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---
license: cc-by-sa-4.0
metrics:
- accuracy
pipeline_tag: text-generation
tags:
- code
---
A capable language model for text to SQL generation for Postgres, Redshift and Snowflake that is on-par with the most capable generalist frontier models.
## Model Description
Developed by: Defog, Inc
Model type: [Text to SQL]
License: [CC-by-SA-4.0]
Finetuned from model: [Meta-Llama-3-8B-Instruct]
## defog/llama-3-sqlcoder-8b for CTranslate2
**The model is quantized version of the [defog/llama-3-sqlcoder-8b](https://huggingface.co/defog/llama-3-sqlcoder-8b) with int8_float16 quantization and can be used in [CTranslate2](https://github.com/OpenNMT/CTranslate2).**
## How to use
```pip install ctranslate2```
This repository for use with [CTranslate2](https://github.com/OpenNMT/CTranslate2).
### Use with CTranslate2
This example code is obtained from [CTranslate2_transformers](https://opennmt.net/CTranslate2/guides/transformers.html#mpt) and [tokenizer AutoTokenizer](https://huggingface.co/docs/transformers/main_classes/tokenizer).
More detailed information about the `generate_batch` methon can be found at [CTranslate2_Generator.generate_batch](https://opennmt.net/CTranslate2/python/ctranslate2.Generator.html#ctranslate2.Generator.generate_batch).
```python
import ctranslate2
import transformers
from huggingface_hub import snapshot_download
model_id = "ByteForge/Defog_llama-3-sqlcoder-8b-ct2-int8_float16"
model_path = snapshot_download(model_id)
model = ctranslate2.Generator(model_path)
tokenizer = transformers.AutoTokenizer.from_pretrained(model_id)
prompt="""
CREATE TABLE stadium (
stadium_id number,
location text,
name text,
capacity number,
highest number,
lowest number,
average number
)
CREATE TABLE singer (
singer_id number,
name text,
country text,
song_name text,
song_release_year text,
age number,
is_male others
)
CREATE TABLE concert (
concert_id number,
concert_name text,
theme text,
stadium_id text,
year text
)
CREATE TABLE singer_in_concert (
concert_id number,
singer_id text
)
-- Using valid SQLite, answer the following questions for the tables provided above.
-- What is the maximum, the average, and the minimum capacity of stadiums ? (Generate 1 Sql query. No explaination needed)
answer:
"""
messages = [
{"role": "system", "content": "You are SQL Expert. Given a input question and schema, answer with correct sql query"},
{"role": "user", "content": prompt},
]
input_ids = tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True
)
terminators = [
tokenizer.eos_token_id,
tokenizer.convert_tokens_to_ids("<|eot_id|>")
]
input_tokens = tokenizer.convert_ids_to_tokens(tokenizer.encode(input_ids))
results = model.generate_batch([input_tokens], include_prompt_in_result=False, max_length=256, sampling_temperature=0.6, sampling_topp=0.9, end_token=terminators)
output = tokenizer.decode(results[0].sequences_ids[0])
print(output)
```
## Ideal prompt and inference parameters
Set temperature to 0, and do not do sampling.
## Evaluation
This model was evaluated on SQL-Eval, a PostgreSQL based evaluation framework developed by Defog for testing and alignment of model capabilities.
You can read more about the methodology behind SQLEval [here](https://defog.ai/blog/open-sourcing-sqleval/). |