|
--- |
|
license: llama3 |
|
--- |
|
--- |
|
language: |
|
- en |
|
pipeline_tag: text-generation |
|
tags: |
|
- facebook |
|
- meta |
|
- pytorch |
|
- llama |
|
- llama-3 |
|
license: llama3 |
|
|
|
|
|
|
|
--- |
|
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 [meta-llama/Meta-Llama-3-8B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct) 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. |
|
|
|
|
