---
license: apache-2.0
---
# Inference
```python
import time
import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Mr-Vicky-01/gpt2-medium-Fintuned")
model = AutoModelForCausalLM.from_pretrained("Mr-Vicky-01/gpt2-medium-Fintuned")
```

```python
BOS_TOKEN = "<sos>"
alpaca_prompt = BOS_TOKEN + """You are an AI specialized in generating SQL queries. Your task is to provide SQL queries based on the given instruction and input.

### Instruction:

The schema for the scans table is as follows:

org_name: Organization name
group_name: Group name
project_name: Project name
git_url: Repo URL
public: Boolean (True or False)
frequency: Scan frequency (e.g., Once, Daily, Weekly, Monthly, Hourly)
status: Scan status (e.g., COMPLETED, RUNNING, SCANNING, FAILED, CLONING, CLOCING)
created_at: Timestamp (DD-MM-YYYY HH:MM)
total_vulns: Number of vulnerabilities
line_of_codes: Lines of code scanned
files_scanned: Files scanned
total_sast_findings: SAST scan vulnerabilities
total_exec_time_sast: SAST scan execution time (seconds)
total_secret_findings: Secret scan vulnerabilities
total_exec_time_secret: Secret scan execution time (seconds)
total_pii_findings: PII scan vulnerabilities
total_exec_time_pii: PII scan execution time (seconds)
total_sca_findings: SCA scan vulnerabilities
total_exec_time_sca: SCA scan execution time (seconds)
total_container_findings: Container scan vulnerabilities
total_exec_time_container: Container scan execution time (seconds)
total_malware_findings: Malware scan vulnerabilities
total_exec_time_malware: Malware scan execution time (seconds)
total_api_findings: API scan vulnerabilities
total_exec_time_api: API scan execution time (seconds)
total_iac_findings: IAC scan vulnerabilities
total_exec_time_iac: IAC scan execution time (seconds)
exec_time: Total scan execution time (seconds)
total_findings: Total vulnerabilities found

### Input:
{}

### Response:
"""
input_ques = "how many scans i completed today".lower()

s = time.time()
prompt = alpaca_prompt.format(input_ques)
encodeds = tokenizer(prompt, return_tensors="pt",truncation=True).input_ids

device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
model.to(device)
inputs = encodeds.to(device)

# Increase max_new_tokens if needed
generated_ids = model.generate(inputs, max_new_tokens=256,temperature=0.1, top_p=0.90, do_sample=True,pad_token_id=50258,eos_token_id=50258,num_return_sequences=1)
print(tokenizer.decode(generated_ids[0]).replace(prompt,'').split('<eos>')[0])
e = time.time()
print(f'time taken:{e-s}')
```