorthand if possible):"""
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input_ids = tokenizer(text, return_tensors="pt").input_ids
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generated_ids = model.generate(input_ids, max_length=500)
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print(tokenizer.decode(generated_ids[0], skip_special_tokens=True))
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```
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Example 3:
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```python
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import torch
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from transformers import AutoTokenizer, AutoModelForCausalLM
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tokenizer = AutoTokenizer.from_pretrained("motherduckdb/DuckDB-NSQL-7B-v0.1")
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model = AutoModelForCausalLM.from_pretrained("motherduckdb/DuckDB-NSQL-7B-v0.1", torch_dtype=torch.bfloat16)
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text = """### Instruction:
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Your task is to generate valid duckdb SQL to answer the following question, given a duckdb database schema.
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### Input:
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Here is the database schema that the SQL query will run on:
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CREATE TABLE rideshare (
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hvfhs_license_num varchar,
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dispatching_base_num varchar,
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originating_base_num varchar,
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request_datetime timestamp,
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on_scene_datetime timestamp,
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pickup_datetime timestamp,
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dropoff_datetime timestamp,
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trip_miles double,
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trip_time bigint,
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);
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### Question:
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get longest trip in december 2022
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### Response (use duckdb shorthand if possible):
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"""
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input_ids = tokenizer(text, return_tensors="pt").input_ids
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generated_ids = model.generate(input_ids, max_length=500)
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print(tokenizer.decode(generated_ids[0], skip_special_tokens=True))
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```
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For more information (e.g., run with your local database), please find examples in [this repository](https://github.com/NumbersStationAI/DuckDB-NSQL).
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config.json
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"LlamaForCausalLM"
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"vocab_size": 32016
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}
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||||
"model.layers.7.self_attn.k_proj.weight": "model-00001-of-00003.safetensors",
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"model.layers.7.self_attn.o_proj.weight": "model-00001-of-00003.safetensors",
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||||
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||||
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"model.layers.8.mlp.gate_proj.weight": "model-00001-of-00003.safetensors",
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"model.layers.9.self_attn.k_proj.weight": "model-00001-of-00003.safetensors",
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"model.layers.9.self_attn.o_proj.weight": "model-00001-of-00003.safetensors",
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||||
"model.norm.weight": "model-00003-of-00003.safetensors"
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||||
}
|
||||
}
|
||||
93418
tokenizer.json
Normal file
93418
tokenizer.json
Normal file
File diff suppressed because it is too large
Load Diff
3
tokenizer.model
Normal file
3
tokenizer.model
Normal file
@@ -0,0 +1,3 @@
|
||||
version https://git-lfs.github.com/spec/v1
|
||||
oid sha256:45ccb9c8b6b561889acea59191d66986d314e7cbd6a78abc6e49b139ca91c1e6
|
||||
size 500058
|
||||
34
tokenizer_config.json
Normal file
34
tokenizer_config.json
Normal file
@@ -0,0 +1,34 @@
|
||||
{
|
||||
"add_bos_token": true,
|
||||
"add_eos_token": false,
|
||||
"bos_token": {
|
||||
"__type": "AddedToken",
|
||||
"content": "<s>",
|
||||
"lstrip": false,
|
||||
"normalized": true,
|
||||
"rstrip": false,
|
||||
"single_word": false
|
||||
},
|
||||
"clean_up_tokenization_spaces": false,
|
||||
"eos_token": {
|
||||
"__type": "AddedToken",
|
||||
"content": "</s>",
|
||||
"lstrip": false,
|
||||
"normalized": true,
|
||||
"rstrip": false,
|
||||
"single_word": false
|
||||
},
|
||||
"legacy": null,
|
||||
"model_max_length": 1000000000000000019884624838656,
|
||||
"pad_token": null,
|
||||
"sp_model_kwargs": {},
|
||||
"tokenizer_class": "CodeLlamaTokenizer",
|
||||
"unk_token": {
|
||||
"__type": "AddedToken",
|
||||
"content": "<unk>",
|
||||
"lstrip": false,
|
||||
"normalized": true,
|
||||
"rstrip": false,
|
||||
"single_word": false
|
||||
}
|
||||
}
|
||||
Reference in New Issue
Block a user