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---
tags:
- text-generation-inference
- transformers
- trl
- sft
license: apache-2.0
language:
- en
---
# INFERENCE
```python
import time
import torch
from transformers import AutoTokenizer, AutoModelForCausalLM, TextStreamer
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
finetuned_model = AutoModelForCausalLM.from_pretrained("Mr-Vicky-01/sql-assistant")
finetuned_model.to(device)
tokenizer = AutoTokenizer.from_pretrained("Mr-Vicky-01/sql-assistant")
prompt = """<|im_start|>system
<|im_start|>system
You are a helpful SQL assistant named Securitron. Your working table is 'scans' with the following schema:
CREATE TABLE scans (
id SERIAL PRIMARY KEY,
findings_sca INT,
findings_secrets INT,
findings_compliance INT,
findings_iac INT,
findings_malware INT,
findings_api INT,
findings_pii INT,
findings_container INT,
timestamp TIMESTAMP,
total_findings INT,
fp_vulnerabilities INT,
tp_vulnerabilities INT,
unverified_vulnerabilities INT,
findings_sast INT,
group_id INT,
project_link TEXT,
project TEXT,
repository TEXT,
scan_link TEXT,
scan_id TEXT,
branch TEXT,
commit TEXT,
tags TEXT,
initiator TEXT
);<|im_end|>
<|im_start|>user
Show me yesterday's scan with the fewest API findings.<|im_end|>
<|im_start|>assistant
"""
s = time.time()
encodeds = tokenizer(prompt, return_tensors="pt",truncation=True).input_ids.to(device)
text_streamer = TextStreamer(tokenizer, skip_prompt = True)
# Increase max_new_tokens if needed
response = finetuned_model.generate(
input_ids=encodeds,
streamer=text_streamer,
max_new_tokens=512,
use_cache=True,
pad_token_id=151645,
eos_token_id=151645,
num_return_sequences=1
)
e = time.time()
print(f'time taken:{e-s}')
``` |