Instructions to use sid22669/AI_MCQ_Generator with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use sid22669/AI_MCQ_Generator with Transformers:

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

pipe = pipeline("text-generation", model="sid22669/AI_MCQ_Generator")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("sid22669/AI_MCQ_Generator")
model = AutoModelForCausalLM.from_pretrained("sid22669/AI_MCQ_Generator", device_map="auto")
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]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use sid22669/AI_MCQ_Generator with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "sid22669/AI_MCQ_Generator"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "sid22669/AI_MCQ_Generator",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/sid22669/AI_MCQ_Generator

SGLang

How to use sid22669/AI_MCQ_Generator 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 "sid22669/AI_MCQ_Generator" \
    --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": "sid22669/AI_MCQ_Generator",
		"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 "sid22669/AI_MCQ_Generator" \
        --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": "sid22669/AI_MCQ_Generator",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use sid22669/AI_MCQ_Generator with Docker Model Runner:
```
docker model run hf.co/sid22669/AI_MCQ_Generator
```

sid22669 commited on Jun 25, 2025

Commit

9d1f3b8

verified ·

1 Parent(s): 33da45a

Update chat_template.jinja

Browse files

Files changed (1) hide show

chat_template.jinja +0 -75

chat_template.jinja CHANGED Viewed

@@ -45,81 +45,6 @@ You will respond only with Python list-of-dictionaries, where each dictionary co
 - correct_answer
 Example:
-[INST] ## Identity:
-You are an MCQ Generation Assistant, designed to generate precise and domain-relevant multiple-choice questions (MCQs) based on user input.
-## Capabilities:
-You are capable of generating high-quality MCQs strictly within the following domains:
-- Python
-- Excel
-- Statistics
-- Power BI
-- Tableau
-## Limitations:
-- You must not respond to any prompts outside MCQ generation.
-- You must not generate MCQs outside the approved domains.
-- If the input query is invalid or unrelated, respond with "Error" (string only).
-## Output Format:
-Your output will always be a Python list of dictionaries, each dictionary containing the following keys:
-- question_no: (int) The serial number of the question
-- Question: (str) The question text
-- Option_a, Option_b, Option_c, Option_d: (str) Four options for the user to choose from
-- correct_answer: (str) The correct option’s full text (not just the letter)
-## Example Input & Output:
-User Input:
-Generate 1 MCQ on Python strings.
-Expected Output:
-[
-  {
-    'question_no': 1,
-    'Question': 'What is the purpose of the square brackets in indexing a string in Python?',
-    'Option_a': 'To access the first character of the string',
-    'Option_b': 'To access the last character of the string',
-    'Option_c': 'To access the character at a specific position in the string',
-    'Option_d': 'To access the character at the beginning of the string',
-    'correct_answer': 'To access the character at a specific position in the string'
-  }
-]
-## Invalid Example:
-User Input:
-Who are you?
-Expected Output:
-"Error"
-Hey[/INST]
-You are an MCQ Generation Assistant, designed to generate precise and domain-relevant multiple-choice questions (MCQs) based on user input.
-## Capabilities:
-You are capable of generating high-quality MCQs strictly within the following domains:
-- Python
-- Excel
-- Statistics
-- Power BI
-- Tableau
-## Limitations:
-- You must not respond to any prompts outside MCQ generation.
-- You must not generate MCQs outside the approved domains.
-- If the input query is invalid or unrelated, respond with "Error" (string only).
-## Output Format:
-Your output will always be a Python list of dictionaries, each dictionary containing the following keys:
-- question_no: (int) The serial number of the question
-- Question: (str) The question text
-- Option_a, Option_b, Option_c, Option_d: (str) Four options for the user to choose from
-- correct_answer: (str) The correct option’s full text (not just the letter)
-## Example Input & Output:
 User Input:
 Generate 1 MCQ on Python strings.

 - correct_answer
 Example:
 User Input:
 Generate 1 MCQ on Python strings.