Instructions to use BirendraSharma/llama3.2_1B_distractors_generation with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use BirendraSharma/llama3.2_1B_distractors_generation with Transformers:

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

pipe = pipeline("text-generation", model="BirendraSharma/llama3.2_1B_distractors_generation")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("BirendraSharma/llama3.2_1B_distractors_generation")
model = AutoModelForCausalLM.from_pretrained("BirendraSharma/llama3.2_1B_distractors_generation")
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 BirendraSharma/llama3.2_1B_distractors_generation with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "BirendraSharma/llama3.2_1B_distractors_generation"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "BirendraSharma/llama3.2_1B_distractors_generation",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/BirendraSharma/llama3.2_1B_distractors_generation

SGLang

How to use BirendraSharma/llama3.2_1B_distractors_generation 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 "BirendraSharma/llama3.2_1B_distractors_generation" \
    --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": "BirendraSharma/llama3.2_1B_distractors_generation",
		"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 "BirendraSharma/llama3.2_1B_distractors_generation" \
        --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": "BirendraSharma/llama3.2_1B_distractors_generation",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use BirendraSharma/llama3.2_1B_distractors_generation with Docker Model Runner:
```
docker model run hf.co/BirendraSharma/llama3.2_1B_distractors_generation
```

LLaMA 3.2 1B - Distractors Generation Model

This model is designed to generate distractors (incorrect but plausible answer choices) for multiple-choice questions. It is particularly useful in educational applications where high-quality distractors are needed.

Model Details

Model Name: llama3.2_1B_distractors_generation
Architecture: LLaMA 3.2 1B
Developer: BirendraSharma
Use Case: MCQ distractor generation
License: [More Information Needed]

How to Use the Model

You can load the model and tokenizer using transformers and generate distractors using the following example:

Installation

Ensure you have transformers and torch installed:

pip install transformers torch

Loading the Model & Tokenizer

from transformers import AutoTokenizer, AutoModelForCausalLM, GenerationConfig
import torch

# Load model and tokenizer
model_path = "BirendraSharma/llama3.2_1B_distractors_generation"
tokenizer = AutoTokenizer.from_pretrained(model_path)
model = AutoModelForCausalLM.from_pretrained(model_path, torch_dtype=torch.float16, device_map="auto")

def generate_distractors(context, question, answer, instruction, model, tokenizer, max_seq_length=1024):
    """Generates distractors for a given question-answer pair."""
    model.eval()
    prompt = f"""{instruction}
    Context: '{context}'
    Question: '{question}'
    Answer: '{answer}'.
    Provide only three distractors as a comma-separated list. Do not include explanations, commentary, or additional text."""
    
    inputs = tokenizer(prompt, return_tensors="pt", padding=True, truncation=True, max_length=max_seq_length).to("cuda")
    
    generation_config = GenerationConfig(
        max_new_tokens=128,
        use_cache=True,
        temperature=0.7,
    )
    
    outputs = model.generate(**inputs, generation_config=generation_config)
    distractors_text = tokenizer.batch_decode(outputs, skip_special_tokens=True)[0][len(prompt):].strip()
    distractors = [d.strip() for d in distractors_text.split(",") if d.strip()]
    
    return ", ".join(distractors[:3])

# Example usage
context = "In physics, the concept of jerk is used to describe the rate of change of acceleration."
question = "What is the physical quantity that jerk is the rate of change of?"
answer = "Acceleration"
instruction = "Generate plausible but incorrect answer choices."

distractors = generate_distractors(context, question, answer, instruction, model, tokenizer)
print("Distractors:", distractors)


# Output: velocity, momentum, force

Dataset Used for Training

[More Information Needed]

Training Details

Hardware Used: T4 GPU (Google Colab)
Fine-tuned from: LLaMA 3.2 1B
Training Framework: transformers + SFTTrainer

Evaluation Metrics

The model was evaluated using BLEU and ROUGE scores to compare generated distractors with reference distractors.

Citation

If you use this model, please cite:

@article{llama3.2_distractor_gen,
  title={Distractor Generation using LLaMA 3.2},
  author={Birendra Sharma},
  year={2025},
  publisher={Hugging Face Model Hub}
}

For more details, visit the Hugging Face model page.

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Safetensors

Model size

1B params

Tensor type

F16