Instructions to use Mr-Vicky-01/sql-assistant with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Mr-Vicky-01/sql-assistant with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="Mr-Vicky-01/sql-assistant")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Mr-Vicky-01/sql-assistant")
model = AutoModelForCausalLM.from_pretrained("Mr-Vicky-01/sql-assistant")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Inference
Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use Mr-Vicky-01/sql-assistant with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Mr-Vicky-01/sql-assistant"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Mr-Vicky-01/sql-assistant",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Mr-Vicky-01/sql-assistant

SGLang

How to use Mr-Vicky-01/sql-assistant with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "Mr-Vicky-01/sql-assistant" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Mr-Vicky-01/sql-assistant",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "Mr-Vicky-01/sql-assistant" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Mr-Vicky-01/sql-assistant",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use Mr-Vicky-01/sql-assistant with Docker Model Runner:
```
docker model run hf.co/Mr-Vicky-01/sql-assistant
```

INFERENCE

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}')

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Safetensors

Model size

0.5B params

Tensor type

F32