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---
license: apache-2.0
datasets:
- IAmSkyDra/HCMUT_FAQ
language:
- vi
tags:
- education
- text-generation-inference
- gemma
- llama-factory
- unsloth
widget:
- text: Chào bạn
output:
text: >-
Chào bạn! Tôi là GemSUra-edu, một trợ lý AI được phát triển bởi Long
Nguyen.
example_title: Query 1
- text: Hiệu trưởng hiện tại của trường Đại học Bách Khoa
output:
text: >-
Hiệu trưởng hiện tại của trường Đại học Bách Khoa là PGS. TS. Mai Thanh Phong.
example_title: Query 2
- text: OISP là viết tắt của
output:
text: >-
Văn phòng Đào tạo Quốc tế (Office for International Study Programs)
example_title: Query 3
---
## Introduction
GemSUra-edu is a large language model fine-tuned on a dataset of FAQs from HCMUT, based on the pre-trained model [GemSUra 2B](https://huggingface.co/ura-hcmut/GemSUra-2B) developed by the URA research group at Ho Chi Minh City University of Technology (HCMUT).
## Inference (with Unsloth for higher speed)
```python
from unsloth import FastLanguageModel
import torch
# Load model and tokenizer
model, tokenizer = FastLanguageModel.from_pretrained(
model_name="IAmSkyDra/GemSUra-edu",
max_seq_length=4096,
dtype=None,
load_in_4bit=True
)
FastLanguageModel.for_inference(model)
query_template = "<start_of_turn>user\n{query}<end_of_turn>\n<start_of_turn>model\n"
while True:
query = input("Query: ")
if query.lower() == "exit":
break
query = query_template.format(query=query)
inputs = tokenizer(query, return_tensors="pt")
outputs = model.generate(**inputs, max_new_tokens=4096, use_cache=True)
generated_text = tokenizer.batch_decode(outputs, skip_special_tokens=True)
answer = generated_text[0].split("model\n")[1].strip()
print(answer)
```
## Inference (with Transformers)
```python
import transformers
from transformers import AutoModelForCausalLM, AutoTokenizer
pipeline_kwargs = {
"temperature": 0.1,
"max_new_tokens": 4096,
"do_sample": True
}
if __name__ == "__main__":
# Load model
model = AutoModelForCausalLM.from_pretrained(
"IAmSkyDra/GemSUra-edu",
device_map="auto"
)
model.eval()
# Load tokenizer
tokenizer = AutoTokenizer.from_pretrained(
"IAmSkyDra/GemSUra-edu",
trust_remote_code=True
)
pipeline = transformers.pipeline(
model=model,
tokenizer=tokenizer,
return_full_text=False,
task='text-generation',
**pipeline_kwargs
)
query_template = "<start_of_turn>user\n{query}<end_of_turn>\n<start_of_turn>model\n"
while True:
query = input("Query: ")
if query.lower() == "exit":
break
query = query_template.format(query=query)
answer = pipeline(query)[0]["generated_text"]
answer = answer.split("model\n")[1].strip()
print(answer)
```
## Notation
If you want to quantize the model for deployment on local devices, it should be quantized to at least 8 bits. |