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---
license: apache-2.0
datasets:
- AIAT/Pangpuriye-dataset
- AIAT/Pangpuriye-public_ThaiSum40k
- AIAT/Pangpuriye-generated_by_LLama3-codeLlama
- AIAT/Pangpuriye-public_alpaca-cleaned
- AIAT/Pangpuriye-generated_by_typhoon
language:
- th
- en
pipeline_tag: text-generation
tags:
- code_generation
- sql
metrics:
- accuracy
---
# 🤖 [Super AI Engineer Development Program Season 4](https://superai.aiat.or.th/) - Pangpuriye Table-based Question Answering Model
This model was fine-tuned from the original [OpenThaiGPT-1.0.1-7b](https://huggingface.co/openthaigpt/openthaigpt-1.0.0-7b-chat). The model is set under Apache license 2.0.
## Example inference using huggingface transformers.
The following code is an exmaple of how to inference our model.
```python
from transformers import AutoModelForCausalLM, AutoTokenizer, LlamaTokenizer
import pandas as pd
def get_prediction(raw_prediction):
if "[/INST]" in raw_prediction:
index = raw_prediction.index("[/INST]")
return raw_prediction[index + 7:]
return raw_prediction
tokenizer = LlamaTokenizer.from_pretrained("AIAT/Pangpuriye-openthaigpt-1.0.0-7b-chat", trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained("AIAT/Pangpuriye-openthaigpt-1.0.0-7b-chat", trust_remote_code=True)
schema = """your SQL schema"""
query = "หาจำนวนลูกค้าที่เป็นเพศชาย"
prompt = f"""
[INST] <<SYS>>
You are a question answering assistant. Answer the question as truthful and helpful as possible คุณคือผู้ช่วยตอบคำถาม จงตอบคำถามอย่างถูกต้องและมีประโยชน์ที่สุด
<</SYS>>
{schema}### (sql extract) {query} [/INST]
"""
tokens = tokenizer(prompt, return_tensors="pt")
output = model.generate(tokens["input_ids"], max_new_tokens=20, eos_token_id=tokenizer.eos_token_id)
print(get_prediction(tokenizer.decode(output[0], skip_special_tokens=True)))
```
## Acknowledgements
The model collaborated by the members of Panguriye's house during the LLMs hackathon in Super AI Engineer Development Program Season 4.
We thank the organizers of this hackathon, [OpenThaiGPT](https://openthaigpt.aieat.or.th/), [AIAT](https://aiat.or.th/), [NECTEC](https://www.nectec.or.th/en/) and [ThaiSC](https://thaisc.io/) for this challenging task and opportunity to be a part of developing Thai large language model. |