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Model: BirendraSharma/llama3.2_1B_distractors_generation
Source: Original Platform

README.md

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+---
+library_name: transformers
+tags:
+  - text-generation
+  - llama
+  - mcq-generation
+  - distractor-generation
+  - safetensors
+---
+
+# 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](https://huggingface.co/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:
+```bash
+pip install transformers torch
+```
+
+### Loading the Model & Tokenizer
+```python
+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](https://huggingface.co/BirendraSharma/llama3.2_1B_distractors_generation).
+